ELAPS框架：实验线性代数性能研究
The ELAPS Framework: Experimental Linear Algebra Performance Studies
AI总结：
ELAPS框架通过多平台开源环境实现高效的线性代数性能实验，支持通过Python脚本或GUI设计实验，分析缓存、算法参数等对性能的影响，适用于多种计算环境，帮助做出关键性能决策。
摘要：
最优利用计算资源需要大量的编码、调优和基准测试。为了提高开发人员在这些耗时任务中的生产力，我们引入了实验线性代数性能研究框架（ELAPS），这是一个多平台开源环境，用于快速且强大的线性代数内核、算法和库的性能实验。ELAPS允许用户构建实验以研究性能和效率如何随缓存、算法参数、问题规模和并行性等因素变化。实验可通过Python脚本或专用GUI设计，并在从笔记本电脑到集群、加速器和超级计算机的整个架构范围内运行。生成的实验报告提供各种指标和统计信息，可以数值和视觉方式分析。我们通过四个具体应用场景和同样数量的计算环境展示了ELAPS的使用，证明了其在支持关键性能决策方面的实用价值。
英文摘要：
Optimal use of computing resources requires extensive coding, tuning and benchmarking. To boost developer productivity in these time consuming tasks, we introduce the Experimental Linear Algebra Performance Studies framework (ELAPS), a multi-platform open source environment for fast yet powerful performance experimentation with dense linear algebra kernels, algorithms, and libraries. ELAPS allows users to construct experiments to investigate how performance and efficiency vary depending on factors such as caching, algorithmic parameters, problem size, and parallelism. Experiments are designed either through Python scripts or a specialized GUI, and run on the whole spectrum of architectures, ranging from laptops to clusters, accelerators, and supercomputers. The resulting experiment reports provide various metrics and statistics that can be analyzed both numerically and visually. We demonstrate the use of ELAPS in four concrete application scenarios and in as many computing environments, illustrating its practical value in supporting critical performance decisions.
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