Benchmarking GPUs to tune dense linear algebra Export

@inproceedings{citeulike:3541127, abstract = {We present performance results for dense linear algebra using recent NVIDIA GPUs. Our matrix-matrix multiply routine (GEMM) runs up to 60\% faster than the vendor's implementation 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 \~{}540 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.}, address = {Piscataway, NJ, USA}, author = {Volkov, Vasily and Demmel, James W.}, booktitle = {SC '08: Proceedings of the 2008 ACM/IEEE conference on Supercomputing}, citeulike-article-id = {3541127}, citeulike-linkout-0 = {http://portal.acm.org/citation.cfm?id=1413370.1413402}, citeulike-linkout-1 = {http://dx.doi.org/10.1145/1413370.1413402}, doi = {10.1145/1413370.1413402}, isbn = {978-1-4244-2835-9}, keywords = {2009, 571r, cache, data, gpgpu, gpu, hpc, lu, parallel, performance, qu}, location = {Austin, Texas}, pages = {1--11}, posted-at = {2009-03-12 01:25:07}, priority = {0}, publisher = {IEEE Press}, title = {Benchmarking GPUs to tune dense linear algebra}, url = {http://dx.doi.org/10.1145/1413370.1413402}, year = {2008} }

TY - CONF ID - citeulike:3541127 L3 - citeulike-article-id:3541127 N2 - We present performance results for dense linear algebra using recent NVIDIA GPUs. Our matrix-matrix multiply routine (GEMM) runs up to 60% faster than the vendor's implementation 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 ~540 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. CY - Austin, Texas SN - 978-1-4244-2835-9 AD - Piscataway, NJ, USA EP - 11 TI - Benchmarking GPUs to tune dense linear algebra PB - IEEE Press T2 - SC '08: Proceedings of the 2008 ACM/IEEE conference on Supercomputing SP - 1 KW - 2009 KW - 571r KW - cache KW - data KW - gpgpu KW - gpu KW - hpc KW - lu KW - parallel KW - performance KW - qu AU - Volkov, Vasily AU - Demmel, James PY - 2008/// UR - http://dx.doi.org/10.1145/1413370.1413402 ER -