一口气七个:可扩展矩阵算法的缓存无关范式的结果

Michael D. Adams, David S. Wise
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引用次数: 20

摘要

提出了块递归矩阵算法的一个蓬勃发展的范例,它立即获得了出色的性能,通过时间、TLB缺失、L1缺失、L2缺失、磁盘分页、分布式处理器上的扩展以及多平台的可移植性来衡量。它提供了一种哲学和工具,允许程序员以不可见的方式处理内存层次结构,从L1和L2到TLB、分页和处理器间通信。它们一起使用,提供了一种无关缓存的编程风格。本文给出了一些图表来支持这些主张,这些图表是通过C语言中直接从范式中使用几个内在调用制作的Cholesky分解的实现。本文的结果侧重于底层性能,包括利用硬件和编译器优化的新的Morton-hybrid表示。特别是,这段代码击败了英特尔的矩阵内核库和AMD的核心数学库,在L1失误时输了一点,而在tlb失误时决定性地赢了。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Seven at one stroke: results from a cache-oblivious paradigm for scalable matrix algorithms
A blossoming paradigm for block-recursive matrix algorithms is presented that, at once, attains excellent performance measured by• time• TLB misses• L1 misses• L2 misses• paging to disk• scaling on distributed processors, and• portability to multiple platforms.It provides a philosophy and tools that allow the programmer to deal with the memory hierarchy invisibly, from L1 and L2 to TLB, paging, and interprocessor communication. Used together, they provide a cache-oblivious style of programming.Plots are presented to support these claims on an implementation of Cholesky factorization crafted directly from the paradigm in C with a few intrinsic calls. The results in this paper focus on low-level performance, including the new Morton-hybrid representation to take advantage of hardware and compiler optimizations. In particular, this code beats Intel's Matrix Kernel Library and matches AMD's Core Math Library, losing a bit on L1 misses while winning decisively on TLB-misses.
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