NDLI: Understanding the efficiency of GPU algorithms for matrix-matrix multiplication

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Understanding the efficiency of GPU algorithms for matrix-matrix multiplication

Content Provider	ACM Digital Library
Author	Fatahalian, K. Sugerman, J. Hanrahan, P.
Abstract	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 near-optimal 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 to cached data will impair the performance of GPU implementations of any computation featuring significant input reuse.
Starting Page	133
Ending Page	137
Page Count	5
File Format	PDF
ISBN	3905673150
ISSN	17273471
DOI	10.1145/1058129.1058148
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2004-08-29
Publisher Place	New York
Access Restriction	Subscribed
Content Type	Text
Resource Type	Article

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