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[diderot] Diff of /branches/cuda/TODO
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Diff of /branches/cuda/TODO

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revision 1156, Sun May 8 21:20:52 2011 UTC revision 1165, Mon May 9 22:02:04 2011 UTC
# Line 1  Line 1 
1  NOTE: GLK's approximate ranking of 5 most important tagged with  NOTE: GLK's approximate ranking of 8 most important tagged with
2  [GLK:1], [GLK:2], ...  [GLK:1], [GLK:2], ...
3    
4  ========================  ========================
5  SHORT TERM ============= (*needed* for streamlines & tractography)  SHORT TERM ============= (*needed* for streamlines & tractography)
6  ========================  ========================
7    
8  [GLK:1] Add sequence types (needed for evals & evecs)  [GLK:3] Add sequence types (needed for evals & evecs)
9      syntax      syntax
10          types: ty '{' INT '}'          types: ty '{' INT '}'
11          value construction: '{' e1 ',' … ',' en '}'          value construction: '{' e1 ',' … ',' en '}'
12          indexing: e '{' e '}'          indexing: e '{' e '}'
13  [GLK:1] evals & evecs for symmetric tensor[3,3] (requires sequences)  
14    [GLK:4] evals & evecs for symmetric tensor[2,2] and
15    tensor[3,3] (requires sequences)
16    
17  ability to emit/track/record variables into dynamically re-sized  ability to emit/track/record variables into dynamically re-sized
18  runtime buffer  runtime buffer
# Line 18  Line 20 
20  tensor fields: convolution on general tensor images  tensor fields: convolution on general tensor images
21    
22  ========================  ========================
23  SHORT-ISH TERM ========= (to make using Diderot less annoying/slow)  SHORT-ISH TERM ========= (to make using Diderot less annoying to
24  ========================  ========================  program in, and slow to execute)
25    
26    value-numbering optimization [DONE, but needs more testing]
27    
28    [GLK:1] Add a clamp function, which takes three arguments; either
29    three scalars:
30      clamp(lo, hi, x)  = max(lo, min(hi, x))
31    or three vectors of the same size:
32      clamp(lo, hi, [x,y])  = [max(lo[0], min(hi[0], x)),
33                               max(lo[1], min(hi[1], y))]
34    This would be useful in many current Diderot programs.
35    One question: clamp(x, lo, hi) is the argument order used in OpenCL
36    and other places, but clamp(lo, hi, x) is much more consistent with
37    lerp(lo, hi, x), hence GLK's preference
38    
39    [GLK:2] Proper handling of stabilize method
40    
41  value-numbering optimization  allow "*" to represent "modulate": per-component multiplication of
42    vectors, and vectors only (not tensors of order 2 or higher).  Once
43    sequences are implemented this should be removed: the operation is not
44    invariant WRT basis so it is not a legit vector computation.
45    
46  proper handling of stabilize method  implicit type promotion of integers to reals where reals are
47    required (e.g. not exponentiation "^")
48    
49  [GLK:2] Save Diderot output to nrrd, instead of "mip.txt"  [GLK:5] Save Diderot output to nrrd, instead of "mip.txt"
50    For grid of strands, save to similarly-shaped array    For grid of strands, save to similarly-shaped array
51    For list of strands, save to long 1-D (or 2-D for non-scalar output) list    For list of strands, save to long 1-D (or 2-D for non-scalar output) list
52    For ragged things (like tractography output), will need to save both    For ragged things (like tractography output), will need to save both
53      complete list of values, as well as list of start indices and lengths      complete list of values, as well as list of start indices and lengths
54      to index into complete list      to index into complete list
55    
56  [GLK:3] Use of Teem's "hest" command-line parser for getting  [GLK:6] Use of Teem's "hest" command-line parser for getting
57  any input variables that are not defined in the source file  any input variables that are not defined in the source file
58    
59  [GLK:4] ability to declare a field so that probe positions are  [GLK:7] ability to declare a field so that probe positions are
60  *always* "inside"; with various ways of mapping the known image values  *always* "inside"; with various ways of mapping the known image values
61  to non-existant index locations.  One possible syntax emphasizes that  to non-existant index locations.  One possible syntax emphasizes that
62  there is a index mapping function that logically precedes convolution:  there is a index mapping function that logically precedes convolution:
63    F = bspln3 ⊛ (img  clamp)    F = bspln3 ⊛ (img ◦ clamp)
64    F = bspln3 ⊛ (img ◦ repeat)    F = bspln3 ⊛ (img ◦ repeat)
65    F = bspln3 ⊛ (img ◦ mirror)    F = bspln3 ⊛ (img ◦ mirror)
66  where "◦" or "∘" is used to indicate function composition  where "◦" or "∘" is used to indicate function composition
67    
 Use ∇⊗ etc. syntax  
     syntax [DONE]  
     typechecking  
     IL and codegen  
   
 Add a clamp function, which takes three arguments; either three scalars:  
   clamp(x, minval, maxval)  = max(minval, min(maxval, x))  
 or three vectors of the same size:  
   clamp([x,y], minvec, maxvec)  = [max(minvec[0], min(maxvec[0], x)),  
                                    max(minvec[1], min(maxvec[1], y))]  
 This would be useful in many current Diderot programs.  
 One question: clamp(x, minval, maxval) is the argument order  
 used in OpenCL and other places, but clamp(minval, maxval, x)  
 would be more consistent with lerp(minout, maxout, x).  
   
68  Level of differentiability in field type should be statement about how  Level of differentiability in field type should be statement about how
69  much differentiation the program *needs*, rather than what the kernel  much differentiation the program *needs*, rather than what the kernel
70  *provides*.  The needed differentiability can be less than or equal to  *provides*.  The needed differentiability can be less than or equal to
71  the provided differentiability.  the provided differentiability.
72    
73    Use ∇⊗ etc. syntax
74        syntax [DONE]
75        typechecking
76        IL and codegen
77    
78  Add type aliases for color types  Add type aliases for color types
79      rgb = real{3}      rgb = real{3}
80      rgba = real{4}      rgba = real{4}
# Line 94  Line 105 
105    
106  support for Python interop and GUI  support for Python interop and GUI
107    
108    Allow integer exponentiation ("^2") to apply to square matrices,
109    to represent repeated matrix multiplication
110    
111  Alow X *= Y, X /= Y, X += Y, X -= Y to mean what they do in C,  Alow X *= Y, X /= Y, X += Y, X -= Y to mean what they do in C,
112  provided that X*Y, X/Y, X+Y, X-Y are already supported.  provided that X*Y, X/Y, X+Y, X-Y are already supported.
113  Nearly every Diderot program would be simplified by this.  Nearly every Diderot program would be simplified by this.
# Line 142  Line 156 
156  (but we should only duplicate over the live-range of the result of the  (but we should only duplicate over the live-range of the result of the
157  conditional.  conditional.
158    
159  [GLK:5] Want: non-trivial field expressions & functions:  [GLK:8] Want: non-trivial field expressions & functions.
160    scalar fields from scalar fields F and G:
161      field#0(2)[] X = (sin(F) + 1.0)/2;
162      field#0(2)[] X = F*G;
163    scalar field of vector field magnitude:
164    image(2)[2] Vimg = load(...);    image(2)[2] Vimg = load(...);
165    field#0(2)[] Vlen = |Vimg ⊛ bspln3|;    field#0(2)[] Vlen = |Vimg ⊛ bspln3|;
166  to get a scalar field of vector length, or  field of normalized vectors (for LIC and vector field feature extraction)
167      field#2(2)[2] F = ...
168      field#0(2)[2] V = normalize(F);
169    scalar field of gradient magnitude (for edge detection))
170    field#2(2)[] F = Fimg ⊛ bspln3;    field#2(2)[] F = Fimg ⊛ bspln3;
171    field#0(2)[] Gmag = |∇F|;    field#0(2)[] Gmag = |∇F|;
172  to get a scalar field of gradient magnitude, or  scalar field of squared gradient magnitude (simpler to differentiate):
173    field#2(2)[] F = Fimg ⊛ bspln3;    field#2(2)[] F = Fimg ⊛ bspln3;
174    field#0(2)[] Gmsq = ∇F•∇F;    field#0(2)[] Gmsq = ∇F•∇F;
175  to get a scalar field of squared gradient magnitude, which is simpler  There is value in having these, even if the differentiation of them is
176  to differentiate.  However, there is value in having these, even if  not supported (hence the indication of "field#0" for these above)
177  the differentiation of them is not supported (hence the indication  
178  of "field#0" for these above)  co- vs contra- index distinction
   
 Want: ability to apply "normalize" to a field itself, e.g.  
   field#0(2)[2] V = normalize(Vimg ⊛ ctmr);  
 so that V(x) = normalize((Vimg ⊛ ctmr)(x)).  
 Having this would simplify expression of standard LIC method, and  
 would also help express other vector field expressions that arise  
 in vector field feature exraction.  
179    
180  Permit fields composition, especially for warping images by a  Permit field composition:
 smooth field of deformation vectors  
181    field#2(3)[3] warp = bspln3 ⊛ warpData;    field#2(3)[3] warp = bspln3 ⊛ warpData;
182    field#2(3)[] F = bspln3 ⊛ img;    field#2(3)[] F = bspln3 ⊛ img;
183    field#2(3)[] Fwarp = F ◦ warp;    field#2(3)[] Fwarp = F ◦ warp;
184  So Fwarp(x) = F(warp(X)).  Chain rule can be used for differentation  So Fwarp(x) = F(warp(X)).  Chain rule can be used for differentation.
185    This will be instrumental for expressing non-rigid registration
186    methods (but those will require co-vs-contra index distinction)
187    
188  Allow the convolution to be specified either as a single 1D kernel  Allow the convolution to be specified either as a single 1D kernel
189  (as we have it now):  (as we have it now):
# Line 177  Line 192 
192    field#0(3)[] F = (bspln3 ⊗ bspln3 ⊗ tent) ⊛ img;    field#0(3)[] F = (bspln3 ⊗ bspln3 ⊗ tent) ⊛ img;
193  This is especially important for things like time-varying data, or  This is especially important for things like time-varying data, or
194  other multi-dimensional fields where one axis of the domain is very  other multi-dimensional fields where one axis of the domain is very
195  different from the rest.  What is very unclear is how, in such cases,  different from the rest, and hence must be treated separately when
196    it comes to convolution.  What is very unclear is how, in such cases,
197  we should notate the gradient, when we only want to differentiate with  we should notate the gradient, when we only want to differentiate with
198  respect to some of the axes.  respect to some subset of the axes.  One ambitious idea would be:
199      field#0(3)[] Ft = (bspln3 ⊗ bspln3 ⊗ tent) ⊛ img; // 2D time-varying field
200  co- vs contra- index distinction    field#0(2)[] F = lambda([x,y], Ft([x,y,42.0]))    // restriction to time=42.0
201      vec2 grad = ∇F([x,y]);                            // 2D gradient
202    
203  some indication of tensor symmetry  representation of tensor symmetry
204  (have to identify the group of index permutations that are symmetries)  (have to identify the group of index permutations that are symmetries)
205    
206  dot works on all tensors  dot works on all tensors

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