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Dense Linear Algebra (Data Distributions) Sathish Vadhiyar.

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Presentation on theme: "Dense Linear Algebra (Data Distributions) Sathish Vadhiyar."— Presentation transcript:

1 Dense Linear Algebra (Data Distributions) Sathish Vadhiyar

2 Gaussian Elimination - Review Version 1 for each column i zero it out below the diagonal by adding multiples of row i to later rows for i= 1 to n-1 for each row j below row i for j = i+1 to n add a multiple of row i to row j for k = i to n A(j, k) = A(j, k) – A(j, i)/A(i, i) * A(i, k) i i i,i X x j k

3 Gaussian Elimination - Review Version 2 – Remove A(j, i)/A(i, i) from inner loop for each column i zero it out below the diagonal by adding multiples of row i to later rows for i= 1 to n-1 for each row j below row i for j = i+1 to n m = A(j, i) / A(i, i) for k = i to n A(j, k) = A(j, k) – m* A(i, k) i i i,i X x j k

4 Gaussian Elimination - Review Version 3 – Dont compute what we already know for each column i zero it out below the diagonal by adding multiples of row i to later rows for i= 1 to n-1 for each row j below row i for j = i+1 to n m = A(j, i) / A(i, i) for k = i+1 to n A(j, k) = A(j, k) – m* A(i, k) i i i,i X x j k

5 Gaussian Elimination - Review Version 4 – Store multipliers m below diagonals for each column i zero it out below the diagonal by adding multiples of row i to later rows for i= 1 to n-1 for each row j below row i for j = i+1 to n A(j, i) = A(j, i) / A(i, i) for k = i+1 to n A(j, k) = A(j, k) – A(j, i)* A(i, k) i i i,i X x j k

6 GE - Runtime Divisions Multiplications / subtractions Total … (n-1) = n 2 /2 (approx.) …. (n-1) 2 = n 3/ 3 – n 2 /2 2n 3 /3

7 Parallel GE 1 st step – 1-D block partitioning along blocks of n columns by p processors i i i,i X x j k

8 1D block partitioning - Steps 1. Divisions 2. Broadcast 3. Multiplications and Subtractions Runtime: n 2 /2 xlog(p) + ylog(p-1) + zlog(p-3) + … log1 < n 2 logp (n-1)n/p + (n-2)n/p + …. 1x1 = n 3 /p (approx.) < n 2 /2 +n 2 logp + n 3 /p

9 2-D block To speedup the divisions i i i,i X x j k P Q

10 2D block partitioning - Steps 1. Broadcast of (k,k) 2. Divisions 3. Broadcast of multipliers logQ n 2 /Q (approx.) xlog(P) + ylog(P-1) + zlog(P-2) + …. = n 2 /Q logP 4. Multiplications and subtractions n 3 /PQ (approx.)

11 Problem with block partitioning for GE Once a block is finished, the corresponding processor remains idle for the rest of the execution Solution? -

12 Onto cyclic The block partitioning algorithms waste processor cycles. No load balancing throughout the algorithm. Onto cyclic cyclic 1-D block-cyclic2-D block-cyclic Load balance Load balance, block operations, but column factorization bottleneck Has everything

13 Block cyclic Having blocks in a processor can lead to block-based operations (block matrix multiply etc.) Block based operations lead to high performance

14 GE: Miscellaneous GE with Partial Pivoting 1D block-column partitioning: which is better? Column or row pivoting 2D block partitioning: Can restrict the pivot search to limited number of columns Column pivoting does not involve any extra steps since pivot search and exchange are done locally on each processor. O(n-i-1) The exchange information is passed to the other processes by piggybacking with the multiplier information Row pivoting Involves distributed search and exchange – O(n/P)+O(logP)

15 Triangular Solve – Unit Upper triangular matrix Sequential Complexity - Complexity of parallel algorithm with 2D block partitioning (P 0.5 *P 0.5 ) O(n 2 ) O(n 2 )/P 0.5 Thus (parallel GE / parallel TS) < (sequential GE / sequential TS) Overall (GE+TS) – O(n 3 /P)

16 Dense LU on GPUs

17 LU for Hybrid Multicore + GPU Systems (Tomov et al., Parallel Computing, 2010) Assume the CPU host has 8 cores Assume NxN matrix; Divided into blocks of size NB; Split such that the first N-7NB columns are on GPU memory Last 7NB on the host

18 Load Splitting for Hybrid LU

19 Steps Current panel is downloaded to CPU; the dark blue part in the figure Panel factored on CPU and result sent to GPU to update trailing sub-matrix; the red part; GPU updates the first NB columns of the trailing submatrix Updated panel sent to CPU; Asynchronously factored on CPU while the GPU updates the rest of the trailing submatrix The rest of the 7 host cores update the last 7 NB host columns

20 Dense Cholesky on GPUs

21 Cholesky Factorization Symmetric positive definite matrix A A = LL T Similar to Gaussian elimination; succession of block column factorizations followed by updates of the trailing submatrix Can be parallelized using fork-join model similar to parallel GE Matrix-matrix multiplications in parallel (fork) and synchronization needed for next block column factorization (join)

22 Heterogeneous Tile Algorithms (Song et al., ICS 2012) Divide a matrix into a set of small and large tiles Small tiles for CPU, and large tiles for GPUs At the top level, divide the matrix into large square tiles of size B x B Then subdivide each top level tile into a number of small rectangular tiles of size Bxb and a remaining tile

23 Heterogeneous Tile Cholesky Factorization Factor the first tile to solve L(1,1) Apply L(1,1) to update its right A(1,2)

24 Heterogeneous Tile Cholesky Factorization Factor the two tiles below L(1,1) Update all tiles on the right of the first tile column

25 Heterogeneous Tile Cholesky Factorization At the second iteration, repeat the above steps on the trailing submatrix starting from the second tile column

26 Two Level Block-Cyclic Distribution Divide a matrix A into p x (s.p) rectangular tiles discussed earlier i.e., first divided into pxp large tiles, then partition each large tile into s tiles On a hybrid CPU and GPU machine, allocate the tiles to the host and a number of P GPUs in a 1-D block-cyclic way Those columns whose indices are multiples of s are mapped to the P GPUs in a cyclic way, and remaining columns go to all CPU cores in the host

27 Two Level Block-Cyclic Distribution - Example

28 Tuning Tile Size Depends on relative performance of CPU and GPU cores For example, to determine the tile width, Bh for CPU:

29 References E. Agullo, C. Augonnet, J. Dongarra, H. Ltaief, R. Namyst, S. Thibault, S. Tomov, and W. m. Hu. Faster, Cheaper, Better: a Hybridization Methodology to Develop Linear Algebra Software for GPUs. GPU Computing Gems, 2, F. Song, S. Tomov, and J. Dongarra. Enabling and Scaling Matrix Computations on Heterogeneous Multi-core and Multi-GPU Systems. In In the Proceedings of IEEE/ACM Supercomputing Conference (SC), pages 365{376, 2012.


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