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NOTE: GLK's approximate ranking of 8 most important tagged with [GLK:1], [GLK:2], ... ======================== SHORT TERM ============= (*needed* for streamlines & tractography) ======================== [GLK:3] Add sequence types (needed for evals & evecs) syntax types: ty '{' INT '}' value construction: '{' e1 ',' … ',' en '}' indexing: e '{' e '}' [GLK:4] evals & evecs for symmetric tensor[2,2] and tensor[3,3] (requires sequences) ability to emit/track/record variables into dynamically re-sized runtime buffer tensor fields: convolution on general tensor images ======================== SHORT-ISH TERM ========= (to make using Diderot less annoying to ======================== program in, and slow to execute) value-numbering optimization [GLK:1] Add a clamp function, which takes three arguments; either three scalars: clamp(lo, hi, x) = max(lo, min(hi, x)) or three vectors of the same size: clamp(lo, hi, [x,y]) = [max(lo[0], min(hi[0], x)), max(lo[1], min(hi[1], y))] This would be useful in many current Diderot programs. One question: clamp(x, lo, hi) is the argument order used in OpenCL and other places, but clamp(lo, hi, x) is much more consistent with lerp(lo, hi, x), hence GLK's preference [GLK:2] Proper handling of stabilize method allow "*" to represent "modulate": per-component multiplication of vectors, and vectors only (not tensors of order 2 or higher). Once sequences are implemented this should be removed: the operation is not invariant WRT basis so it is not a legit vector computation. implicit type promotion of integers to reals where reals are required (e.g. not exponentiation "^") [GLK:5] Save Diderot output to nrrd, instead of "mip.txt" For grid of strands, save to similarly-shaped array For list of strands, save to long 1-D (or 2-D for non-scalar output) list For ragged things (like tractography output), will need to save both complete list of values, as well as list of start indices and lengths to index into complete list [GLK:6] Use of Teem's "hest" command-line parser for getting any input variables that are not defined in the source file [GLK:7] ability to declare a field so that probe positions are *always* "inside"; with various ways of mapping the known image values to non-existant index locations. One possible syntax emphasizes that there is a index mapping function that logically precedes convolution: F = bspln3 ⊛ (img ◦ clamp) F = bspln3 ⊛ (img ◦ repeat) F = bspln3 ⊛ (img ◦ mirror) where "◦" or "∘" is used to indicate function composition Level of differentiability in field type should be statement about how much differentiation the program *needs*, rather than what the kernel *provides*. The needed differentiability can be less than or equal to the provided differentiability. Use ∇⊗ etc. syntax syntax [DONE] typechecking IL and codegen Add type aliases for color types rgb = real{3} rgba = real{4} ============================== MEDIUM TERM ================== (*needed* for particles) ============================== run-time birth of strands "initially" supports lists "initially" supports lists of positions output from different initalization Diderot program Communication between strands: they have to be able to learn each other's state (at the previous iteration). Early version of this can have the network of neighbors be completely static (for running one strand/pixel image computations). Later version with strands moving through the domain will require some spatial data structure to optimize discovery of neighbors. ============================ MEDIUM-ISH TERM ============ (to make Diderot more useful/effective) ============================ Python/ctypes interface to run-time support for Python interop and GUI Allow integer exponentiation ("^2") to apply to square matrices, to represent repeated matrix multiplication Alow X *= Y, X /= Y, X += Y, X -= Y to mean what they do in C, provided that X*Y, X/Y, X+Y, X-Y are already supported. Nearly every Diderot program would be simplified by this. Put small 1-D and 2-D fields, when reconstructed specifically by tent and when differentiation is not needed, into faster texture buffers. test/illust-vr.diderot is good example of program that uses multiple such 1-D fields basically as lookup-table-based function evaluation expand trace in mid to low translation extend norm (|exp|) to all tensor types [DONE for vectors and matrices] determinant ("det") for tensor[3,3] add ":" for tensor dot product (contracts out two indices instead of one like •), valid for all pairs of tensors with at least two indices test/uninit.diderot: documents need for better compiler error messages when output variables are not initialized; the current messages are very cryptic want: warnings when "D" (reserved for differentiation) is declared as a variable name (get confusing error messages now) ============================== LONG TERM ==================== (make Diderot more interesting/attractive from ============================== a research standpoint) IL support for higher-order tensor values (matrices, etc). tensor construction [DONE] tensor indexing [DONE] tensor slicing verify that hessians work correctly [DONE] Better handling of variables that determines the scope of a variable based on its actual use, instead of where the user defined it. So, for example, we should lift strand-invariant variables to global scope. Also prune out useless variables, which should include field variables after the translation to mid-il. test/vr-kcomp2.diderot: Add support for code like (F1 if x else F2)@pos This will require duplication of the continuation of the conditional (but we should only duplicate over the live-range of the result of the conditional. [GLK:8] Want: non-trivial field expressions & functions. scalar fields from scalar fields F and G: field#0(2)[] X = (sin(F) + 1.0)/2; field#0(2)[] X = F*G; scalar field of vector field magnitude: image(2)[2] Vimg = load(...); field#0(2)[] Vlen = |Vimg ⊛ bspln3|; field of normalized vectors (for LIC and vector field feature extraction) field#2(2)[2] F = ... field#0(2)[2] V = normalize(F); scalar field of gradient magnitude (for edge detection)) field#2(2)[] F = Fimg ⊛ bspln3; field#0(2)[] Gmag = |∇F|; scalar field of squared gradient magnitude (simpler to differentiate): field#2(2)[] F = Fimg ⊛ bspln3; field#0(2)[] Gmsq = ∇F•∇F; There is value in having these, even if the differentiation of them is not supported (hence the indication of "field#0" for these above) co- vs contra- index distinction Permit field composition: field#2(3)[3] warp = bspln3 ⊛ warpData; field#2(3)[] F = bspln3 ⊛ img; field#2(3)[] Fwarp = F ◦ warp; So Fwarp(x) = F(warp(X)). Chain rule can be used for differentation. This will be instrumental for expressing non-rigid registration methods (but those will require co-vs-contra index distinction) Allow the convolution to be specified either as a single 1D kernel (as we have it now): field#2(3)[] F = bspln3 ⊛ img; or, as a tensor product of kernels, one for each axis, e.g. field#0(3)[] F = (bspln3 ⊗ bspln3 ⊗ tent) ⊛ img; This is especially important for things like time-varying data, or other multi-dimensional fields where one axis of the domain is very different from the rest, and hence must be treated separately when it comes to convolution. What is very unclear is how, in such cases, we should notate the gradient, when we only want to differentiate with respect to some subset of the axes. One ambitious idea would be: field#0(3)[] Ft = (bspln3 ⊗ bspln3 ⊗ tent) ⊛ img; // 2D time-varying field field#0(2)[] F = lambda([x,y], Ft([x,y,42.0])) // restriction to time=42.0 vec2 grad = ∇F([x,y]); // 2D gradient representation of tensor symmetry (have to identify the group of index permutations that are symmetries) dot works on all tensors outer works on all tensors Einstein summation notation "tensor comprehension" (like list comprehension) ====================== BUGS ================= ====================== test/zslice2.diderot: // HEY (bug) bspln5 leads to problems ... // uncaught exception Size [size] // raised at c-target/c-target.sml:47.15-47.19 //field#4(3)[] F = img ⊛ bspln5;

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