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EGH
2004
Springer

Understanding the efficiency of GPU algorithms for matrix-matrix multiplication

13 years 7 months ago
Understanding the efficiency of GPU algorithms for matrix-matrix multiplication
Utilizing graphics hardware for general purpose numerical computations has become a topic of considerable interest. The implementation of streaming algorithms, typified by highly parallel computations with little reuse of input data, has been widely explored on GPUs. We relax the streaming model's constraint on input reuse and perform an in-depth analysis of dense matrix-matrix multiplication, which reuses each element of input matrices O(n) times. Its regular data access pattern and highly parallel computational requirements suggest matrix-matrix multiplication as an obvious candidate for efficient evaluation on GPUs but, surprisingly we find even nearoptimal GPU implementations are pronouncedly less efficient than current cache-aware CPU approaches. We find the key cause of this inefficiency is that the GPU can fetch less data and yet execute more arithmetic operations per clock than the CPU when both are operating out of their closest caches. The lack of high bandwidth access ...
Kayvon Fatahalian, Jeremy Sugerman, Pat Hanrahan
Added 20 Aug 2010
Updated 20 Aug 2010
Type Conference
Year 2004
Where EGH
Authors Kayvon Fatahalian, Jeremy Sugerman, Pat Hanrahan
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