The Cache Performance and Optimizations of Blocked Algorithms

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The Cache Performance and Optimizations of Blocked Algorithms (1991)

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by Monica S. Lam , Edward E. Rothberg , Michael E. Wolf

Venue:	In Proceedings of the Fourth International Conference on Architectural Support for Programming Languages and Operating Systems
Citations:	571 - 5 self

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@INPROCEEDINGS{Lam91thecache,
    author = {Monica S. Lam and Edward E. Rothberg and Michael E. Wolf},
    title = {The Cache Performance and Optimizations of Blocked Algorithms},
    booktitle = {In Proceedings of the Fourth International Conference on Architectural Support for Programming Languages and Operating Systems},
    year = {1991},
    pages = {63--74}
}

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Abstract

Blocking is a well-known optimization technique for improving the effectiveness of memory hierarchies. Instead of operating on entire rows or columns of an array, blocked algorithms operate on submatrices or blocks, so that data loaded into the faster levels of the memory hierarchy are reused. This paper presents cache performance data for blocked programs and evaluates several optimizations to improve this performance. The data is obtained by a theoretical model of data conflicts in the cache, which has been validated by large amounts of simulation. We show that the degree of cache interference is highly sensitive to the stride of data accesses and the size of the blocks, and can cause wide variations in machine performance for different matrix sizes. The conventional wisdom of trying to use the entire cache, or even a fixed fraction of the cache, is incorrect. If a fixed block size is used for a given cache size, the block size that minimizes the expected number of cache misses is very small. Tailoring the block size according to the matrix size and cache parameters can improve the average performance and reduce the variance in performance for different matrix sizes. Finally, whenever possible, it is beneficial to copy non-contiguous reused data into consecutive locations. 1

Keyphrases

blocked algorithm cache performance block size different matrix size memory hierarchy blocked program data conflict cache size fixed block size theoretical model entire row cache parameter consecutive location well-known optimization technique average performance matrix size cache performance data entire cache faster level several optimization algorithm operate machine performance conventional wisdom cache interference wide variation cache miss fixed fraction expected number data access large amount

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