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Optimizing Matrix Multiplication with a Classifier Learning System

Optimizing Matrix Multiplication with a Classifier Learning System. Xiaoming Li (presenter) María Jesús Garzarán University of Illinois at Urbana-Champaign. Tuning library for recursive matrix multiplication. Use cache-aware algorithms that take into account architectural features

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Optimizing Matrix Multiplication with a Classifier Learning System

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  1. Optimizing Matrix Multiplication with a Classifier Learning System Xiaoming Li (presenter) María Jesús Garzarán University of Illinois at Urbana-Champaign

  2. Tuning library for recursive matrix multiplication • Use cache-aware algorithms that take into account architectural features • Memory hierarchy • Register file, … • Take into account input characteristics • matrix sizes • The process of tuning is automatic.

  3. Recursive Matrix Partitioning • Previous approaches • Multiple recursive steps • Only divide by half A B

  4. Recursive Matrix Partitioning • Previous approaches: • Multiple recursive steps • Only divide by half A B Step 1:

  5. Recursive Matrix Partitioning • Previous approaches: • Multiple recursive steps • Only divide by half A B Step 2:

  6. Recursive Matrix Partitioning • Our approach is more general • No need to divide by half • May use a single step to reach the same partition • Faster and more general A B Step 1:

  7. Our approach • A general framework to describe a family of recursive matrix multiplication algorithms, where given the input dimensions of the matrices, we determine: • Number of partition levels • How to partition at each level • An intelligent search method based on a classifier learning system • Search for the best partitioning strategy in a huge search space

  8. Outline • Background • Partition Methods • Classifier Learning System • Experimental Results

  9. Recursive layout framework • Multiple levels of recursion • Takes into account the cache hierarchy

  10. Recursive layout framework • Multiple levels of recursion • Takes into account the cache hierarchy 2 1 4 3

  11. Recursive layout in our framework • Multiple levels of recursion • Takes into account the cache hierarchy

  12. Recursive layout framework • Multiple levels of recursion • Takes into account the cache hierarchy

  13. Recursive layout framework • Multiple levels of recursion • Takes into account the cache hierarchy 1 2 5 6 3 4 7 8 9 10 13 14 11 12 15 16

  14. Padding • Necessary when the partition factor is not a divisor of the matrix dimension. Divide by 3 2000

  15. Padding • Necessary when the partition factor is not a divisor of the matrix dimension. Divide by 3 2001 667

  16. Padding • Necessary when the partition factor is not a divisor of the matrix dimension. Divide by 4 2001 667

  17. Padding • Necessary when the partition factor is not a divisor of the matrix dimension. Divide by 4 2004 668

  18. Recursive layout in our framework • Multiple level recursion • Support cache hierarchy • Square tile  rectangular tile • Fit non-square matrixes

  19. Recursive layout in our framework • Multiple level recursion • Support cache hierarchy • Square tile  rectangular tile • Fit non-square matrixes 8 9

  20. Recursive layout in our framework • Multiple level recursion • Support cache hierarchy • Square tile  rectangular tile • Fit non-square matrixes 8 10 Padding

  21. Recursive layout in our framework • Multiple level recursion • Support cache hierarchy • Square tile  rectangular tile • Fit non-square matrixes 4 3

  22. Outline • Background • Partition Methods • Classifier Learning System • Experimental Results

  23. Two methods to partition matrices • Partition by Block (PB) • Specify the size of each tile • Example: • Dimensions (M,N,K) = (100, 100, 40) • Tile size (bm, bn, bk) = (50, 50, 20) Partition factors (pm, pn, pk) = (2,2,2) • Tiles need not to be square

  24. Two methods to partition matrices • Partition by Size (PS) • Specify the maximum size of the three tiles. • Maintain the ratios between dimensions constant • Example: • (M,N,K) = (100, 100,50) • Maximum tile size for M,N = 1250 (pm, pn, pk) = (2,2,1) • Generalization of the “divide-by-half” approach. • Tile size = 1/4 * matrix size

  25. Outline • Background • Partition Methods • Classifier Learning System • Experimental Results

  26. Classifier Learning System • Use the two partition primitives to determine how the input matrices are partitioned • Determine partition factors at each level f: (M,N,K)  (pmi,pni,pki), i=0,1,2 (only consider 3 levels) • The partition factors depend on the matrix size • Eg. The partitions factors of a (1000 x 1000) matrix should be different that those of a (50 x 1000) matrix. • The partition factors also depend on the architectural characteristics, like cache size.

  27. Determine the best partition factors • The search space is huge  exhaustive search is impossible • Our proposal: use a multi-step classifier learning system • Creates a table that given the matrix dimensions determines the partition factors

  28. Classifier Learning System • The result of the classifier learning system is a table with two columns • Column 1 (Pattern): A string of ‘0’, ‘1’, and ‘*’ that encodes the dimensions of the matrices • Column 2 (Action): Partition method for one step • Built using the “partition-by-block” and “partition-by-size” primitives with different parameters.

  29. Learn with Classifier System

  30. Learn with Classifier System 5 bits / dim

  31. Learn with Classifier System 24 16

  32. Learn with Classifier System 24 16

  33. Learn with Classifier System 12 8

  34. Learn with Classifier System 12 8

  35. Learn with Classifier System 12 8

  36. Learn with Classifier System 4 4

  37. How classifier learning algorithm works? • Change the table based on the feedback of performance and accuracy from previous runs. • Mutate the condition part of the table to adjust the range of matching matrix dimensions. • Mutate the action part to find the best partition method for the matching matrices.

  38. Outline • Background • Partition Methods • Classifier Learning System • Experimental Results

  39. Experimental Results • Experiments on three platforms • Sun UltraSparcIII • P4 Intel Xeon • Intel Itanium2 • Matrices of sizes from 1000 x 1000 to 5000 x 5000

  40. Algorithms • Classifier MMM: our approach • Include the overhead of copying in and out of recursive layout • ATLAS: Library generated by ATLAS using the search procedure without hand-written codes. • Has some type of blocking for L2 • L1: One level of tiling • tile size: the same that ATLAS for L1 • L2: Two levels of tiling • L1tile and L2tile: the same that ATLAS for L1

  41. Conclusion and Future Work • Preliminary results prove the effectiveness of our approach • Sun UltraSparcIII and Xeon: 18% and 5% improvement, respectively. • Itanium: -14% • Need to improve padding mechanism • Reduce the amount of padding • Avoid unnecessary computation on padding

  42. Thank you!

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