Automatic Performance Tuning of Sparse Matrix Kernels

Richard Vuduc, Shoaib Kamil1, Rajesh Nishtala2, Benjamin Lee3, and Attila Gyulassy4
(Professors James W. Demmel and Katherine A. Yelick)
(DOE) DE-FG03-94ER25219, (DOE) DE-FC03-98ER25351, and (LLNL/DOE) W-7405-ENG-48

The overall performance in a variety of scientific computing and information retrieval applications is dominated by a few computational kernels. One important class of such operations are sparse matrix kernels--computations with matrices having relatively few non-zero entries. Performance tuning of sparse kernels is a particularly tedious and time-consuming task because performance is a complicated function of the kernel, machine, and non-zero structure of the matrix: for every machine, a user must carefully choose a data structure and implementation (code and transformations) that minimize the matrix storage while achieving the best possible performance.

The goal of this research project is to generate implementations of particular sparse kernels tuned to a given matrix and machine. Our work builds on Sparsity [1], an early successful prototype for sparse matrix-vector multiply (y = A*x, where A is a sparse matrix and x, y, are dense vectors), in the following ways:

This research is being conducted by members of the Berkeley Benchmarking and OPtimization (BeBOP) project [5].

E.-J. Im, "Optimizing the Performance of Sparse Matrix-Vector Multiplication," PhD thesis, UC Berkeley, May 2000.
R. Vuduc, J. Demmel, K. Yelick, et al., "Performance Optimizations and Bounds for Sparse-Matrix Vector Multiply," Supercomputing, November 2002.
R. Vuduc, S. Kamil, et al., "Automatic Performance Tuning and Analysis of Sparse Triangular Solve," Int. Conf. Supercomputing, Workshop on Performance Optimization of High-level Languages and Libraries, June 2002.
BLAS Technical Forum: blas/blast-forum.
The BeBOP Homepage: http://www.cs.berkeley. edu/~richie/bebop.
1Undergraduate (EECS)
2Undergraduate (EECS)
3Undergraduate (EECS)
4Undergraduate (EECS)

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