An Empirical Roofline Methodology for Quantitatively Assessing Performance Portability
An Empirical Roofline Methodology for Quantitatively Assessing Performance Portability
复制标题
定量评估性能可移植性的实证 Roofline 方法
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Samuel Williams
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文献类型：
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作者：
Charlene Yang;Rahulkumar Gayatri;T. Kurth;P. Basu;Z. Ronaghi;A. Adetokunbo;B. Friesen;B. Cook;D. Doerfler;L. Oliker;J. Deslippe;Samuel Williams
系统和节点架构不断多样化，以更好地平衡特定计算工作负载的节点计算、内存容量、内存带宽、互连带宽、功耗和成本。对于许多应用程序开发人员来说，实现性能可移植性（有效利用多种架构的功能）是一个期望的目标。不幸的是，每个节点性能的显着差异加上机器平衡的差异可能会导致开发人员无法确定他们是否已获得性能可移植性或只是编写了可移植代码。 Roofline 模型提供了一种定量评估给定应用程序利用目标平台计算能力的情况的方法。在本文中，我们扩展了 Roofline 模型，使其 1）根据经验捕获了一组更现实的 CPU 和 GPU 性能界限，2）在计算 FLOP 时考虑不同浮点指令的真实成本，3）纳入不同内存访问模式的影响，4）通过代码性能和 Roofline 上限的适当配对，促进性能可移植性分析。
System and node architectures continue to diversify to better balance on-node computation, memory capacity, memory bandwidth, interconnect bandwidth, power, and cost for specific computational workloads. For many application developers, achieving performance portability (effectively exploiting the capabilities of multiple architectures) is a desired goal. Unfortunately, dramatically different per-node performance coupled with differences in machine balance can lead to developers being unable to determine whether they have attained performance portability or simply written portable code. The Roofline model provides a means of quantitatively assessing how well a given application makes use of a target platform’s computational capabilities. In this paper, we extend the Roofline model so that it 1) empirically captures a more realistic set of performance bounds for CPUs and GPUs, 2) factors in the true cost of different floating-point instructions when counting FLOPs, 3) incorporates the effects of different memory access patterns, and 4) with appropriate pairing of code performance and Roofline ceiling, facilitates the performance portability analysis.