SHF: Small: Bench-testing Environment for Automated Software Tuning (BEAST)

在高性能科学计算领域，大量使用加速器技术的混合处理器的迅速出现，如图形处理单元（gpu）或英特尔Xeon Phi（又名多集成核心，MIC），给计算科学家带来了关键的新挑战。他们的研究应用通常依赖于计算内核（即科学计算的一个或多个基本模式的软件实现），这些内核针对速度进行了优化。这类程序的大部分计算时间都花在执行一个或多个内核上，长期的经验告诉开发人员，针对给定处理器的体系结构调整内核对于在单个计算节点级别上实现出色的性能是绝对必要的。由于科学家希望在拥有数千个节点的超级计算机上运行这些应用程序，因此节点级别的高性能对于整个应用程序的高生产率至关重要。不幸的是，对于绝大多数计算内核来说，性能调优的三种经典方法是什么？编译器驱动的代码转换，低级手工编程，还是经验自动调优？一直都很困难，往往产生喜忧参半的结果；新兴的混合处理器时代使得这三种技术更加低效。用于自动软件调优的台架测试环境（BEAST）为解决这一重要问题做出了实质性的贡献。BEAST创建了一个框架，用于探索和优化混合处理器上计算内核的性能，该框架1)适用于各种计算内核，2)（半）自动地在各种混合处理器架构上生成更好的性能实现，以及3)使开发人员更深入地了解为什么给定的内核/处理器组合具有它们所具有的性能概况。为了实现这一三重目标，它以一种全新的方式应用了用于传统应用程序基准测试的模型：它将抽象内核规范和相应的验证测试（类似于标准基准测试）与自动化测试引擎、数据分析和机器学习工具（称为BEAST工作台）相结合。该项目使用一种新的方法来指定与语言无关的代码模板和一个BEAST原型工作台，为各种计算内核探索了可选的调优方法和策略。在这个项目下进行的实验预计将表明，BEAST框架可以显着提高许多对科学计算至关重要的计算内核的性能。由于这个软件和使用它的技术被广泛地提供给科学和工程社区，它们将有助于确保为许多领域和许多类型的混合处理器及时交付性能优化的内核，使得BEAST bench-tuning软件基础设施的影响确实非常广泛。科学家和工程师，跨越大量的智力、经济和社会重要领域，将能够迅速调整其应用程序中的底层内核，以适应最新平台的特点，从而迅速获得每一代加速器技术的生产力优势。

In the world of high-performance scientific computing, the rapid emergence of hybrid processors that make heavy use of accelerator technologies, such as Graphics Processing Units (GPUs) or the Intel Xeon Phi (a.k.a., Many Integrated Cores, MIC), raises critical new challenges for computational scientists. Their research applications typically depend on computational kernels (i.e., software implementations of one or more of the basic patterns of scientific computing) that are optimized for speed. Such programs spend most of their computing time executing one or more of these kernels, and long experience has taught developers that tuning their kernels for the architecture of a given processor is absolutely essential to achieving excellent performance at the level of the individual computing node. Since scientists want to run these applications on supercomputers with thousands of such nodes, high performance at the node level is essential to high productivity for the application at large. Unfortunately, for the vast majority of computational kernels, the three classic approaches to performance tuning?compiler-driven code transformations, low-level manual programming, or empirical autotuning?have always been very difficult, often producing mixed results; and the emerging era of hybrid processors makes all three techniques less effective still. The Bench-testing Environment for Automated Software Tuning (BEAST) makes a substantial contribution to solving this important problem. BEAST creates a framework for exploring and optimizing the performance of computational kernels on hybrid processors that 1) applies to a diverse range of computational kernels, 2) (semi)automatically generates better performing implementations on various hybrid processor architectures, and 3) increases developer insight into why given kernel/processor combinations have the performance profiles they do. To achieve this three-fold goal, it applies the model used for traditional application benchmarking in a completely novel way: it combines an abstract kernel specification and corresponding verification test, similar to standard benchmarking, with an automated testing engine and data analysis and machine learning tools, called the BEAST workbench. Using a new method for specifying language-neutral code stencils and a prototype BEAST workbench, the project explores alternative tuning methods and strategies for a diverse range of computational kernels. Experiments carried out under this project are expected to show that the BEAST framework can dramatically improve the performance of many computational kernels that are of fundamental importance to scientific computing. As this software and the techniques for using it are made widely available to the science and engineering community, they will help to ensure the timely delivery of performance- optimized kernels for many domains and many types of hybrid processors, making the impact of the BEAST bench-tuning software infrastructure very broad indeed. Scientists and engineers, across a vast array of intellectually, economically and socially important domains, will be able to rapidly tune the underlying kernels in their applications to the characteristics of the latest platform, and thereby quickly gain the productivity benefits of each successive generation of accelerator technology.