IPDPS 2006

1. Algorithmic Skeletons for Stream Programming in Embedded Hetereogeneous Parallel Image Processing Applications IPDPS 2006 Wouter Caarls, Pieter Jonker, Henk Corporaal Quantitative Imaging Group, department of Imaging Science and Technology

2. Overview • Stream programming • Writing stream kernels • Algorithmic skeletons • Writing algorithmic skeletons • Skeleton merging • Results • Conclusion & Future work

3. Stream Programming • FIFO-connected kernels processing series of data elements • Well suited to signal processing applications • Explicit communication and task decomposition • Ideal for distributed-memory systems • Each data element processed (mostly) independently • Ideal for data-parallel systems such as SIMDs

4. Kernel Examples from Image Processing Increasing generality & Architectural requirements • Pixel processing (color space conversion) • Perfect match • Local neighborhood processing (convolution) • Requires 2D access • Recursive neighborhood processing (distance transform) • Regular data dependencies • Stack processing (region growing) • Irregular data dependencies

5. Writing Kernels • The language for writing kernels should be restricted • To allow efficient compilation to constrained architectures • But also general • So many different algorithms can be specified • Solution: a different language for each type of kernel • User selects the most restricted language that supports his kernel • Retargetability • Efficiency • Ease-of-use

6. Algorithmic skeletons* as kernel languages • An algorithmic skeleton captures a pattern of computation • Is conceptually a higher-order function, repetitively calling a kernel function with certain parameters • Iteration strategy may be parallel • Kernel parameters restrict dependencies • Provides the environment in which the kernel runs, and can be seen as a very restricted DSL *M. Cole. Algorithmic Skeletons: Structured Management of Parallel Computation, 1989

7. NeighborhoodToPixelOp() Average(in stream float i[-1..1] [-1..1], out stream float *o) { int ky, kx; float acc=0; for (ky=-1; ky <=1; ky++) for (kx=-1; kx <=1; kx++) acc += i[ky][kx]; *o = acc/9; } void Average(float **i, float **o) { for (int y=1; y < HEIGHT-1; y++) for (int x=1; x < WIDTH-1; x++) { float acc=0; acc += i[y-1][x-1]; acc += i[y-1][x ]; acc += i[y-1][x+1]; acc += i[y ][x-1]; acc += i[y ][x ]; acc += i[y ][x+1]; acc += i[y+1][x-1]; acc += i[y+1][x ]; acc += i[y+1][x+1]; o[y][x] = acc/9; } } Sequential neighborhood skeleton Kernel definition Resulting operation Skeleton

8. Skeleton tasks • Implement structure • Outer loop, border handling, buffering, parallel implementation • Just write C code • Transform kernel • Stream access, translation to target language • Term rewriting • How to combine in a single language? • Partial evaluation

9. Term rewriting (1) Input *o = acc/9; Rewrite Rule (applied topdown to all nodes) replace(`o`, `&o[y][x]`); Output o[y][x] = acc/9;

10. Term rewriting (2) Using Stratego* Input acc += i[ky][kx]; Rewrite Rule (applied topdown to all nodes) RelativeToAbsolute: |[ i[~e1][~e2] ]| -> |[ i[y + ~e1][x + ~e2] ]| Output acc += i[y+ky][x+kx]; *E. Visser. Stratego: A language for program transformation based on rewriting strategies, 2001

11. PEPCI (1)Rule composition and code generation in C stratego RelativeToAbsolute(code i, code body) { main = <topdown(RelativeToAbsolute’)>(body) RelativeToAbsolute’: |[ ~i[~e1][~e2] ]| -> |[ ~i[y + ~e1][x + ~e2] ]| } for (a=0; a < arguments; a++) if (args[a].type == ARG_STREAM_IN) body = RelativeToAbsolute(args[a].id, body); else if (args[a].type == ARG_STREAM_OUT) body = DerefToArrayIndex(args[a].id, body); for (y=1; y < HEIGHT-1; y++) for (x=1; x < WIDTH-1; x++) @body; Rule definition Rule composition Code generation

12. PEPCI (2)Combining rule composition and code generation • How to distinguish rule composition from code generation? for (a=0; a < arguments; a++) body = DerefToArrayIndex(args[a].id, body); for (x=0; x < stride; x++) @body; • Partial evaluation: evaluate only the parts of the program that are known. Output the rest • arguments is known, DerefToArrayIndex is known, args[a].id is known, body is known -> evaluate • stride is unknown -> output

13. double n, x=1; int ii, iterations=3; scanf(“%lf”, &n); for (ii=0; ii < iterations; ii++) x = (x + n/x)/2; printf(“sqrt(%f) = %f\n”, n, x); double n; double x; int ii; int iterations; x = 1; iterations = 3; scanf(“%lf”, &n); ii = 0; x = (1 + n/1)/2; ii = 1; x = (x + n/x)/2; ii = 2; x = (x + n/x)/2; ii = 3; printf(“sqrt(%f) = %f\n”, n, x); PEPCI (3)Partial evaluation by interpretation Input Output Symbol table double n double x int ii int iterations ? 1 ? 1 ? 3 ? 1 0 3 ? ? 0 3 ? ? 1 3 ? ? 2 3 ? ? 3 3

14. Kernelization overheads • Kernelizing an application impacts performance • Mapping • Scheduling • Buffers management • Lost ILP • Merge kernels • Extract static kernel sequences • Statically schedule at compile-time • Replace sequence with merged kernel

15. Skeleton merging • Skeletons are completely general functions • Cannot be properly analyzed or reasoned about • Restrict skeleton generality be using metaskeletons • Skeletons using the same metaskeleton can be merged • Merged operation still uses the original metaskeleton, and can be recursively merged

16. Example • Philips Inca+ smart camera • 640x480 sensor • XeTaL 16MHz, 320-way SIMD • TriMedia 180MHz, 5-issue VLIW • Ball detection • Filtering, Segmentation, Hough transform

17. Results Buffers, Scheduling, ILP ILP not fully recovered

18. Conclusion • Stream programming is a natural fit for running image processing applications on distributed-memory systems • Algorithmic Skeletons efficiently exploit data parallelism, by allowing the user to select the most restricted skeleton that supports his kernel • Extensible (new skeletons) • Retargetable (new skeleton implementations) • PEPCI effectively combines the necessities of efficiently implementing algorithmic skeletons • Term rewriting (by embedding Stratego) • Partial evaluation (to automatically separate rule composition and code generation)

19. Future Work • Better merging of kernels • Merge more efficiently • Merge different metaskeletons • Implement on a more general architecture • Implement more demanding applications • And more involved skeletons

20. End

21. Partial evaluation (2)Free optimizations • Loop unrolling • If the conditions are known, and the body isn’t • Function inlining • Aggressive constant folding • Including external “pure” functions

22. Kernel translation • SIMD processors are not programmed in C, but in parallel derivatives • Skeleton should translate kernel to target language • Extend PEPCI with C derivative syntax • Though only minimally interpreted

23. NeighbourhoodToPixelOp() sobelx(in stream unsigned char i[-1..1][-1..1], out stream int *o) { int x, y, temp; temp = 0; for (y=-1; y < 2; y++) for (x=-1; x < 2; x=x+2) temp = temp + x*i[y][x]; *o = temp; } static lmem _in2; static lmem _in1; { lmem temp; temp = (0)+((-1)*(_in2[-1 .. 0])); temp = (temp)+((1)*(_in2[1 .. 2])); temp = (temp)+((-1)*(_in1[-1 .. 0])); temp = (temp)+((1)*(_in1[1 .. 2])); temp = (temp)+((-1)*(larg0[-1 .. 0])); temp = (temp)+((1)*(larg0[1 .. 2])); larg1 = temp; } _in2 = _in1; _in1 = larg0; Example: local neighborhood operation in XTC

24. Stream program void main(int argc, char **argv) { STREAM a, b, c; int maxval, dummy, maxc; scInit(argc, argv); while (1) { capture(&a); interpolate(&a, &a); sobelx(&a, &b); sobely(&a, &c); magnitude(&b, &c, &a); direction(&b, &c, &b); mask(&b, &a, &a, scint(128)); hough(&a, &a); display(&a); imgMax(&a, scint(0), &maxval, scint(0), &dummy, scint(0), &maxc); _block(&maxc, &maxval); printf(“Ball found at %d with strength %d\n”, maxc, maxval); } return scExit(); }

25. Programming with algorithmic skeletons (1) PixelToPixelOp() binarize(in stream int *i, out stream int *o, in int *threshold) { *o = (*i > *threshold); } NeighbourhoodToPixelOp() average(in stream int i[-1..1][-1..1], out stream int *o) { int x, y; *o = 0; for (y=-1; y < 2; y++) for (x=-1; x < 2; x++) *o += i[y][x]; *o /= 9; }

26. Programming with algorithmic skeletons (2) StackOp(in stream int *init) propagate(in stream int *i[-1..1][-1..1], out stream int *o) { int x, y; for (y=-1; y < 2; y++) for (x=-1; x < 2; x++) if (i[y][x] && !*o) { *o = 1; push(y, x); } } AssocPixelReductionOp() max(in stream int *i, out int *res) { if (*i > *res) *res = *i; }

27. <=t + = >t <=t <=t + = >t + = >t Algorithmic Skeletons

28. Term rewriting (1) From code to abstract syntax tree acc += i [ ] ky [ ] kx ; Stat AssignPlus Id ArrayIndex “acc” ArrayIndex Id Id Id “kx” “i” “ky” Stat(AssignPlus(Id("acc"),ArrayIndex(ArrayIndex(Id("i"),Id("ky")),Id("kx"))))