Authors:
We present performance results for dense linear algebra using the
8-series NVIDIA GPUs. Our GEMM routine runs 60% faster than the vendor
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 ~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 register blocking to optimize GEMM and heterogeneity of the
system (compute 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.
The full paper can be found in the
IEEE Computer Society
archive
and
ACM Digital Library
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Materials > Research Papers >
Benchmarking GPUs to Tune Dense Linear Algebraposted Dec 18, 2008 12:58 AM by Nicolas Pinto [ updated Dec 31, 2008 10:05 AM ] |