SCHEDULE: NOV 12-18, 2011

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Fast Implementation of DGEMM on Fermi GPU

SESSION: GPU Applications

EVENT TYPE: Paper

TIME: 10:30AM - 11:00AM

AUTHOR(S):Guangming Tan, Linchuan Li, Sean Triechler, Everett Phillips, Yungang Bao, Ninghui Sun

ROOM:TCC 303

ABSTRACT:
The GPU is offering more than an order of magnitude speedup of peak floating-point computing over conventional processors. In this paper we present a thorough experience on tuning double-precision matrix-matrix multiplication (DGEMM) on the Fermi GPU architecture. We choose an optimal algorithm with blocking in both shared memory and registers to satisfy the constraints of the Fermi memory hierarchy. Our optimization strategy is further guided by a performance modeling based on micro-architecture benchmarks. Our optimizations include software pipelining, use of vector memory operations, and instruction scheduling. Our best CUDA algorithm achieves comparable performance with the latest vendor supplied library: CUBLAS 3.2. We further improve upon this with an implementation in the native machine language, leading to a 20% increase in performance over CUBLAS. That is, the achieved peak performance (efficiency) is improved from 302Gflop/s (58%) to 362Gflop/s (70%).

Chair/Author Details:

Guangming Tan - Institute of Computing Technology

Linchuan Li - Institute of Computing Technology

Sean Triechler - NVIDIA

Everett Phillips - NVIDIA

Yungang Bao - Institute of Computing Technology

Ninghui Sun - Institute of Computing Technology

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The full paper can be found in the ACM Digital Library