We present performance results for dense linear algebra using the 8-series NVIDIA GPUs. Our matrix-matrix multiply routine (GEMM) runs 60% faster than the vendor implementation in CUBLAS 1.1 and approaches the peak of hardware capabilities. Our LU, QR and Cholesky factorizations achieve up to 80-90% of the peak GEMM rate. Our parallel LU running on two GPUs achieves up to ~300 Gflop/s. These results are accomplished by challenging the accepted view of the GPU architecture and programming guidelines. We argue that modern GPUs should be viewed as multithreaded multicore vector units. We exploit blocking similarly to vector computers and heterogeneity of the system by computing both on GPU and CPU. This study includes detailed benchmarking of the GPU memory system that reveals sizes and latencies of caches and TLB. We present a couple of algorithmic optimizations aimed at increasing parallelism and regularity in the problem that provide us with slightly higher performance.
Title
LU, QR and Cholesky Factorizations using Vector Capabilities of GPUs
Published
EECS Department, University of California, University of California at Berkeley, Berkeley, California, May 13, 2008
Full Collection Name
Electrical Engineering & Computer Sciences Technical Reports
Other Identifiers
EECS-2008-49
Type
Text
Extent
13 p
Archive
The Engineering Library
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