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\begin{document}

\thispagestyle{empty}

\title{Pseudoknot: a Float-Intensive Benchmark \\ for Functional Compilers (DRAFT)}

\author{
Pieter H. Hartel \thanks{Dept.\ of Computer Systems, Univ.\ of
Amsterdam, Kruislaan 403, 1098 SJ Amsterdam, The Netherlands, e-mail:
pieter@fwi.uva.nl} \and
Marc Feeley \thanks{D\'epart.\ d'informatique et r.o., univ.\ de
Montr\'eal, succursale centre-ville, Montr\'eal H3C 3J7, Canada,
e-mail: feeley@iro.umontreal.ca} \and
Martin Alt \thanks{Informatik, Universit\"at des Saarlandes,
66041 Saarbr\"cken 11, Germany, e-mail: alt@cs.uni-sb.de} \and
Lennart Augustsson \thanks{Dept.\ of Computer Systems, Chalmers Univ.\ of
Technology, 412 96 G\"oteborg, Sweden, e-mail: augustss@cs.chalmers.se} \and
Peter Baumann \thanks{Dept.\ of Computer Science, Univ.\ of Zurich,
Winterthurerstr.\ 190, 8057 Zurich, Switzerland, e-mail:
baumann@ifi.unizh.ch} \and
Marcel Beemster \thanks{Dept.\ of Computer Systems, Univ.\ of
Amsterdam, Kruislaan 403, 1098 SJ Amsterdam, The Netherlands, e-mail:
beemster@fwi.uva.nl} \and
Emmanuel Chailloux \thanks{LIENS, URA 1327 du CNRS, \'Ecole Normale
Sup\'erieure, 45 rue d'Ulm, 75230 PARIS C\'edex 05, France, e-mail:
Emmanuel.Chailloux@ens.fr} \and
Christine H. Flood \thanks{Laboratory for Computer Science, MIT, 545
Technology Square, Cambridge Massachusetts 02139, USA, e-mail:
chf@lcs.mit.edu} \and
Wolfgang Grieskamp \thanks{Berlin University of Technology,
Franklinstr. 28-29, 10587 Berlin, Germany, e-mail: wg@cs.tu-berlin.de} \and
John H. G.\ van Groningen \thanks{Faculty of Mathematics and Computer
Science, Univ.\ of Nijmegen, Toernooiveld 1, 6525 ED Nijmegen, The
Netherlands, e-mail: johnvg@cs.kun.nl} \and
Kevin Hammond \thanks{Dept.\ of Computing Science, Glasgow University,
17 Lilybank Gardens, Glasgow, G12 8QQ, UK, e-mail: kh@dcs.glasgow.ac.uk} \and
Bogumi\l~Hausman \thanks{Computer Science Lab, Ellemtel Telecom Systems
Labs, Box 1505, S-125 25 \"Alvsj\"o, Sweden, e-mail:
bogdan@erix.ericsson.se} \and
Melody Y. Ivory \thanks{Computer Research Group, Institute for
Scientific Computer Research, Lawrence Livermore National Laboratory,
P. O. Box 808 L-419, Livermore, CA 94550, e-mail: ivory1@llnl.gov} \and
Peter Lee\thanks{Dept.\ of Computer Science, Carnegie Mellon University,
5000 Forbes Avenue Pittsburgh, Pennsylvania 15213, USA, e-mail:
petel@cs.cmu.edu} \and
Xavier Leroy \thanks{INRIA Rocquencourt, projet Cristal,
B.P. 105, 78153 Le Chesnay, France. e-mail: Xavier.Leroy@inria.fr} \and
Sandra Loosemore \thanks{Dept.\ of Computer Science, Yale Univ., New haven,
Connecticut, e-mail: loosemore-sandra@cs.yale.edu} \and
Niklas R\"ojemo \thanks{Dept.\ of Computer Systems, Chalmers Univ.\ of
Technology, 412 96 G\"oteborg, Sweden, e-mail: rojemo@cs.chalmers.se} \and
Manuel Serrano \thanks{INRIA Rocquencourt, projet Icsla,
B.P. 105, 78153 Le Chesnay, France. e-mail: Manuel.Serrano@inria.fr} \and
Jean-Pierre Talpin \thanks{European Computer-Industry Research Centre, 
Arabella Strasse 17, D-81925 Munich, Germany. e-mail: jp@ecrc.de} \and
Jon Thackray \thanks{Harlequin Ltd, Barrington Hall, Barrington,
Cambridge CB2 5RG, England, e-mail: jont@harlequin.co.uk} \and
Pierre Weis \thanks{INRIA Rocquencourt, projet Cristal,
B.P. 105, 78153 Le Chesnay, France. e-mail: Pierre.Weis@inria.fr} \and
Peter Wentworth \thanks{Dept.\ of Computer Science, Rhodes Univ.,
Grahamstown 6140, South Africa, e-mail: cspw@cs.ru.ac.za}
}

\maketitle
\sloppy

\begin{abstract}
Over 20 implementations of different functional languages are
benchmarked using the same program, a floating-point intensive
application taken from molecular biology.  The principal aspects
studied are compile time and execution time for the varying
implementations.  An important consideration is how the program can be
modified and tuned to obtain maximal performance on each language
implementation.

With few exceptions, the compilers take a significant amount of time
to compile this program, though almost all compilers were faster than
the current GNU C compiler.  Compilers that generate C or Lisp are
often slower than those that generate native code directly; the cost
of compiling the intermediate form is normally a large fraction of the
total compilation time.

There is no clear distinction between the runtime performance of eager
and lazy implementations when appropriate annotations are used: lazy
implementations have clearly come of age when it comes to implementing
largely strict applications, such as the pseudoknot program. The speed
of C can be approached by some implementations, and is even exceeded
by Sisal on the Cray, but in order to achieve this performance,
special measures such as strictness annotations are required by
non-strict implementations.
\end{abstract}


\section{Introduction}
At the Dagstuhl Workshop on Applications of Functional Programming in
the Real World in May 1994~\cite{Gie94}, several interesting applications of
functional languages were presented. One of these applications, the
pseudoknot problem~\cite{Fee94} had been written in several languages,
including C, Scheme~\cite{Ree91}, Multilisp~\cite{Hal85} and
Miranda\footnote{Miranda is a trademark of Research Software Ltd.}~\cite{Tur85}.
A number
of workshop participants decided to test their compiler technology
using this particular program. One point of comparison is the speed of
compilation and the speed of the compiled program. Another important
point is how the program can be modified and tuned to obtain maximal
performance on each language implementation available. It is also
interesting to compare the performance of typed and untyped languages.
Finally, an interesting question is whether laziness is or is not
beneficial for this application.

The initial benchmarking efforts revealed important differences between
the various compilers. The first impression was that compilation speed
should generally be improved. After the workshop we have continued to
work on improving both the compilation and execution speed of the
pseudoknot program. Some researchers not present at Dagstuhl joined the
team and we present the results as a record of a small scale, but
exciting collaboration with contributions from many parts of the world.

As is the case with any benchmarking work, our results should be taken
with a grain of salt. We are using a realistic program that performs a
useful computation, however it stresses particular language features
that are probably not representative of the applications for which the
language implementations were intended. Implementations invariably
trade-off the performance of some programming feature for others in the
quest for the right blend of usability, flexibility, and performance on
``typical'' applications. It is clear that a single benchmark is not a
good way to measure the overall quality of an implementation.
Moreover, the performance of an implementation usually (but not always)
improves with new releases as the implementors repair bugs, add new
features, and modify the compiler. We feel that our choice of
benchmark can be justified by the fact that it is a real world
application, that it had already been translated into C and several
functional languages, and that we wanted to compare a wide range of
languages and implementations. The main results agree with those found
in earlier studies~\cite{Can92,Har92b}.

Section~\ref{sec:languages} briefly characterises the functional
languages that have been used. The pseudoknot application is introduced
in Section~\ref{sec:application}. The compilers and interpreters for
the functional languages are presented in Section~\ref{sec:compilers}.
Section~\ref{sec:translation} describes the translations of the program
from one language into the other. The results and conclusions are given
in the last two sections.

\begin{table}
\begin{minipage}{\hsize}
\begin{center}
\begin{tabular}{|l|l|l|l|l|l|l|}
\hline
language     & source    & ref.        &    typing     &evaluation & order&match \\
\hline
Caml         & INRIA     &\cite{Wei93} & strong, poly  &  eager    &higher&pattern \\
Clean        & Nijmegen  &\cite{Pla94} & strong, poly  &   lazy    &higher&pattern \\
Common Lisp  & Committee &\cite{Ste90} & dynamic       &  eager    &higher& access \\
Erlang       & Ericsson  &\cite{Arm93} & dynamic       &  eager    & first&pattern \\
Facile       & ECRC      &\cite{Tho93a}& strong, poly  &  eager    &higher&pattern \\
Gofer        & Yale      &\cite{Jon94c}& strong, poly  &   lazy    &higher&pattern \\
Haskell      & Committee &\cite{Hud92a}& strong, poly  &   lazy    &higher&pattern \\
ID           & MIT       &\cite{Nik90a}& strong, poly  &   eager \footnote{eager, non-strict}
                                                                   &higher&pattern \\
Miranda      & Kent      &\cite{Tur85} & strong, poly  &   lazy    &higher&pattern \\
Opal         & TU Berlin &\cite{Did94} & strong, param &  eager    &higher&pattern \\
RUFL         & Rhodes    &\cite{Wen92} & strong, poly  &   lazy    &higher&pattern \\
Scheme       & Committee &\cite{Ree91} & dynamic       &  eager    &higher& access \\
Sisal        & LLNL      &\cite{McG85} & strong, mono  &  eager    &first &   none \\
Standard ML  & Committee &\cite{Mil90} & strong, poly  &  eager    &higher&pattern \\
Stoffel      & Amsterdam &\cite{Bee92a}& strong, poly  &   lazy    &higher&   case \\
Trafola & Saarbr\"ucken  &\cite{Alt93} & strong, poly  &  eager    &higher&pattern \\
\hline
ANSI C       & Committee &\cite{Ker88} &   weak        &  eager    & first&   none \\
\hline
\end{tabular}
\end{center}
\end{minipage}
\caption{Language characteristics. For each language its source is given, as
well as a key reference to the language definition. The rest of the
table presents important language characteristics.}
\label{tbl:language}
\end{table}


\section{Languages}
\label{sec:languages}
Details of the languages which were benchmarked may be found in
Table~\ref{tbl:language}. C has been used as a reference language in
order to allow comparison with imperative languages.  The first column
of the table gives the name of the language. The second column gives
the source (i.e.\ a University or a Company) if a language has been
developed in one particular place. Some languages were designed by a
committee, which is also shown. The third column of the table gives a
key reference to the language definition.

The last four columns describe some important properties of the
languages. The typing discipline may be {\it strong} (and static),
{\it dynamic}, or {\it weak}; a strong typing discipline may be
monomorphic ({\it mono}), polymorphic ({\it poly}), or parametrically
polymorphic ({\it param}).  The evaluation strategy may be {\it
eager}, {\it lazy} or {\it eager} with {\it non-strict}
evaluation~\cite{Nik90a}. The language may be {\it first order} or
{\it higher order}.  Accessing components of data structures may be
supported by either pattern-matching on function arguments, local
definitions and/or as part of case expressions ({\em pattern},
{\em case}), by compiler generated access functions to destructure data
({\em access}), or not at all ({\em none}). The reader should consult
the references provided for further details of individual languages.


\section{Application}
\label{sec:application}
The pseudoknot program is derived from a ``real-world'' molecular
biology application that computes the three-dimensional structure of
part of a nucleic acid molecule. The program exhaustively searches a
discrete space of shapes and returns the set of shapes that respect
some structural constraint.

\begin{table}
\begin{center}
\begin{tabular}{|l|r|l|r|}
\hline
\multicolumn{2}{|c}{floating point operations} &
\multicolumn{2}{|c|}{square root and trigonometric functions} \\
\hline
$\times$         & 3,567,672 &                &         \\
$+$              & 2,798,571 & $\sqrt{\;\;}$  &  69,600 \\
$>$, $\leq$, $<$ &   129,656 & $\arctan$      &  40,184 \\
$-$              &   330,058 & $\cos$         &  40,184 \\
$/$              &    40,184 & $\sin$         &  40,184 \\
\hline
total            & 6,866,141 & total          & 190,152 \\
\hline
\end{tabular}
\end{center}
\caption{Breakdown of the ``real'' work involved in the pseudoknot
problem as counted by the FAST system. The floating point operations
occurring in the trigonometric functions and the square root are {\em
not} counted separately.}
\label{tbl:float}
\end{table}

The program is heavily floating point oriented. For example the C
version of the program spends 25\% of its time in the C library
trigonometric and square root routines.

Statistics from the lazy FAST~\cite{Har91} compiler show that with lazy
evaluation the fastest version of the program does about 7~M floating
point operations, which excludes those performed by the 190~K
trigonometric and square root functions. A detailed breakdown of these
statistics is shown in Table~\ref{tbl:float}. The program makes about
1.5~M function calls and claims about 15~Mbytes of space (The maximum
heap size required is about 30~Kbytes).
Statistics from the eager MLWorks system show that with eager
evaluation the program spends about 50\% of its time performing
floating point operations.
All this suggests that the application can be characterised as
somewhere in between ``numeric'' and ``symbolic''.

The pseudoknot program does not explicitly depend on laziness for its
correct behaviour. The program is thus ideal for comparing lazy and
non-lazy implementations of functional languages. It is relatively easy
to translate the program from a lazy to a strict functional
language. Inserting strictness annotations in a lazy version of the
program is also relatively easy.

The program uses mostly algebraic data types as data structures. Some
of these are rather large. Having to use pattern-matching style
destructuring on data structures with as many components as 12 or even
30 leads to distinctly unreadable code.

With the obvious exception of the C version, all other versions of the
program are purely functional.

The original program is described in detail by Feeley, et.\ al~\cite{Fee94}. The
program used in the present benchmarking effort differs in two ways
from the original.

Firstly, the original program only computed the number of solutions
found during the search. However, it is the location of each atom in
the solutions that are of interest to a biologist because these
solutions typically need to be screened manually by visualizing them
one after another. The program was thus modified to compute the
location of each atom in the structures that are found. In order to
minimize I/O overhead, a single value is printed: the distance from
the origin to the farthest atom in any solution (this requires that the
position of each atom be computed).

Secondly, the original program did not attempt to exploit laziness in
any way. However, there is an opportunity for laziness in the
computation of the absolute position of atoms. To compute the absolute
position of an atom, it is necessary to transform a 3D vector from one
coordinate system to another by multiplying a 3D vector by a $3 \times
4$ transformation matrix. The reason for this is that the position of
an atom is expressed relatively to the nucleotide it is in. Thus the
position of an atom is specified by two values: the 3D position of the
atom in the nucleotide, and the absolute position and orientation of
the nucleotide in space. However, the position of atoms in structures
that are pruned by the search process are not needed (unless they are
needed to test a constraint). There are thus two formulations of the
pseudoknot program which differ in the way the position computation of
atoms is expressed:
\begin{description}
\item[Late formulation]
The absolute position of an atom is computed each time it is needed;
either for testing a constraint or for computing the distance to
origin if it is in a solution. This formulation may duplicate
work. Because the placement of a nucleotide is shared by all the
structures generated in the subtree that stems from that point in the
search, there can be as many recomputations of a position as there are
nodes in the search tree (which can number in the thousands to
millions).
\item[Early formulation]
The absolute position of an atom is computed as soon as the nucleotide
it is in is placed. If this computation is done lazily, this
formulation avoids the duplication of work because the coordinate
transformation for an atom is at most done once.
\end{description}
The original program used the late formulation. To explore the
benefits of laziness, the program was modified so that it is easy to
obtain the alternative early formulation (only a few lines need to be
commented and uncommented to switch from one formulation to the
other).

The C, Scheme, and Miranda versions of the program support the early
formulation. The Miranda version implicitly uses lazy evaluation. The
Scheme version uses the explicitly lazy {\em delay} and {\em force}
constructs.  The C version requires lazy computation to be explicitly
programmed.

\begin{table}
\begin{center}
\begin{tabular}{|l|l|l|l|l|}
\hline
language     &version&source                & ref.        & FTP / Email \\
\hline
Bigloo       & 1.7   &INRIA Rocquencourt    &\cite{Ser94a}& Ftp: {\small ftp.inria.fr:} \\
             &       &                      &             & {\small /INRIA/Projects/icsla/Implementations/} \\
Caml Light   & 0.61  &INRIA Rocquencourt    &\cite{Ler93} & Ftp: {\small ftp.inria.fr:} \\
             &       &                      &             & {\small /lang/caml-light/} \\
Caml Gallium &       &INRIA Rocquencourt    &\cite{Ler92} & Email: {\small Xavier.Leroy@inria.fr} \\
Camloo       & 0.2   &INRIA Rocquencourt    &\cite{Ser94b}& Ftp: {\small ftp.inria.fr:} \\
             &       &                      &             & {\small /lang/caml-light/} \\
CeML         & 0.22  & LIENS                &\cite{Cha92a}& Email: {\small Emmanuel.Chailloux@ens.fr} \\
Clean        & 1.0b  &Nijmegen              &\cite{Sme91} & Ftp: {\small ftp.cs.kun.nl:} \\
             &       &                      &             & {\small /pub/Clean/} \\
CMU CL       & 17e   &Carnegie Mellon       &\cite{Mac92} & Ftp: {\small lisp-sun1.slisp.cs.cmu.edu:} \\
             &       &                      &             & {\small /pub/} \\
Erlang       & 6.0.4 &Ellemtel AB           &\cite{Hau94} & commercial \\
             &       &                      &             & Email: {\small erlang@erix.ericsson.se} \\
Facile       & Antigua & ECRC               &\cite{Tho93a}& Email: {\small facile@ecrc.de} \\
FAST         & 33    &Southampton/          &\cite{Har91} & Email: {\small pieter@fwi.uva.nl} \\
             &       &Amsterdam             &             & \\
Gofer        & 2.30a &Yale                  &\cite{Jon94c}& Ftp: {\small nebula.cs.yale.edu:} \\
             &       &                      &             & {\small /pub/haskell/gofer/} \\
Haskell      & 999.6 &Chalmers              &\cite{Aug93c}& Ftp: {\small ftp.cs.chalmers.se:} \\
             &       &                      &             & {\small /pub/haskell/chalmers/} \\
Haskell      & 0.22  &Glasgow               &\cite{Pey93c}& Ftp: {\small ftp.dcs.glasgow.ac.uk:} \\
             &       &                      &             & {\small /pub/haskell/glasgow/} \\
Haskell      & 2.1   &Yale                  &\cite{Yal94} & Ftp: {\small nebula.cs.yale.edu:} \\
             &       &                      &             & {\small /pub/haskell/yale/} \\
ID           &TL0 2.1&MIT/Berkeley          &\cite{Nik90a}& Email: {\small chf@lcs.mit.edu} \\
MLWorks      & n.a.  &Harlequin Ltd.        &\cite{Har94x}& commercial \\
Miranda      & 2.018 &Research Software Ltd.&\cite{Tur90a}& commercial \\
             &       &                      &             & Email: {\small mira-request@ukc.ac.uk} \\
Nearly Haskell & Pre rel. &Chalmers         &\cite{Roj94} & Ftp: {\small ftp.cs.chalmers.se:} \\
             &       &                      &             & {\small /pub/haskell/nhc/} \\
Opal         & 2.1c  &Berlin                &\cite{Sch91} & Ftp: {\small ftp.cs.tu-berlin.de:} \\
             &       &                      &             & {\small /pub/local/uebb/ocs} \\
RUFL         & 1.8.4 &Rhodes                &\cite{Wen91} & Ftp: {\small cs.ru.ac.za:} \\
             &       &                      &             & {\small /pub/rufl/} \\
Gambit Scheme&  2.2  &Montr\'eal            &\cite{Fee90} & Ftp: {\small ftp.iro.umontreal.ca:} \\
             &       &                      &             & {\small /pub/parallele/gambit/} \\
Sisal        &12.9.2 &LLNL                  &\cite{Can92c}& Ftp: {\small sisal.llnl.gov} \\
             &       &                      &             & {\small /pub/sisal} \\
SML/NJ       & 1.03z &AT\&T Bell Labs.      &\cite{App92} & Ftp: {\small research.att.com:} \\
             &       &                      &             & {\small /dist/ml/} \\
Poly/ML      & 2.05M &Abstract Hardware Ltd.&\cite{Fin92a}& commercial \\
Stoffel      &       & Amsterdam            &\cite{Bee93} & Email: {\small beemster@fwi.uva.nl} \\
Trafola      & 1.2   & Saarbr\"ucken        &\cite{Alt93} & Email: {\small alt@cs.uni-sb.de} \\
\hline
\end{tabular}
\end{center}
\caption{Compiler details consisting of the name of the compiler and/or language, the
University or Company that built the compiler, one key reference to the
description of the implementation and the address from which the
compiler can be obtained.}
\label{tbl:compiler}
\end{table}


\section{Compilers}
\label{sec:compilers}
An overview of the compilers which were studied may be found in
Table~\ref{tbl:compiler}.  The first column gives the name of the
language and/or compiler, the second shows the source of the compiler. A key reference
that describes the compiler is given in the third column. The last
column gives instructions for obtaining the compiler by FTP or email.

\begin{table}
\begin{center}
\begin{tabular}{|l|l*{3}{|r@{\,}r@{\,}r}|}
\hline
C compiler    & version     & \mmm{compilation}  & \mmm{execution}  & \mmm{compilation} \\
              &             & \mmm{complete}     & \mmm{complete}   & \mmm{stripped} \\
\hline
cc --O        & SunOS 4.1.2 & 325\dzz &+& 26\dzz & 3.35 &+& 0.23    & 12.9 &+& 2.4 \\
gcc --O       & 2.5.8       & 910\dzz &+& 96\dzz & 2.70 &+& 0.19    & 13.4 &+& 2.3 \\
\hline
ratio         &             &   0.35  &+&  0.26  & 1.23 &+& 1.15    & \mmm{$\approx 1+1$} \\
\hline
\end{tabular}
\end{center}
\caption{The compilation and execution times (user+system seconds) of
the C version of the pseudoknot program with the two most commonly
used C compilers.}
\label{tbl:c_gcc}
\end{table}

Most of the experiments were performed on Sun SPARC workstations. The
basis of the experiments is formed by the performance of the C
compiler. We have used either Sun's bundled C compiler cc or the GNU C
compiler gcc.  The gcc compiler generates slightly faster code for the
pseudoknot program, but it takes about three times as long to compile.
Table~\ref{tbl:c_gcc} gives the detailed results as measured on
machine~\sysfast~(c.f. Table~\ref{tbl:machine}) using gcc version
2.5.8. These results indicate that the C version of the pseudoknot
program is difficult to compile, even for a C compiler. This is due to
the presence of four large routines (consisting of 574, 552, 585 and
541 lines of code, respectively) which together build the conformation
data base of the nucleotides. Stripping the body of these four
functions to be empty (which still leaves a program of 1073 lines of C
code) reduces the compilation time to about 13 seconds, for cc as well
as gcc. All the functional versions of the program have the same
structure as the C version, so the functional compilers are faced with
the same difficulty of compiling the code that builds the conformation
data base.

An interesting difference between cc and gcc is that over 75\% of the
compilation time for cc is spent in the assembler, whereas gcc spends
99\% of its time in the main compiler pass (\verb=cc1=).

Table~\ref{tbl:option} shows some properties of the compilers and
experiments that have an effect on the runtime performance of the
implementations. In addition to the compile and runtime options
(columns~2 and~3), the fourth column shows the type of garbage
collector used, and the fifth column shows what floating point
precision is used.
The floating point precision is important to take into account when
comparing results for two reasons. Firstly, using single precision
floating point numbers makes it easier to generate fast code. Secondly,
some architectures implement single precision floating point arithmetic
significantly faster than double precision (DEC Alpha). However, on the
SPARC that has been used for the majority of the experiments reported
here there is only a small difference between the speed of single and
double precision arithmetic.

\begin{table}
\begin{minipage}{\hsize}
\begin{center}
\begin{tabular}{|l|l|l|l|l|}
\hline
compiler     & compiler options& execution options           & collector& float\\
\hline
Bigloo       & --unsafe --O4   &                             & mark-scan& double \\
Caml Light   &                 &                             & gen.     & double \\
Caml Gallium &                 &                             & gen.     & double \\
Camloo       & --unsafe --O4   &                             & mark-scan& double \\
CeML         & --O             &                             & 1-space  & single \\
Chalmers     & --c --Y--S --H50Mg --Y--A500k --cpp &         & 2-space  & single \\
Clean        &                 & --nt --s 10k --h ...        & 2-sp/ms  & double \\
CMU          & see text        & see text                    & 2-space  & single \\
Erlang BEAM  & --fast          & --h 600000                  & 2-space  & double \\
Facile       &                 &                             & gen.     & double \\
FAST         & --fcg           & --v 1 --h ... --s 400K 1    & 2-space  & single \\
Gambit       & --h14336        & --h14336                    & 2-space  & double \\
Glasgow      & --O --fvia--C --O2--for--C & +RTS --H1M&gen.~\cite{San93}& single \\
Gofer        & --DTIMER        &                             & 2-space  & single \\
ID           & strict, merge--partitions (tlc: opt) &        & none     & double \\
MLWorks      &
no details available\footnote{MLWorks is not yet available. Compilation
was for maximum optimisation, no debugging or statistics collection
was taking place at runtime.}  &                             & 2-space  & double \\
Miranda      &                 & /heap ...; /count           & mark-scan& double \\
NHC(HBC)     & --H30M          &                             & 2-space  & single \\
NHC(NHC)     & --h2M           &                             & 1-space  & single \\
Opal         & opt=full debug=no  &                          & refcount & single \\
Poly/ML      & --h 48000       & --h 48000                   & gen.     & single \\
RUFL         & --w             & --m300                      & mark-scan& double \\
RUFLI        & --iw            & --m300 --r32000             & mark-scan& double \\
Sisal        & --cpp --seq --O --c atan2 --cc=--O &          & refcount & double \\
SML/NJ       &                 &                             & gen.     & double \\
Stoffel      & --O2 (for C)    &                             & 2-space  & double \\
Trafola      & --TC --INLINE 1 & --HE 8000000                & 1-space  & sgl/dbl\\
Yale         & see text        & see text                    & 2-space  & single \\
\hline
\end{tabular}
\end{center}
\end{minipage}
\caption{Compiler and execution options. The type of garbage collector
is one of 2-space (non-generational 2-space copying collector); mark-scan; gen.
(generational with two or more spaces); 1-space (mark-scan, one space
compactor); or reference counting. Floating point arithmetic used is
either single or double precision.}
\label{tbl:option}
\end{table}

\section{Translation, annotations and optimisations}
\label{sec:translation}
The pseudoknot program was translated by hand from the Scheme version
to the various other languages. All versions were hand tuned to
exhibit the best performance available with the compiler being used.
The following set of guide lines has been used to make the comparison
as fair as possible:
\begin{enumerate}
\item
Algorithmic changes are forbidden but slight modifications to the code
to better use a particular feature of the language or programming
system are allowed. A modification that would be valid for all
languages involved is not permitted.
\item
Only small changes to the data structures are permitted (e.g.\ a tuple
may be turned into an array or a list).
\item
Annotations are permitted, for example strictness annotations, or
annotations for inlining and specialisation of code.
\item
All changes and annotations should be documented.
\item
Anyone should be able to repeat the experiments. So all sources and
measurement procedures should be made public (by \verb=ftp= somewhere).
\item
All programs must produce the same output (the number 33.7976 to 6
significant figures).
\end{enumerate}

The optimisations and annotations made to obtain best performance with
each of the compilers are discussed in the subsections to follow. Often
we will make reference to particular parts of the program text. As the
program is relatively large it would be difficult to reproduce it in
full here, and in many versions. The reader is invited to consult the
archive that contains most of the versions of the program at
\verb=ftp.fwi.uva.nl=, file \verb=/pub/functional/pseudoknot.tar.Z=.

\subsection{Bigloo}
The Scheme version of the pseudoknot benchmark was changed only
slightly to serve as input to the Bigloo optimising Scheme compiler.
This compiler translates to C, as do many other compilers. In the C
code it produces, the many vectors of floating point constants present
in the conformation data base are not represented as ordinary C
constants but as a large string. This enables the C compiler to compile
the generated C much more quickly. During initialisation at runtime the
string representation is transformed into the required numeric data.

\subsection{Caml}
Three compilers for the Caml dialect of ML have been benchmarked: Caml
Light, a simple bytecode compiler; Camloo, a Caml to C compiler derived
from the Bigloo optimizing Scheme compiler; and Gallium, an
experimental native-code compiler.

The source code is a straightforward translation to Caml of the SML
version of the benchmark. The only changes made to increase performance
was the use of an array instead of a list in the functions
\verb|list_of_atoms| and \verb|most_distant_atoms|. For the Gallium
compiler only, some tuples have been converted into records to guide
the data representation heuristics; this transformation makes no
difference for the other two compilers.

The Caml Gallium compiler uses unboxed double precision float in
monomorphic parts of the source code. Since the pseudoknot benchmark does
not use polymorphism, all floats are unboxed at all times. This is the
main reason why the Gallium compiler generates faster code than most
of the other compilers.

\subsection{CeML}
CeML is another dialect of Caml. The purpose of the development of CeML
and its compiler is to prove that ML can be compiled into efficient C.
CeML does not tag immediate values and uses a garbage collector with
ambiguous roots which manages its own stack. This is important for the
pseudoknot program because the floats are represented as a full word
(32 bits).

The CeML source of the pseudoknot program differs only in one aspect
from the Camloo source. To avoid rebuilding the tuple \verb=(i,t,n)=
the definition of \verb=get_var= was changed from:
\begin{verbatim}
> fun get_var id ((i,t,n)::lst) = if id = i then (i,t,n)
>                                 else get_var id lst
\end{verbatim}
to:
\begin{verbatim}
> fun get_var id (((i,_,_) as v)::lst) = if id = i then v
>                                        else get_var id lst
\end{verbatim}

The total compilation time consist mainly of the gcc optimized
compilation (--O2) time, particularly to build the data base. If the
program is sliced into different modules and the module corresponding
to the data base is not compiled with the --O2 option, the total
compilation time is about ten minutes and the execution time does not
change.

\subsection{CLEAN}
The syntax of the Clean 1.0 programming language is similar to the
syntax of Haskell and Miranda. Porting the Miranda program to Clean 1.0
was easy. The guards were changed to Haskell syntax, the first
character of each type name was changed to upper case, and the names of
a few functions were changed. Some patterns on the right hand side of
function definitions were moved to the left hand side or replaced by
case expressions. This was necessary, because the current compiler
cannot yet handle all patterns on the right hand side, only tuple and
record patterns are currently allowed there. For example:
\begin{verbatim}
> anticodon_constraint v partial_inst
>  | i==33 = ...
>  where Var i t n = v
\end{verbatim}
was replaced by:
\begin{verbatim}
> anticodon_constraint v=:(Var i t n) partial_inst
>  | i==33 = ...
>  where
\end{verbatim}

To achieve good performance the following five changes have been
made. The basic floating point operations and some constants were
inlined. The components of the main algebraic data types \verb=Pt= and
\verb=Tfo= and the integer component of \verb=Var= were annotated as
strict. The function \verb=absolute_pos= was inlined in the function
\verb=atom_pos=. If a node on the right hand side of a definition
occurred in the pattern, the code was changed to prevent rebuilding the
node, for example:
\begin{verbatim}
> atom_pos atom (Var i t n) = absolute_pos (Var i t n) (atom n)
\end{verbatim}
was changed to:
\begin{verbatim}
> atom_pos atom v=:(Var i t n) = absolute_pos v (atom n)
\end{verbatim}
Finally, in the function \verb=pseudoknot_domains= the values of
\verb=rA=, \verb=rC=, \verb=rG=, \verb=rU= and \verb=rCs= were assigned
to local functions as: \verb|ra=rA| etc. The local functions were used
throughout the body of \verb=pseudoknot_domains=. This is necessary
because the current compiler does not yet support constant applicative
forms (CAFs)~\cite{Pey87}.

\subsection{Chalmers Haskell}
\label{sec:hbc}
The Haskell version of the pseudoknot program is a straightforward
translation of the Miranda version. The latter often destructs and
reconstructs a data item. In the Haskell version care has been taken to
introduce ``as'' patterns thus:
\begin{verbatim}
> atom_pos atom v@(Var i t n) = absolute_pos v (atom n)
\end{verbatim}

It proved necessary to split the program into a number of modules, as
the program as a whole could not be compiled (see
Section~\ref{sec:NHC}).  Splitting the program into modules was
straightforward since each of the four functions that initialise the
conformation data base can be separated into its own large module of
about 600 lines.  The global data structure definitions are placed in
their own module which is imported by each worker module, and the main
program imports all of these modules.

The Chalmers Haskell compiler (HBC) provides a compiler option (--DSTR)
to annotate all data structures as strict. The late formulation of the
pseudoknot program runs about twice as fast with this option
selected.

\subsection{Erlang}
Erlang is a concurrent functional programming language designed for
prototyping and implementing reliable real-time systems~\cite{Arm93}.
The Erlang BEAM compiler~\cite{Hau94} is an efficient and portable
implementation of Erlang where Erlang programs are compiled into C.
However, Erlang is a concurrent language and has explicit
syntax for referring to time (time-outs). The implementation therefore
has to provide a scheduling mechanism and a notion of time which
introduces constant overhead to the Erlang BEAM execution.

Porting the benchmark from Miranda to Erlang required some minor changes.
Since Erlang is not a higher order language some function calls were
done by applying \verb=p_apply/N=, for example \verb=reference/3= is
called as:
\begin{verbatim}
> p_apply(reference,Arg1,Arg2,Arg3) -> reference(Arg1,Arg2,Arg3).
\end{verbatim}
when all arguments are known.
Another problem is typing. Some limited typing can be done in Erlang
using guard tests. For example, the following Erlang function types its
arguments as floats:
\begin{verbatim}
> pt_sub({X1,Y1,Z1},{X2,Y2,Z2})
>       when float(X1),float(Y1),float(Z1),
>            float(X2),float(Y2),float(Z2) ->
>       {X1-X2,Y1-Y2,Z1-Z2}.
\end{verbatim}
The type information obtained from guards and extended by compile-time
type analysis is used by the Erlang BEAM compiler to optimize
different arithmetic operations.

Since Erlang BEAM compiles Erlang programs into C, the Erlang BEAM
compilation time presented in Table~\ref{tbl:compilation} includes the
costs both of compiling Erlang to C and of calling gcc (version 2.5.8)
to compile the generated C program. The total compilation time consists
mainly of the gcc compilation time (4751 + 20 seconds). The gcc
compiler was run with the -O2 option which increases compilation time
but a better performance of the generated code was considered more
important than the compilation time.

The best execution time was achieved by extending the available heap
space to minimize the garbage collection time, and by inlining floating
point operations after compile-time type analysis.

Erlang BEAM provides double precision floating point arithmetic. All
floating point numbers are stored as boxed objects in the heap.
Integers are 32 bits objects, which may be stored directly on stack.
The relatively poor performance of the Erlang BEAM compiler (reported
in Table~\ref{tbl:execution}) is due to the fact that the pseudoknot
benchmark is float-intensive and thus tests mainly the implementation
of floating point arithmetic. As shown later in Section~\ref{sec:OPAL},
having single precision floating point arithmetic and using an unboxed
representation of floating point data may improve the execution time by
a factor of 4. In the Erlang BEAM compiler giving up the double
precision floating point arithmetic would similarly improve both access
and garbage collection time by storing single-float objects directly on
stack instead of heap.

To support the above claim we did the following simple experiment. We
ran the two functions \verb=tfo_apply/2= and \verb=tfo_combine/2=,
which are very floating-point intensive, using first integers and then
(double precision) floats as input data. The experiment was carried out
on machine~\sysfloat from Table~\ref{tbl:machine}. The results showed
that using integers instead of floats improved the Erlang BEAM
execution time of those two functions by a factor of 6. To be sure that
the improvement comes from better data representation and not from
better integer arithmetic provided by the hardware, a simple C program
was written to compare the speed of floating-point and integer
arithmetic. The results showed that the floating-point arithmetic was
as efficient as the integer arithmetic.

Since single precision floats can be represented in Erlang BEAM in the
same way as integers we can expect that giving up the double precision
floating point arithmetic would improve the pseudoknot execution time
to a large extent.

\subsection{Facile}
Facile is a high-level, higher-order programming language for systems
that require a combination of complex data manipulation and concurrent
and distributed computing. It combines Standard ML (SML), with a model
of higher-order concurrent processes based on CCS. Facile is
well-suited for running on loosely connected, physically distributed
systems with distributed memory. It is possible to execute Facile
programs on both local area networks (LANs) and wide area networks
(WANs).

The basic computational model of Facile consists of one or more nodes
or virtual processors, on each of which there are zero or more
processes (light weight threads). Processes execute by evaluating
expressions, and they can communicate values between each other by
synchronising over typed channels. All types of values, including user
defined types, channels, functions and process scripts, are
transmissible.

% Facile enriches Standard ML with primitives for distribution,
% concurrency and communication over typed channels. The additional data
% types provided in the language include node identifiers, process
% scripts and communication channels. All of these are first-class values
% that can be manipulated in an applicative style and, in particular, be
% communicated. New nodes and channels can be created dynamically and
% processes executing a given script can be spawned dynamically on a
% given node. Special care has been given to developing the formal
% foundations of the Facile model. The basic philosophy of the Facile
% project has been to develop a language comprising a basic set of
% primitive constructs that are intuitive for programming, have a clear
% semantic formalisation and have a quite efficient implementation. These
% primitives are powerful enough for defining a variety of abstractions
% commonly employed in concurrent and distributed computing.

The pseudoknot benchmark does not use the distributed capabilities of
the Facile system. Yet it is interesting to see that the cost of
starting up a sequential program in the Facile system is small (26
milli seconds on machine~\sysfacile (c.f. Table~\ref{tbl:machine}).

\begin{table}
\begin{center}
\begin{tabular}{|l|r@{\,}r@{\,}r|r@{\,}r@{\,}r|}
\hline
version   & \mmm{unoptimised} &\mmm{optimised} \\
\hline
curried   & 8.48  &+& 0.51    & 8.38 &+& 0.51 \\
uncurried & 8.03  &+& 0.50    & 7.66 &+& 0.49 \\
\hline
\end{tabular}
\end{center}
\caption{Four versions of the pseudoknot program executed with the
Facile Antigua system, showing execution times (user+system seconds).}
\label{tbl:facile}
\end{table}

We did several experiments with the Pseudoknot benchmark program. The
results are shown in Table~\ref{tbl:facile}. Firstly, the Standard ML
version of the pseudoknot program was run unoptimised and unmodified
(i.e.\ with curried function definitions). Secondly, the execution time
was found to be unaffected by the following compiler optimisations:
loop unrolling, scope localisation of free variables, hoisting,
contraction, common subexpression elimination, range analysis, and
inlining. Thirdly, uncurrying function definitions made the program
slightly faster, and fourthly, provided more scope for the compiler
optimisations. The Facile compiler is essentially based on the SML/NJ,
version 0.93. In Section~\ref{sec:SML} we describe experiments with a
more recent release of the SML/NJ compiler, which is about twice as
fast for the pseudoknot benchmark.

\subsection{FAST}
\label{sec:FAST}
The FAST compiler input language is a subset of Miranda; the port from
Miranda to this subset is trivial. Three changes have been made to
achieve best performance. Firstly, all components of the main algebraic data types
(\verb=pt=, \verb=tfo= and \verb=var=) in the program were annotated as
strict. Secondly, the basic definitions of floating point operations
such as \verb|fadd = (+)| and \verb|fsin = sin| were inlined. Finally,
one construct that the FAST compiler should be able to deal with, but
which it cannot at present handle has been changed.
Functions such as:
\begin{verbatim}
> atom_pos atom (Var i t n) = absolute_pos (Var i t n) (atom n)
\end{verbatim}
were replaced by:
\begin{verbatim}
> atom_pos atom v = absolute_pos v (atom (var2nuc v))
> var2nuc (Var i t n) = n
\end{verbatim}

\begin{table}
\begin{center}
\begin{tabular}{|l|r@{\,}r@{\,}r|l|r@{\,}r@{\,}r|l|}
\hline
strictness & \multicolumn{4}{c|}{late}  & \multicolumn{4}{c|}{early} \\
annotations& \mmm{seconds} & Mbytes     & \mmm{seconds}   & Mbytes \\
\hline
without    & 31.8 &+& 0.7  & 89.5       & 30.1 &+& 4.9    & 94.1 \\
with       & 11.0 &+& 0.5  & 15.5       & 12.1 &+& 1.2    & 35.3 \\
\hline
\end{tabular}
\end{center}
\caption{Four versions of the pseudoknot program executed with the (lazy)
FAST system, showing execution times (user+system seconds) and heap
allocation (Mbytes).}
\label{tbl:fast}
\end{table}

To explore the possible advantage of laziness, a number of different
versions of the pseudoknot program have been compiled, executed and
analysed using the FAST system. Table~\ref{tbl:fast} shows the total
execution time (user+system seconds) and the total amount of heap
space (Mbytes) consumed by four different versions of the
program. Both the late and early formulations of the program were
tested with and without strictness annotations.  The strictness
annotations declare the three primary data types (\verb=pt=,
\verb=tfo= and \verb=var=) of the program strict.

Strictness annotations have a profound effect on the performance of the
pseudoknot program. The user time of the late formulation is reduced by a
factor of 2.9 and the user time of the early formulation is reduced by a
factor of 2.5. These improvements are due to the reduction in heap
allocation.
A less profound effect is caused by the laziness of the program.
There are two differences between the early and late formulations of the
pseudoknot program: the early formulation does fewer computations but it
allocates more heap space.
Regardless of whether strictness annotations are used or not, the
number of floating point multiplications made by the early formulation is
19\% less and the number of floating point additions is 14\% less.

Without strictness annotations the early formulation allocates 5\% more heap
space than the late formulation. The combined effect of allocating a
little more space, but doing less work makes the early formulation 9\%
faster than the late formulation.
With strictness annotations the early formulation allocates 44\% more space
than the late formulation. This time the reduction in heap space more than
cancels the savings in floating point operations. Now the late formulation
is 10\% faster than the early formulation.

\subsection{Gambit}
Gambit is an R4RS Scheme conformant optimizing compiler which supports
the whole numeric tower (complex, real, rational, and integer).
Measurements could not be done on the SPARC because currently the only
fully complete code generator is for M680x0 processors.  Since the
original program had been developed with Gambit the source code was
not modified in any way from the distributed source code (\cite{Fee94}
gives the details of the development process and an analysis of the
program when compiled with Gambit).

Both the late and early formulations were tested.
Table~\ref{tbl:gambit} shows the total execution time for the program
when compiled by Gambit 2.2 and gcc 2.0 on
machine~\sysgambit~(c.f. Table~\ref{tbl:machine}).  On this benchmark,
the code generated by Gambit executes at about 37\% the speed of C.
This slowdown is due in a large part to the fact that Gambit boxes all
floating point numbers.  To better evaluate this cost, the two most
numerically intensive functions (\verb=tfo_combine= and
\verb=tfo_apply=) were hand coded in assembler to avoid the boxing of
intermediate floating point values; the input and result of the
functions were still boxed.  The execution time of the late formulation
went down by a substantial 34\% to 11.4 seconds, that is 55.6\% the
speed of C.  Since the two functions are relatively small and have a
trivial data flow and control flow, a simple ``local'' type analysis
would probably be sufficient for the compiler to generate much better
code for this benchmark.

\begin{table}
\begin{center}
\begin{tabular}{|l|l|l|}
\hline
Compiler   & late       & early       \\
\hline
Gambit 2.2 & 17.30+0.22 & 25.90+0.35  \\
gcc 2.0    &  6.34+0.12 &  9.75+0.15  \\
\hline
\end{tabular}
\end{center}
\caption{Execution times (user+system seconds) for both formulations
of the pseudoknot program when compiled with Gambit and gcc 2.0.}
\label{tbl:gambit}
\end{table}

For both Gambit and gcc, the early formulation is roughly 50\% slower than
the late formulation.  Thus, for the pseudoknot data set, the cost of
lazy computation in the early formulation is larger than the saving
in computation it provides.  It is conceivable however that the early
formulation can surpass the late formulation if the program is run with a
data set which generates few solutions compared to the number of
shapes searched.  In such a case, a small proportion of the atom
positions will have to be computed.

\subsection{Glasgow Haskell}
\label{sec:ghc}
The Glasgow compiler is a highly-optimising Haskell compiler, with
elaborate performance monitoring tools.  The pseudoknot program was
subjected to much optimisation on the basis of results from these
tools.

The late formulation of the program (identical to the Chalmers Haskell
program described in Section~\ref{sec:hbc}) ran in 10.2 seconds on
machine~\sysglasgow~(c.f.  Table~\ref{tbl:machine}). Based on the raw
time profiling information from this program
(Table~\ref{tbl:glasgow-profile}-a), it is obvious that a few
functions account for a significant percentage of the time used, and
over 80\% of the total space usage. Three of the top four functions by
time (\verb=tfo_combine=, \verb=tfo_apply= and
\verb=tfo_align=) manipulate \verb=TFO=s and \verb=Pt=s, and the
remainder are heavy users of the \verb=Var= structure. Since these
functions can be safely made strict, they are prime candidates for the
use of unboxed types \cite{Pey91b}; types whose values can be
represented literally rather than by pointers to nodes in the heap.
Unboxing is desirable when possible both because execution is faster
and because a program's space requirements can generally be reduced.

\begin{table}
\begin{center}
\begin{tabular}{lr}
\begin{tabular}{|l|l|l|}
\hline
{\bf Cost Centre} &  \%time      & \%alloc \\
\hline
tfo\_combine      &       18.0   & 4.7 \\
tfo\_apply        &       15.9   & 0.0 \\
p\_o3'            &       8.3    & 25.5 \\
tfo\_align        &       6.2    & 1.9 \\
dgf\_base         &       5.9    & 21.6 \\
get\_var          &       5.9    & 0.0 \\
absolute\_pos     &       4.7    & 24.1 \\
...               &       ...    & ... \\
\hline
\multicolumn{3}{l}{(a) Original profile (by time)}
\end{tabular}
&
\begin{tabular}{|l|l|l|}
\hline
{\bf Cost Centre} &  \%time     & \%alloc \\
\hline
get\_var          &   11.1      &    0.0 \\
tfo\_combine      &   10.5      &   26.5 \\
p\_o3'            &    8.5      &   13.0 \\
pseudoknot\_constraint &    7.8 &    9.9 \\
search            &    7.8      &    2.6 \\
tfo\_align        &    5.2      &    5.6 \\
pt\_phi           &    5.2      &    0.0 \\
tfo\_apply        &    5.2      &    0.0 \\
...               &      ...    & ... \\
\hline
\multicolumn{3}{l}{(c) Maximum map (by time)}
\end{tabular}
\\
\begin{tabular}{|l|l|l|}
\hline
{\bf Cost Centre} &  \%time     & \%alloc \\
\hline
tfo\_apply        &   11.1      &    0.0 \\
tfo\_combine      &   10.5      &   23.2 \\
search            &    9.9      &    2.3 \\
pseudoknot\_constraint &    8.2 &    8.7 \\
get\_var          &    7.0      &    0.0 \\
var\_most\_distant&    6.4      &    8.7 \\
tfo\_align        &    5.8      &    4.9 \\
...               &      ...    & ... \\
\hline
\multicolumn{3}{l}{(b) Strict types (by time)}
\end{tabular}
&
\begin{tabular}{|l|l|l|}
\hline
{\bf Cost Centre} &  \%time     & \%alloc \\
\hline
tfo\_combine      &    9.7      &   26.5 \\
p\_o3'            &    7.7      &   13.0 \\
pseudoknot\_constraint &    4.6 &    9.9 \\
mk\_var           &    2.0      &    6.6 \\
tfo\_align        &   10.2      &    5.6 \\
tfo\_inv\_ortho   &    2.6      &    5.6 \\
...               &      ...    & ... \\
\hline
\multicolumn{3}{l}{(d) Maximum map (by allocation)}
\end{tabular}
\end{tabular}
\end{center}
\caption{Time and allocation profile by function, as a percentage of
total time/heap allocations.}
\label{tbl:glasgow-profile}
\end{table}

Unboxing these data structures semi-automatically, and strictifying the
lazy pattern match in the definition of \verb=var_most_distant_atom=
gives a significant performance improvement, of roughly a factor of 3.
This is similar to the improvements which are possible by simply
annotating the relevant data structures to make them strict. However,
further unboxing optimisations are possible if the three uses of the function composition
\verb=maximum . map= are replaced by a new function \verb=maximum_map=
as shown below. This function maps a function whose result is a
floating-point number over a list of arguments, and selects the maximum
result. It is not possible to map a function directly over a list of
unboxed values using the normal Prelude \verb=map= function, because
unboxed values cannot be passed to polymorphic functions.
\begin{verbatim}
> maximum_map :: (a->Float#) -> [a]->Float#
> maximum_map f (h:t) =
>    max f t (f h)
>    where max f (x:xs) m = max f xs (let fx = f x in
>                                     if fx `gtFloat#` m then fx else m)
>          max f [] m = m
>          max :: (a->Float#) -> [a] -> Float# -> Float#
\end{verbatim}

This optimisation is suggested indirectly by the time profile
(Table~\ref{tbl:glasgow-profile}-b) which shows that the top function
by time is \verb=tfo_apply=. This is called through \verb=absolute_pos=
within \verb=most_distant_atom=. It seems likely that the three nested
calls to produce the maximum value of a function over a list of values
can be merged to hold the current maximum in a register (an extreme
form of deforestation \cite{Wad90b}). This optimisation reduces the
total execution time to 1.8 seconds user time.

The final time profile (Table~\ref{tbl:glasgow-profile}-c) shows
\verb=get_var= and \verb=p_o3'= jointly using 20\% of the Haskell
execution time with \verb=tfo_combine=, \verb=tfo_align= and
\verb=tfo_apply= accounting for a further 20\%. (The minor differences
in percentage time for \verb=tfo_combine= in
Tables~\ref{tbl:glasgow-profile}-c and \ref{tbl:glasgow-profile}-d are
probably explained by sampling error over such a short run.) While the
first two functions could be optimised to use a non-list data
structure, it is not easy to optimise the latter functions any further.
Since the total execution time is now close to that for C, with a large
fraction of the total remaining time being spent in the Unix
mathematical library, and since the allocation profile
(Table~\ref{tbl:glasgow-profile}-d) also suggests that there are no
space gains which can be obtained easily, it was decided not to attempt
further optimisations. The Glasgow Haskell overall time and space results are
summarised in Table~\ref{tbl:glasgow-haskell-times}. The heap usage is
the total number of bytes allocated in each case, with the maximum live
data residency after a major garbage collection shown in parentheses.

As with Erlang and other C-based compilers, much of the compilation
time is spent in the C compiler.  A native code generator is
available, and is faster, but this does not allow all the
optimisations which were attempted.

It is planned to incorporate an automatic generalised version of the
deforesting optimisation, the foldr/build transformation~\cite{Gil94},
into the Glasgow Haskell compiler in due course.

\begin{table}
\begin{center}
\begin{tabular}{|l|r@{\,}r@{\,}r|r|l|r@{\,}r@{\,}r|r|l|}
\hline
Version      & \multicolumn{5}{c|}{late}    & \multicolumn{5}{c|}{early} \\
             & \mmm{seconds} & Mbytes& (Residency) & \mmm{seconds} & Mbytes & (Residency) \\
\hline
Original     &  10.0 &+& 0.2 &  36.8 & (55K)       &  11.0 &+& 0.3 & 57.6   & (419K) \\
Strict Types &   3.5 &+& 0.3 &  10.1 & (53K)       &   3.4 &+& 0.2 & 30.6   & (215K) \\
Maximum Map  &   1.8 &+& 0.1 &   7.6 & (46K)       &   2.9 &+& 0.1 & 29.3   & (215K) \\
\hline
\end{tabular}
\end{center}
\caption{Time and Heap Usage of three pseudoknot variants compiled for
machine~\sysglasgow~by the Glasgow Haskell compiler.}
\label{tbl:glasgow-haskell-times}
\end{table}

\subsection{Gofer}
The Gofer version of the program was derived very quickly from the
Miranda version supplied. The only significant changes were needed to
accommodate the different notations for infix operators and comments.
The Gofer primitive \verb=atan2= function was used in place of the
\verb=math_atan2= function in the Miranda code. Using \verb=math_atan2=
instead of the primitive \verb=atan2= adds only 4\% to the total
execution time.

Gofer does not use any significant optimizations, nor does it provide
a mechanism for annotating data type or function definitions to
indicate, for example, strictness properties. No attempts were made to
try to optimize the program to run faster or more efficiently in
Gofer.

\subsection{ID}
ID is an eager, non-strict, mostly functional, implicitly parallel
language. Only the purely functional subset of ID was used in this
benchmark. The compiler used in this study was developed jointly
between MIT and Berkeley. This first part of the compiler generates
dataflow graphs from ID source code. The Berkeley back end compiles
these dataflow graphs into Tl0 which is an abstract multi-threaded
machine~\cite{Gol94}. Then Tl0 is compiled into C. All of the other ID
compilers are targeted to dataflow machines. The Berkeley back end
creates separate files for each ID function, which contributes to
the long compile time for ID.

The port of the pseudoknot benchmark from Haskell to ID was trivial.
To improve the performance four changes were made. A number of the
lists were changed to arrays; the recursive functions \verb=get_var=
and \verb=search= were changed to be iterative using ID while loops;
the local function \verb=generate= within \verb=p_03'= was replaced by
an ID array comprehension; deforestation was applied (by hand) to the
function composition \verb=maximum . map=, as with the Glasgow
compiler (Section~\ref{sec:ghc}). These changes cut the execution
time roughly in half. They also reduced the space required for
execution. There is associated overhead with compiling for parallel
machines that we pay even on sequential machines. We compiled with
--O2 and linked all of the executables using --static to improve
performance.


\subsection{MLWorks}
The initial SML version of the pseudoknot program is not quite
Standard ML. It contains use of a pervasive list length function, and
an overloaded print function, neither of which are part of the
pervasive environment provided by the language. These problems were
corrected.

A list length function was provided in the obvious manner (i.e.\ tail
recursive accumulating). The uses of the overloaded print function
(two of) were replaced by calls to the relevant functions (output for
the string and an MLWorks real printing function for the real).

An initial profile of the run of this program suggests that slightly
over 50\% of the run time is taken up in three functions,
\verb=tfo_combine=, \verb=tfo_align= and \verb=tfo_apply=. These do
little more than floating point arithmetic and trigonometric functions,
which means that half the time, little more than the floating point
capability of the implementations is being tested. Some of that is
functionality provided by a runtime library implemented through
\verb=libc=.

ML is a strict language. Thus if a function is defined but only used in
one half of a conditional, a closure must still be built for that
function before evaluating the conditional. Building a closure, for
MLWorks, involves allocating heap and copying in values. Moving the
function definition within the conditional avoids unnecessary closure
building (which would be avoided anyway by a lazy implementation).
Small savings were achieved by modifying the function
\verb=pseudoknot_constraint= to build \verb=dist= only when required.
The function \verb=try_assignments= has been modified to be tail
recursive.

\subsection{NHC}
\label{sec:NHC}
The name ``NHC'' comes from the description ``Nearly a Haskell
Compiler''. Speed is not the main goal of NHC, instead NHC is written
so that it can recompile itself in less than 3 Mbytes of memory.
Floating point performance is completely ignored, and all arithmetic
operations are treated as user defined functions.

Two versions of the NHC compiler have been used. The first is NHC
compiled by HBC, denoted as NHC(HBC). This is a relatively fast, but
space hungry version of the compiler. The second version is NHC
compiled by itself, denoted as NHC(NHC). This version is slower, but
more economic in space.

The version of the pseudoknot program as it has been used with the HBC
compiler has been changed for NHC in two ways.

Firstly, all six separate modules were combined into one module. This
was done to show that NHC indeed compiles large programs using less
memory than the other Haskell compilers. NHC(NHC) needs an 8 Mbytes heap
to compile the single module version of the pseudoknot program, HBC
could not compile the single module version of the pseudoknot program
with 80 Mbytes of heap space, not even when using a single space garbage
collector. NHC(HBC) needs 30 Mbytes of heap to compile the single module
version of the pseudoknot program.

Secondly, all floating point types have been changed from \verb=Float=
to \verb=Double=. This does not increase the precision as \verb=Double=
is implemented as single precision floating point in NHC, but it is
necessary for the type checker.

The early formulation of the pseudoknot program is slightly faster
than the late formulation, 170 seconds compared to 175 seconds (both
on machine~\syschalmers~c.f. Table~\ref{tbl:machine}). A possible
explanation is that the early formulation does fewer arithmetic
computations than the late formulation, and that NHC is bad at
arithmetic.

\subsection{OPAL}
\label{sec:OPAL}
OPAL is based on language constructs similar to those found in most
other functional languages. The port of the pseudoknot program was a
matter of applying regular expressions to the SML and Haskell versions
of the program. Some specialities of OPAL
had to be tackled: (1) OPAL provides only strings and booleans as
builtin data types, such that constant data has to be denoted by
applying conversion functions to strings, and (2) OPAL requires an
explicit type signature for each global function. Furthermore, a static
limit on the number of components of a constructor made it necessary to
split the common atoms of the data type \verb=nuc= into two subrecords.

Only the late formulation of the pseudoknot program has been ported. No
optimizing annotations have been added to the source. OPAL is strict by
default, and the compiler performs inlining of function definitions
across module boundaries automatically. Hence, in the target code, all
floating point operations are inlined on the instruction level.

To measure the costs incurred in allocating floating point numbers in
the heap, a new single precision floating point data type has been
added to the system. These single precision numbers are represented as
``unboxed'' values, which thus do not incur garbage collection costs.
The first two rows of Table~\ref{tbl:opal} show the difference, as
measured on machine~\sysopal~(c.f. Table~\ref{tbl:machine}).
Arithmetic is still performed
with double precision, but the results of the operations are stored
with single precision. The timings suggest, that dynamically allocating
floating point numbers slows down the pseudoknot program by about a
factor of $2$. The amount of heap space allocated does not differ much
in both versions; this is a result of the reference counting based
garbage collection scheme of the OPAL system, which minimizes the total
amount of allocated memory at the expense of some execution speed.

The single precision floating point data type has been further refined
by encapsulating values which do not require dynamic type information.
These special values are not visible on the user level. However, after
automatic unfolding and simplifications performed by the compiler,
intermediate results of floating point operations are now passed
literally to subsequent operations in the resulting code. This is
similar to the way unboxed values are handled by the Glasgow Haskell
compiler~\cite{Pey91b}. Unboxing intermediate results improves the
execution time again by a factor of $2$. The results are shown in the
last row of Table~\ref{tbl:opal}.

The current distributed version 2.1b of the OPAL compilation system
failed to properly compile the benchmark. The reason was that this
compiler version packs all initialization code of a module into a
single C function (C is the target language), which is -- with several
thousand lines of code for the nucleotide conformation data base -- too
large for the C compiler. The compilation scheme has been slightly
modified to fix this design bug. The new scheme will be available in
the next release 2.1c.  As with other C-based compilers, more than
50\% of the compilation time for the pseudoknot program is used by the
GNU C compiler. The compilation is particularly slowed down by the
need to check and compile several thousand applications of overloaded
conversion functions from strings to numbers. This overhead is a
result of the fact that numbers cannot be denoted literally in the
OPAL language.

\begin{table}
\begin{center}
\begin{tabular}{|l|r@{\,}r@{\,}r|r|}
\hline
version                    & \mmm{time(s)} & space(M) \\
\hline
double precision (boxed)   & 19.9 &+& 0.26 & 1.10     \\
single precision (unboxed) &  9.7 &+& 0.23 & 0.85     \\
no intermediate type tags  &  5.1 &+& 0.14 & 0.85     \\
\hline
\end{tabular}
\end{center}
\caption{Three versions of the pseudoknot program executed with the
OPAL system on machine~\sysopal.}
\label{tbl:opal}
\end{table}

\subsection{RUFL}
RUFL is (almost) a subset of Haskell. It excludes type classes, and
type signatures are mandatory for all top-level functions. It has an
abridged module mechanism.

The source was ported from the modularized Chalmers Haskell version.
The bulk of the effort went into providing the type signatures and
module interface files. The main algebraic data types were renamed to
avoid ambiguity between similarly named types and constructors. One
function (\verb=make_relative_nuc=) was too complex for the compiler
and had to be split into two parts, one of the library primitives was
named differently, and a top-level \verb=main= function which does the
output replaced the Haskell construct. No annotations were made. (The
RUFL system does not support annotating components of data structures
as strict.)

The compiler generates SPARC assembly code or SECD-like intermediate
code which is interpreted. Timings for the interpreted version are
denoted here as RUFLI. In both cases run-time memory organization is
rather simplistically based around fixed-size cells, and algebraic data
types are instantiated as chains of these cells. The heavy use of
high-arity constructors in this example generates long chains and
incurs considerable run-time penalties.

\subsection{Sisal}
Collaborators at Lawrence Livermore National Laboratory and Colorado
State University developed the Sisal parallel functional language with
the objective of obtaining high performance on commercial scalar and
vector multiprocessors. Currently, mature Sisal compilers exist for a
variety of shared memory scalar and vector multiprocessors. The two
most important optimizations of the Sisal compiler are memory
preallocation~\cite{Ran87} and copy elimination~\cite{Gop89}. These
optimizations enable most Sisal programs to execute in place without
copying; thus Sisal programs typically run as fast as equivalent
imperative programs.

We ported the C version of the pseudoknot program to Sisal and made
the following minor changes to increase performance. First, we
converted the nucleotide database to an array-based structure instead
of the record-based structure as in the C code. Sisal supports
records, but the compiler is heavily optimized for arrays and array
operations.  Then, we rewrote the distance and transformation
functions as scalar functions to eliminate array allocations and
deallocations in the inner loops. Although array allocations and
deallocations in Sisal are efficient (cost is constant for arrays of
known size), it is difficult to recuperate the cost for small arrays.

Finally, we developed a recursive program that single threads the
\verb=partial_inst= stack and list of solutions. To accomplish this, we
changed the code of the function \verb=try= as follows:

\noindent
\begin{tabular}{l@{\hspace{10mm}}l}
C                              & Sisal revised \\
\hline
                               & \verb|> let| \\
add new element to stack       & \verb|>   stack1 := array_addh(stack)| \\
increment stack counter        & \verb|>   stack2 := pseudoknot_domains(stack1)| \\
pseudoknot\_domains            & \verb|> in | \\
decrement stack counter        & \verb|>   array_remh(stack2)| \\
                               & \verb|> end let|
\end{tabular}

In principle, this code is identical to the C code. The Sisal compiler
realizes that there is only a single consumer of each stack. It tags
the data structure as mutable and generates code to perform all updates
in place. Consequently, the Sisal code maintains a single stack
structure similar to the C code, eliminating excessive memory usage and
copy operations. The Sisal code runs in approximately 85KB of memory
and achieves execution speeds comparable to the C code.

\subsection{SML/NJ and Poly/ML}
\label{sec:SML}
Both Standard ML of New Jersey and Poly/ML are implementations of
Standard ML as described in the Definition of Standard
ML~\cite{Mil90}. The SML version of the pseudoknot application was
initially derived from the Scheme version of the program (by Marc
Feeley) and then subsequently modified to make better use of SML's
typing facilities. These modifications had no effect on the execution
time of the program, but improved its readability and robustness, and
this was helpful for later experimentation with the code.

For SML/NJ, a signature was used to constrain the program (which was
written as one large SML module) to export only the top-level
\verb|run| function. This had the effect of improving the compilation
time as well as providing the compiler with more opportunities for
automatic inlining and other optimizations. Other than that, no other
changes were made to the program (though various things were tried, as
explained below).

Initially, the program was compiled with versions 0.75 and 0.93 of the
SML/NJ system. It was expected that the latest version (1.03z) would
show a great improvement, since it employs a representation
analysis~\cite{Ler92}, implemented by Zhong Shao (described in his
Ph.D. thesis~\cite{Sha94}), which would ``unbox'' many of the
floating-point numbers stored in the rather large tuple data
structures. Surprisingly, the first attempt with this version of the
compiler resulted in a slowdown. This was due to the fact that the
implementation of the representation analysis was not finished. In
particular, unboxing of records of floating-point numbers was not yet
supported, nor were floats being passed to functions in floating-point
registers. When these features were completed (by Shao), the
speed improved by more than a factor of two. Analysis of the compiler
output for the pseudoknot program also showed that excessive
register-spilling was occurring. A small improvement to the spilling
heuristic (again by Shao) led to a further speedup of the code. The
SML/NJ 1.03z compiler does not use FPU load and store instructions for
loading and storing floating-point numbers. It is strongly suspected
that changing this would lead to yet more large improvements to the
running time.

Like the other functional versions of the pseudoknot program (including
the Haskell and CLEAN versions), the SML version is ``pure'' in the
sense that all of the data structures are immutable. (The SML program
is also evaluated strictly; note, however, that the Haskell and CLEAN
versions also use mostly strict evaluation.) Significant improvements
might be possible if mutable arrays were used, in a manner similar to
the C version of the program. However, one suspects that such a
program would be less reliable and understandable than the current
version.

A large number of variations on the SML program were tried, in an
attempt to find better performance. The execution time was
surprisingly stable under these changes, and in fact no change made
any significant difference (good or bad) to the execution
speed. Almost all of the functions in the program are presently
curried; uncurrying made a barely measurable improvement. All of the
various ``deforestation'' transformations used by the other
implementations were also tried, and again none made any measurable
improvement (though several led to minor slowdowns). It was recognized
early on that the representation of the ``tfo'' matrix as a large
tuple of values leads to a considerable amount of register spilling in
the code generated by SML/NJ\@. However, changing the representation
to an array of floating-point numbers made no improvement. The current
version of the program also uses the late formulation for position
computation of atoms; changing this to the early formulation greatly
increased both the time and space required. In the end, the original
transcription of the Scheme program, with the signature constraint,
was used for all of the measurements.

The SML/NJ implementation of the pseudoknot program performs better on the DECstation 5000 than
on the SPARC. On the DECstation 5000 it runs at 55\% of the speed of C,
on the SPARC it runs at only 36\% of the speed of C. We suspect that
this is mainly due to memory effects. Previous studies~\cite{Diw94}
have shown that the intensive heap allocation which is characteristic
of the SML/NJ interacts badly with memory subsystems that use a
write-no-allocate cache policy, as is the case of the SPARC; in
contrast, the use of a write-allocate policy coupled with what amounts
to sub-block placement on the DECstation (the cache block size is four
bytes) supports such intensive heap allocation extremely well.

\subsection{Stoffel}
The Stoffel language is an intermediate language designed for studying
code generation of high level languages to fine-grain parallel
processors (see, for example, Fisher~\cite{Fis84}). Because of this, the
implementation is robust but lacks the sophistication of some of the
other compilers discussed here. The source code that has been used is
the same as that used for the FAST compiler (see Section~\ref{sec:FAST}),
but without the strictness annotations. The intermediate format
used by the FAST compiler has been translated automatically into
Stoffel by the FAST compiler front-end.

The Stoffel compiler is based on a spineless abstract machine and
implements many of the optimization commonly found, such as strictness
and boxing analysis. The implementation uses double precision floating
point numbers, which can also be passed around in unboxed form.

Most discouraging were the compilation times. The actual Stoffel
compiler takes about 216 seconds to run, the gcc-compiler backend with
--O2 takes more than two hours on machine~\sysstoffel~(c.f.
Table~\ref{tbl:machine}). Most of this time is spent in compiling the
function that initialises the floating point number representations.

Both the late and early formulations of the program were run {\it out
of the box}, there was no tweaking. The late formulation runs 30\%
faster than the early formulation, the difference being relatively small
because no strictness annotations have been used.

\subsection{Trafola}
The Trafola language and system has been designed as a (pure
functional) transformation language in a compiler construction
project. In addition to the usual functional constructs Trafola offers
nondeterministic pattern matching. This allows for an expressive coding
of algorithms, in particular tree transformations. The first version of
the system and the language were untyped. User defined data types and
Milner/Cardelli type inference were added later. At the moment there
is still only one Trafola compiler and runtime system. This compiler
is able to generate better code from typed programs than from untyped
programs. The runtime system has to support both, at the cost of some
efficiency.

\begin{table}
\begin{center}
\begin{tabular}{|l|r|r@{\,}r@{\,}r|r|}
\hline
floats & compilation (user time) & \mmm{execution} & garbage collection \\
\hline
Single &  6.16                   & 162.6 &+& 2.4   &   4\% \\
Double &  6.35                   & 186.5 &+& 5.7   &  11\% \\
\hline
\end{tabular}
\end{center}
\caption{Trafola results comparing single and double precision arithmetic.}
\label{tbl:trafola}
\end{table}

The pseudoknot program was transformed from Miranda to typed Trafola
basically by changing syntax.  No special Trafola features were
introduced. Basic definitions such as
\verb|fadd = (+)| were inlined and taken from the C~library. The fact
that Trafola was first designed as an untyped language, together with
the provision of powerful pattern matching facilities, results in an
unusual implementation of algebraic data types. Each $n$-ary
constructor is represented as an $n+1$-tuple. The first component of
the tuple is an element of an enumeration type e.g. \verb=Foo a b c= is
implemented as \verb=(Foo,a,b,c)=.

The Trafola version of the pseudoknot program was compiled and
executed using both single and double precision arithmetic. The
results are shown in Table~\ref{tbl:trafola}. Using double precision
arithmetic increases compilation time because the internal
representation of double precision floats is less compact, and because
constant-folding also takes longer for doubles.
At runtime, double precision floats are stored in the heap, whereas
single precision values are stored in the stack. The double
precision version therefore allocates more space and increases both
execution and garbage collection times.

\subsection{Yale Haskell}
The Yale Haskell compiler uses Common Lisp as an intermediate language
and relies on the Common Lisp compiler for translation to machine
code. The version of Yale Haskell tested here uses CMU Common Lisp as
its back end, although it also runs under most other Common Lisp
implementations.

To prepare the pseudoknot program for running in Yale Haskell, all of
the data structures were annotated as strict. (Internally, Yale
Haskell represents the data structures as Common Lisp simple vectors
containing tagged objects.)

The first argument of \verb=get_var= and the arguments of
\verb=make_relative_nuc= have been forced to be strict because the case
where these arguments are not used is an error. The functions
\verb=is_A=, \verb=is_C=, and \verb=is_G= have been inlined because
they are small; this probably has no effect on timings. The functions
\verb=atom_pos= and \verb=search= have been inlined to avoid a
higher-order function call. These were the only changes made to the
Chalmers Haskell version of the program; the actual code was unchanged.

The code was compiled with all optimizations enabled at the Haskell
level. At the Lisp level, the same optimizations were used as for
obtaining the CMU Common Lisp results. The program was run with a heap
size of about 14 megabytes.
The late formulation of the pseudoknot program runs more than
twice as fast as the early formulation.

\subsection{CMU Common Lisp}
CMU Common Lisp is a public-domain implementation featuring a
high-quality optimizing compiler. This compiler does a higher
level of static type checking and type inference than most other Common
Lisp compilers.

In the Common Lisp version of the program, the \verb=pt= and \verb=tfo=
data types were implemented as vectors specialized to hold untagged
\verb=single-float= objects, rather than as general vectors of tagged
objects. Other data types were implemented using the \verb=defstruct=
facility. Type declarations were added to all of the floating-point
arithmetic operations and local variables that hold floating-point
numbers. Otherwise, the code was a straight translation of the program
from Scheme syntax. The resulting code is written in completely
standard Common Lisp and uses no annotations or extensions specific to
the CMU implementation.

The program was compiled with optimizations \verb=(speed 3)=
\verb=(safety 0)= \verb=(debug 0)= \verb=(compilation-speed 0)=. CMU
Lisp's block compilation feature was not used. The program was run with
a heap size of about 14 megabytes.

The difference between the Yale Haskell and Common Lisp results as shown in
Table~\ref{tbl:execution} is due partly to use of tagged versus
untagged arrays, and partly to the overhead of lazy lists in Haskell.
These were the only significant differences between the hand-written
Common Lisp code and the Lisp code produced by the Yale Haskell compiler. The
Haskell code generator could be extended to use untagged arrays for
homogeneous floating-point tuple types as well, but this has not yet
been implemented.

\section{Results}
\label{sec:results}
The pseudoknot program has always been compiled with option settings
that should give fast execution, we have consistently tried to
optimise for execution speed. The compile time and run time options
used are shown in Table~\ref{tbl:option}. To achieve best performance,
no debugging, run time checks or profiling code has been
generated. Where a ``--O'' option or higher optimisation setting could
be used to generate faster code, we have done so.

\begin{table}
\small
\begin{center}
\begin{tabular}{|r|l|l|l|l|l|l|}
\hline
no.           &machine       & mem. & cache   & op. system  & processor   & C compiler \\
\hline
\sysbigloo    &SUN 4/670      & 64 M& 64 K    &SunOS 4.1.3. &standard     &gcc 2.5.7 \\
\syscaml      &DECstat., 5000/200 &32 M&64 K  &Ultrix 4.1.  &standard     &cc --O2  \\
\syschalmers  &SUN SPARC 10/30& 32 M&1 M      &SunOS 4.1.3. &TMS390Z55    &gcc 2.5.8 \\
\sysclean     &SUN 4/670      & 64 M&64 K     &SunOS 4.1.2. &Cypress CY605&gcc 2.4.5 \\
\syscmucl     &SUN 4/690MP    & 64 M&64 K     &SunOS 4.1.3  &ROSS 40MHz Super&cc \\
\syserlang    &SUN 4/50       & 32 M&64 K     &SunOS 4.1.3. &standard     &gcc 2.5.8 \\
\sysfast      &SUN 4/690      & 64 M&64 K     &SunOS 4.1.2. &standard     &gcc 2.5.8 \\
\sysgambit    &HP9000/385     & 32 M&4K/4K    &HP-UX B.09.00&68040        &gcc 2.0 \\
\sysglasgow   &SUN SPARC 10/41& 96 M&20K/32K+1M&SunOS 4.1.3.&TMS390Z50    &gcc 2.5.7 \\
\sysid        &SUN SPARC 10/41& 96 M& 1 M     &SunOS 4.1.3. &standard     &gcc 2.5.8 \\
\sysmlworks   &SUN 4/330      & 96 M&         &SunOS 4.1.1. &standard     &gcc 2.5.4 \\
\sysopal      &SUN 4/75       & 64 M&64 K     &SunOS 4.1.3. &standard     &gcc 2.5.8 \\
\syspolyml    &SUN 4/690      & 64 M&1 M      &SunOS 4.1.3. &standard     &gcc 2.5.8 \\
\syssisals    &SUN 4/670MP    & 64 M&1 M      &SunOS 4.1.3. &SUNW, system 600&gcc 2.5.8 \\
\syssisalc    &CRAY C90       & 1024 M & none &UNICOS 7.C   &custom vector&scc 4.0 \\
\syssml       &DECstat. 5000/200& 128 M&64 K  &Mach 2.6     &standard     &gcc 2.4 \\
\sysstoffel   &SUN SPARC 10/41&128 M&20K/32K+1M&Solaris 2.3 &standard     &gcc 2.5.8 \\
\systrafola   &SUN 4/670      & 64 M&64 K     &SunOS 4.1.3. &Cypress CY605&gcc 2.4.5 \\
\sysfloat     &SUN SPARC 5    & 32 M&24 K     &SunOS 4.1.3. &standard (85 MHZ)&gcc 2.5.8 \\
\sysfacile    &SUN Sparc 10/41& 64 M&1M       &SunOS 4.1.3. &standard     &gcc 2.5.7 \\
\hline
\end{tabular}
\end{center}
\normalsize
\caption{Details of the machines and C compilers used to compile and/or
execute the pseudoknot program. The make and type of the machine is
followed by the size of the memory (in MB) the size of the cache
(as a total or as instruction/data + secondary cache size),
the operating system name and version, and the type of processor
used. The last column gives the C compiler that has been used on the
machine.}
\label{tbl:machine}
\end{table}

We have tried to use one and the same machine where possible, but not
all programs could be compiled and/or executed on the same machine,
either because of commercial considerations, or because of the lack of
a SPARC code generator. An overview of the machines used may be found
in Table~\ref{tbl:machine}.

To factor out architectural differences, the C version of the
pseudoknot program has been timed on all the machines involved. The
execution time of the C version serves as the basic unit of time. The
unit ``pseudoknot'' gives the relative speed with respect to C
execution. It is computed as:
\[
\mbox{relative speed} ~ = ~
\frac{100 \times \mbox{C execution time}}{\mbox{absolute time}}
\]
To ``run at 100 knots'' thus means to run at exactly the same speed as
C, and a speed of 1 knot is 1\% of the speed of C.

\begin{table}
\begin{center}
\begin{tabular}{|r|l|r|}
\hline
machine     & cache size & pseudoknots \\
\hline
\sysfast    & 64KB       & 73\% \\
\sysstoffel & 20K/32K+1M & 92\% \\
\syssisalc  & 36K+1M     & 100\% \\
\hline
\end{tabular}
\end{center}
\caption{The relative performance of the Sisal version as a function of
the cache size.}
\label{tbl:cache}
\end{table}

The functional language compilers generate code that requires more
memory than the code generated by the C compilers. Therefore, the
functional implementations require larger caches to accommodate the
same fraction of the code and data as the C implementation. This has a
measurable effect on the pseudoknots. For example, we found the
pseudoknots for the Sisal version of the benchmark program to increase
with the cache size. Table~\ref{tbl:cache} shows the speed of the Sisal
version of the pseudoknot program relative to that of C on three
machines with different cache sizes. We could have used a machine with
a cache larger than 64K for all experiments to make the results look better. We
have chosen not to do so because it is important to remember that using
large amounts of memory is an important cost factor.

The ``pseudoknot'' as defined above is a relative measure. In the
long run an absolute measure is also useful both as a measure of overall
system performance, and to demonstrate absolute improvements in functional
compiler technology independently of improvements in the C compilers
(c.f. Drhystone, Whetstone). This requires choosing
a particular architecture and C compiler as a reference to represent
the absolute 100\% mark. Most execution times have been obtained on
machine~\sysfast~(c.f.  Table~\ref{tbl:machine}), using GCC version
2.5.8. This combination is therefore rather a convenient choice to
deliver the absolute 100\% pseudoknot. The C execution times for this
machine are 2.71 seconds user time and 0.20 seconds system time.

To measure the times required by the faster programs with a reasonable
degree of accuracy, the programs have been timed in a Unix C-shell loop
as follows \verb=time repeat 10 a.out=, or even \verb=time repeat 100
a.out=. The resulting system and user times divided by 10 (100) are
reported in the Tables~\ref{tbl:compilation} and~\ref{tbl:execution},
sorted by relative pseudoknot ratings on the user times.

\begin{table}
\begin{minipage}{\hsize}
\begin{center}
\begin{tabular}{|l|c|c|r@{\,}r@{\,}r|rr|r@{\,}r@{\,}r|r@{\,}r@{\,}r|r@{\,}r@{\,}r|}
\hline
compiler  &route& mach.        & \mmm{time(s)}      & \mm{space} &\mmm{C-time(s)}&\multicolumn{6}{c|}{pseudoknots (\%)}\\
            &   &              & user    &+&  sys   & Mb     &\footnote{A = Mbytes allocated space; H = Mbytes heap size; R = Mbytes maximum resident set size}
                                                                 & user &+& sys  & \mmm{rel}     & \mmm{abs} \\
\hline
Gofer       & I &\sysfast      &    6.7  &+&   0.7  &   3\dz & A & 2.71 &+& 0.20 & 40.4 &+& 28.6 & 40.4 &+& 28.6 \\
RUFLI       & I &\sysfast      &    9.1  &+&   1.7  &   1\dz & A & 2.71 &+& 0.20 & 29.8 &+& 11.8 & 29.8 &+& 11.8 \\
Miranda     & I &\sysfast      &   12.5  &+&   0.8  &  13\dz & A & 2.71 &+& 0.20 & 21.7 &+& 25.0 & 21.7 &+& 25.0 \\
&&&&&&&&&&&&&&&&\\
Caml Light  & I &\syscaml      &   29.7  &+&   1.1  &   2.3  & A & 2.66 &+& 0.05 &  9.0 &+&  4.5 &  9.1 &+& 18.2 \\
Clean       & N &\sysclean     &   30\dz &+&  10\dz &   9\dz & A & 2.69 &+& 0.55 &  9.0 &+&  5.5 &  9.0 &+&  2.0 \\
Trafola     & I &\sysfast      &   31.4  &+&  11.5  &   6\dz & A & 2.71 &+& 0.10 &  8.6 &+&  1.7 &  8.6 &+&  1.7 \\
RUFL        & N &\sysfast      &   41.6  &+&   8\dz &   3\dz & A & 2.71 &+& 0.20 &  6.5 &+&  2.5 &  6.5 &+&  2.5 \\
Poly/ML     & I &\syspolyml    &   22.4  &+&   3.1  &   3.3  & A & 1.28 &+& 0.05 &  5.7 &+&  1.6 & 12.1 &+&  6.5 \\
Bigloo      & C &\sysbigloo    &   56.5  &+&   6.4  &   7.5  & A & 3.00 &+& 0.10 &  5.3 &+&  1.6 &  4.8 &+&  3.1 \\
Gambit      & N &\sysgambit    &  147\dz &+&  77\dz &  14\dz & H & 6.34 &+& 0.12 &  4.3 &+&  0.2 &  1.8 &+&  0.3 \\
CMU CL      & N &\syscmucl     &  118\dz &+&  25\dz &  14\dz & H & 4.73 &+& 0.43 &  4.0 &+&  1.7 &  2.3 &+&  0.8 \\
Camloo    & S+C &\sysbigloo    &   98\dz &+&  17.6  &   4.6  & A & 3.00 &+& 0.10 &  3.1 &+&  0.6 &  2.8 &+&  1.1 \\
Caml Gallium& N &\syscaml      &  129\dz &+&   4.4  &   3.7  & A & 2.66 &+& 0.05 &  2.1 &+&  1.1 &  2.1 &+&  4.5 \\
&&&&&&&&&&&&&&&&\\
SML/NJ      & N &\syssml       &  131\dz &+&   4.4  &  50\dz & A & 1.90 &+& 0.10 &  1.5 &+&  2.3 &  2.1 &+&  4.5 \\
Facile      & N &\sysfacile    &  123\dz &+&   2.5  &  11.3  & A & 1.71 &+& 0.08 &  1.4 &+&  3.2 &  2.2 &+&  8.0 \\
NHC(HBC)    & I &\syschalmers  &  122\dz &+&   7.3  &  30\dz & A & 1.56 &+& 0.06 &  1.3 &+&  0.8 &  2.2 &+&  2.7 \\
Sisal (SUN) & N &\syssisals    &  112\dz &+&  13.3  &   2.4  & A & 1.40 &+& 0.05 &  1.3 &+&  0.4 &  2.4 &+&  1.5 \\
MLWorks     & N &\sysmlworks   &  394\dz &+&  19\dz &  14.4  & R & 4.90 &+& 0.00 &  1.2 &+&  0.0 &  0.7 &+&  1.1 \\
&&&&&&&&&&&&&&&&\\
Sisal (CRAY)& N &\syssisalc    &   82.5  &+&  15.5  &  24\dz & A & 0.76 &+& 0.02 &  0.9 &+&  0.2 &  3.3 &+&  1.3 \\
Yale        & L &\syscmucl     &  610\dz &+& 186\dz &  14\dz & H & 4.73 &+& 0.43 &  0.8 &+&  0.2 &  0.4 &+&  0.1 \\
Chalmers    & N &\syschalmers  &  181\dz &+&  45\dz &  50\dz & A & 1.33 &+& 0.06 &  0.7 &+&  0.1 &  1.5 &+&  0.4 \\
FAST        & C &\sysfast      &  450\dz &+&  40\dz & 100\dz & A & 2.71 &+& 0.20 &  0.6 &+&  0.5 &  0.6 &+&  0.5 \\
&&&&&&&&&&&&&&&&\\
NHC(NHC)    & I &\syschalmers  &  560\dz &+&   5.0  &   8.7  & A & 1.56 &+& 0.06 &  0.3 &+&  1.2 &  0.5 &+&  4.0 \\
Glasgow     & C &\sysglasgow   &  564\dz &+&  30\dz &  47\dz & A & 1.28 &+& 0.05 &  0.2 &+&  0.2 &  0.5 &+&  0.7 \\
Opal        & C &\sysopal      & 1301\dz &+&  19\dz &  15\dz & A & 3.00 &+& 0.08 &  0.2 &+&  0.4 &  0.2 &+&  1.1 \\
&&&&&&&&&&&&&&&&\\
Erlang BEAM & C &\syserlang    & \mmm{$>$ 1 Hour}   &   8\dz & A & 3.27 &+& 0.10 &  \mmm{---}    &  \mmm{---} \\
CeML        & C &\sysbigloo    & \mmm{$>$ 1 Hour}   &  35\dz & A & 3.00 &+& 0.10 &  \mmm{---}    &  \mmm{---} \\
ID          & C &\sysid        & \mmm{$>$ 1 Hour}   &  64\dz & A & 2.75 &+& 0.05 &  \mmm{---}    &  \mmm{---} \\
Stoffel     & C &\sysstoffel   & \mmm{$>$ 2 Hours}  &  25\dz & A & 1.28 &+& 0.05 &  \mmm{---}    &  \mmm{---} \\
\hline
cc --O      & N &\sysfast      &  325\dz &+&  26\dz &   8\dz & A & 2.71 &+& 0.20 &  0.8 &+&  0.8 &  0.8 &+&  0.8 \\
gcc --O     & N &\sysfast      &  910\dz &+&  97\dz &  21\dz & A & 2.71 &+& 0.20 &  0.3 &+&  0.2 &  0.3 &+&  0.2 \\
\hline
\end{tabular}
\end{center}
\end{minipage}
\caption{Results giving the amount of time (user+system time in
seconds) and space (in Mbytes) required for compilation of the
pseudoknot program. The ``pseudoknots'' give the relative speed with
respect to the execution (not compilation) of the C version as a
percentage.}
\label{tbl:compilation}
\end{table}

\subsection{Compilation}
Table~\ref{tbl:compilation} shows the results of compiling the
programs. The first column of the table shows the name of the compiler
(c.f. Table~\ref{tbl:compiler}). The second column ``route'' indicates
whether the compiler produces native code (``N''), code for a special
interpreter (``I''), or compiles to native code through a portable C
back-end (``C''), Lisp (``L''), or Scheme (``S''), or through a
combination of these back-ends.  The third column gives a reference to
the particular machine used for compilation (c.f.
Table~\ref{tbl:machine}). The next two columns give the time and space
required to compile the pseudoknot program. Unless noted otherwise,
the space is the largest amount of space required by the compiler, as
obtained from \verb=ps -v= under the heading SIZE. The column marked
``C-time'' gives the time required to execute the C version of the
pseudoknot program on the same machine as the one used for
compilation. The last two columns ``pseudoknots'' show the relative
and absolute performance with respect to C.

It is possible to distinguish broad groups within the compilers.  The
fastest compilers are, unsurprisingly, those that compile to an
intermediate code for an interpreter, and which therefore perform few,
if any, optimisations.  NHC is an outlier, perhaps because unlike the
other interpreters, it is a bootstrapping compiler.  With the
exception of Bigloo, which is faster than many native compilers,
compilers that generate C are the slowest compilers.  Not only does it
take extra time to produce and parse the C, but C compilers have
particular difficulty compiling code that contains large numbers of
constants. As noted in Section~\ref{sec:compilers}, C compilers also have
difficulty compiling the hand written C version of the pseudoknot
program due to this phenomenon.  The faster compilers also generally
allocate less space.  This may be because the slower compilers
generally apply more sophisticated (and therefore space-intensive)
optimisations.

\begin{table}
\begin{minipage}{\hsize}
\begin{center}
\begin{tabular}{|l|c|c|c|r@{\,}r@{\,}r|rr|r@{\,}r@{\,}r|r@{\,}r@{\,}r|}
\hline
compiler  &route& mach.     &\footnote{L = late formulation; E = early formulation}
                                & \mmm{time(s)}      &\mm{space} &\multicolumn{6}{c|}{pseudoknots (\%)}\\
            &   &           &   & user    &+&  sys   & Mb    & \footnote{A = Mbytes allocated space; H = Mbytes heap size}
                                                                 & \mmm{rel}       & \mmm{abs} \\
\hline
Sisal (CRAY)& C &\syssisalc & L &   0.68  &+&  0.03  &  0.1  & A & 111\dz&+&  96.0 & 398\dz&+& 800\dz\\
&&&&&&&&&&&&&&\\
Sisal (SUN) & C &\sysfast   & L &   3.7\z &+&  0.2\z &  0.7  & A &  73.2 &+& 100\dz&  73.2 &+& 100\dz\\
Caml Gallium& N &\syscaml   & L &   3.8\z &+&  0.1\z &  0.3  & A &  70.7 &+&  62.5 &  72.1 &+& 250\dz\\
Glasgow     & C &\sysfast   & L &   3.9\z &+&  0.2\z &  1\dz & A &  69.5 &+& 100\dz&  69.5 &+& 100\dz\\
Opal        & C &\sysfast   & L &   4.7\z &+&  0.5\z &  0.8  & A &  57.4 &+&  40.0 &  57.4 &+&  40.0 \\
Clean       & N &\sysfast   & L &   5.1\z &+&  0.8\z &  2.5  & A &  52.7 &+&  25.6 &  52.7 &+&  25.6 \\
&&&&&&&&&&&&&&\\
CMU CL      & N &\sysfast   & L &   5.8\z &+&  3.3\z & 14\dz & H &  46.7 &+&   6.1 &  46.7 &+&   6.1 \\
Gambit      & N &\sysgambit & L &  17.3\z &+&  0.2\z & 14\dz & H &  36.6 &+&  60.0 &  15.7 &+& 100\dz\\
SML/NJ      & N &\sysfast   & L &   7.6\z &+&  2.8\z &  2.6  & A &  35.9 &+&   7.0 &  35.9 &+&   7.0 \\
MLWorks     & N &\sysmlworks& L &  13.7\z &+&  0.9\z &  2.6  & A &  35.8 &+&   0.0 &  19.8 &+&  22.2 \\
CeML        & C &\sysfast   & L &   8.7\z &+&  0.6\z &  2\dz & A &  31.1 &+&  33.3 &  31.1 &+&  33.3 \\
&&&&&&&&&&&&&&\\
FAST        & C &\sysfast   & L &  11.0\z &+&  0.5\z &  1\dz & A &  24.6 &+&  40.0 &  24.6 &+&  40.0 \\
Camloo    & S+C &\sysfast   & L &  11.5\z &+&  1.3\z &  4.9  & A &  23.6 &+&  15.4 &  23.6 &+&  15.4 \\
ID          & C &\sysfast   & L &  11.6\z &+&  2.9\z & 14\dz & A &  23.4 &+&   6.9 &  23.4 &+&   6.9 \\
Bigloo      & C &\sysfast   & L &  11.7\z &+&  1.1\z &  4.9  & A &  23.2 &+&  18.2 &  23.2 &+&  18.2 \\
Yale        & L &\sysfast   & L &  11.9\z &+&  7.2\z & 14\dz & H &  22.8 &+&   2.8 &  22.8 &+&   2.8 \\
Chalmers    & N &\sysfast   & L &  12.1\z &+&  1.0\z &  3\dz & A &  22.4 &+&  20.0 &  22.4 &+&  20.0 \\
Facile      & N &\sysfast   & L &  15.5\z &+&  4.3\z &  7.9  & A &  17.5 &+&   4.7 &  17.5 &+&   4.7 \\
Stoffel     & C &\sysfast   & L &  26.6\z &+&  2.1\z &  5.6  & A &  10.2 &+&   9.5 &  10.2 &+&   9.5 \\
&&&&&&&&&&&&&&\\
Erlang BEAM & C &\sysfast   & L &  31.8\z &+&  4.5\z & 11\dz & A &   8.5 &+&   4.4 &   8.5 &+&   4.4 \\
Caml Light  & I &\sysfast   & L &  53.9\z &+&  7.5\z &  0.3  & A &   5.0 &+&   2.7 &   5.0 &+&   2.7 \\
RUFL        & N &\sysfast   & L &  87\dzz &+&  2.8\z &  3\dz & A &   3.1 &+&   7.1 &   3.1 &+&   7.1 \\
Poly/ML     & I &\syspolyml & E &  54.7\z &+&  8.4\z &  3.3  & A &   2.3 &+&   0.6 &   5.0 &+&   2.4 \\
Trafola     & I &\sysfast   & L & 124\dzz &+&  6.3\z & 10.7  & A &   2.2 &+&   3.2 &   2.2 &+&   3.2 \\
NHC         & I &\sysfast   & E & 170\dzz &+&  2.2\z &  2.6  & A &   1.6 &+&   9.1 &   1.6 &+&   9.1 \\
&&&&&&&&&&&&&&\\
Gofer       & I &\sysfast   & L & 370\dzz &+& 12.0\z &  3\dz & A &   0.7 &+&   1.7 &   0.7 &+&   1.7 \\
RUFLI       & I &\sysfast   & L & 529\dzz &+& 13.0\z &  4\dz & A &   0.5 &+&   1.5 &   0.5 &+&   1.5 \\
Miranda     & I &\sysfast   & L &1156\dzz &+& 34.0\z & 13\dz & A &   0.2 &+&   0.6 &   0.2 &+&   0.6 \\
\hline
\end{tabular}
\end{center}
\end{minipage}
\caption{Results giving the amount of time (user+system time in
seconds) and space (in Mbytes) required for execution of the pseudoknot
program. The ``pseudoknots'' give the relative speed with respect to
the execution of the C version as a percentage.}
\label{tbl:execution}
\end{table}

\subsection{Execution}
All programs have been executed a number of times, with different heap
sizes to optimise for speed. The results reported in
Table~\ref{tbl:execution} correspond to the fastest execution. This
includes garbage collection time.
The first column of the table shows the name of the
compiler/interpreter (c.f. Table~\ref{tbl:compiler}). The second column
``route'' duplicates the ``route'' column from Table~\ref{tbl:compiler}.
The third column gives a reference to the
particular machine used (c.f. Table~\ref{tbl:machine}). The fourth
column specifies whether the late (``L'') or early (``E'') formulation of
the program gives the fastest execution. Columns 5 and 6 give the time
and space required to execute the pseudoknot program. Unless noted
otherwise, the space is the largest amount of space required by the
program, as obtained from \verb=ps -v= under the heading SIZE.
The execution time of the C version of the pseudoknot program for the
machine used may be found in Table~\ref{tbl:compilation}.
Please note that in most cases execution times and compilation times
were measured on different machines. The last two columns ``pseudoknots''
show the relative and absolute performance with respect to C.

Again, broad performance groupings can be distinguished.
The highest (relative) pseudoknots are measured for the Sisal compiler,
which is actually faster than the corresponding C for the Cray version.
The next best implementations are the Caml Gallium
compiler (eager) and the Glasgow Haskell compiler (lazy). It is
probably fair to say that the Glasgow Haskell version of the program
has been more heavily optimised than the CAML Gallium version.

As the Clean and
Glasgow Haskell compilers show, if the compiler can exploit strictness
at the right points, the presence of lazy evaluation need not be a
hindrance to high performance.  It is also interesting that several
of these compilers compile through C rather than being native compilers.
Clearly, it is possible to compile efficient code without generating
assembler directly.  Space usage for these compilers is also generally low:
the compilers have clearly optimised both for time and space.

The Lisp, Scheme and SML compilers generally yield very similar
performance (35-50 pseudoknots).  An obvious outlier is the Bigloo
optimising Scheme compiler, whose performance is comparable to most of
the non-strict implementations.  The two SML compilers (SML/NJ and
MLWorks) are particularly close in both time and heap usage.

Most of the non-strict compilers (Chalmers, FAST, Id, Stoffel, Yale
and Glasgow Haskell on less optimised code) are grouped in the 10--25
pseudoknot range.  These compilers typically offer around 75\% of the
performance of eager implementations such as SML/NJ or Gambit, or 50\%
of the performance of CMU Common Lisp.  Even so, this level of performance
has required the exploitation of strictness through unboxing and
similar optimisations.  Without these features, on the basis of the
Glasgow results (Section~\ref{sec:ghc}), performance can be estimated
as approximately 8 pseudoknots or just under a quarter of the typical
performance of a compiler for an eager language.  For these compilers
and this application, laziness therefore costs directly a factor of 3,
with a further 50\% probably attributable to the use of different
implementation techniques for predefined functions etc., which are
required due to the possibility of laziness.

The lowest pseudoknot values are found with the interpretive systems,
which are all less than 10 pseudoknots.  This is not particularly
surprising.  The interpreters (Caml Light, Poly/ML, NHC,
Trafola) which lie in the 1--10 pseudoknot range are, however,
significantly faster than their conventional brethren, which are
generally less than one pseudoknot.  Interpreters for strict languages
(CAML Light, Poly/ML, Trafola) do seem on the whole to be faster
than interpreters for non-strict languages (NHC, Gofer, RUFLI,
Miranda).

There are two remaining curiosities: the Erlang and RUFL compilers.
These do not fit clearly in the same groups as other similar
compilers. The low performance of the RUFL compiler may reflect its
relative immaturity, as well as the fact that no special priority has
been placed on floating-point performance.

The low performance recorded by the Erlang BEAM compiler reflects the
fact that Erlang is a programming language primarily intended for
designing robust, concurrent real time systems and a low priority has
been placed on floating-point performance.

The set of CAML compilers offers an interesting spectrum: Caml Gallium
is a slow compiler which produces fast code; Caml Light compiles
quickly, but is relatively slow; and Camloo is intermediate between
the two.

As might be expected, there is, broadly speaking, a strong
relationship between compilation time and execution speed.  Only the
Clean implementation offers both fast compilation and fast execution.
For the compiled systems there is also a very rough relationship
between execution speed and heap usage: faster implementations
use less heap.  There does not, however, seem to be any
correlation between non-strictness and heap usage.

The fact that Sisal is first-order may be significant, though this is
hard to judge since the only other first-order implementation (Erlang)
yields relatively poor performance.  Sisal is also the only
monomorphic language studied, so results here must also be
inconclusive.  Of the languages studied, Sisal is the only
one that was specifically designed for ``numeric'' rather than
``symbolic'' computations, and clearly the design works well
for this application.  Floating-point performance has traditionally
taken second-place in functional language implementations, so
we may hope that these results spur other compiler writers to
attempt to duplicate the Sisal results.

The provision of pattern-matching facilities does not appear to have
any effect on code performance: there are fast and slow
pattern-matching compilers.  The fastest compilers all use strong type
systems, which are known to assist compilation.  The dynamically typed
implementations are, however, intermediate in speed, and comparable
with the statically typed SML systems.

\section{Conclusions}
\label{sec:conclusions}
Over 20 compilers for lazy and strict functional languages have been
benchmarked using a single floating point intensive program. The C
version of the program spends 25\% of its time in the C library
trigonometric and square root routines. The problem at hand may thus be
typical only for geometric problems. The program should not be
construed as a typical numerical scientific application. However, the
program is useful as a benchmark, because the ``real'' work it does
(the trigonometric and floating point calculations) is so clearly
identifiable. Everything else the program does should be kept to a
minimum. Not all implementations of functional languages that have been
benchmarked are capable of realising this, but some are!

The compilation speed of most implementations (including the two C
compilers) could be improved significantly. Generating C as
intermediate code does not necessarily make the compiler slow, as
demonstrated by the performance of the Bigloo and Camloo compilers,
but generating fast C does often lead to high compilation times.

To achieve good performance from lazy implementations, it proved
necessary to annotate certain data structures that are often used as
strict. The performance of the pseudoknot program is not sensitive to
incorrectly placed strictness annotations. In general, lazy functional
programs are not so well behaved in this respect. Inserting annotations
is a fine art, as demonstrated by the efforts of the Glasgow team. To
make functional languages more useful than they are now, clearly more
effort should go into providing users with simple to use and effective
means of analysing and improving the performance of their programs.

Benchmarking a single program can lead to results which cannot easily
be generalised. Special care has been taken to make the comparison as
fair as possible: the pseudoknot program is not an essentially lazy
program or an essentially non-lazy program; the different
implementations use the same algorithm; most of the binaries were timed
on one and the same machine.

Storing floating point numbers as unboxed values has a significant
positive effect on the execution speed of the pseudoknot program. Most
compilers can deal adequately with unboxed single precision floating
point numbers. Few compilers can also implement unboxed double
precision numbers properly. There is thus a need for better methods of
dealing with double precision unboxed floats, although this problem
might become obsolete with the next generation of 64 bit machine
architectures.

There is no clear distinction between the runtime performance of eager
and lazy implementations when appropriate annotations are used: lazy
implementations have clearly come of age when it comes to implementing
largely strict applications, such as the pseudoknot program. The speed
of C can be approached by some implementations, but not without special
measures such as strictness annotations. On the Cray, the Sisal
version is faster than the C version.


\section*{Acknowledgements}
Mark Jones produced the Gofer version of the pseudoknot program.

Will Partain and Jim Mattson performed many of the experiments reported
here for the Glasgow Haskell compiler. The work at Glasgow is
supported by a SOED Research Fellowship from the Royal Society of
Edinburgh, and by the EPSRC AQUA grant.

The work at Nijmegen is supported by STW (Stichting voor de
Technische Wetenschappen, The Netherlands).

Jon-Dean Mountjoy performed some of the experiments for the RUFL
implementation.

The ID version of the pseudoknot program was the result of a group
effort. Credit goes to Jamey Hicks, R. Paul Johnson, Shail Aditya,
Yonald Chery and Andy Shaw.

Zhong Shao made several important changes to the SML/NJ
implementation, and Andrew Appel and David MacQueen provided general
support in the use of this system. David Tarditi performed several of
the SML/NJ experiments.

John T. Feo and Scott Denton of Lawrence Livermore National Laboratory
collaborated on the Sisal version of the pseudoknot program.

\bibliography{refs}
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\end{document}

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