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）或英特尔至强融核（又称，许多集成核（MIC），提出了关键的新的挑战，计算科学家。它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）。这样的程序花费大部分计算时间来执行这些内核中的一个或多个，并且长期的经验告诉开发人员，针对给定处理器的架构调整他们的内核对于在单个计算节点的级别上实现出色的性能是绝对必要的。由于科学家希望在具有数千个此类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力至关重要。不幸的是，对于绝大多数计算内核，性能调优的三种经典方法？编译器驱动的代码转换、低级手动编程还是经验性的自动调优？一直是非常困难的，往往产生混合的结果;和混合处理器的新兴时代，使所有三种技术更有效。自动软件调优的实验室测试环境（BEAST）为解决这一重要问题做出了重大贡献。 BEAST创建了一个用于探索和优化混合处理器上计算内核性能的框架，该框架1）适用于各种计算内核，2）（半）自动在各种混合处理器架构上生成性能更好的实现，以及3）增加开发人员对为什么给定内核/处理器组合具有它们所具有的性能配置文件的洞察力。为了实现这三重目标，它以一种全新的方式应用了传统应用程序基准测试所使用的模型：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）与自动化测试引擎和数据分析以及机器学习工具（称为BEAST工作台）相结合。该项目使用一种新的方法来指定语言中立的代码格式和原型BEAST工作台，探索各种计算内核的替代调优方法和策略。在该项目下进行的实验预计将表明，BEAST框架可以显着提高许多计算内核的性能，这些内核对科学计算至关重要。由于该软件及其使用技术广泛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，从而使BEAST基准调整软件基础设施的影响非常广泛。科学家和工程师，在智力，经济和社会重要领域的广泛阵列，将能够快速调整其应用程序中的底层内核，以适应最新平台的特性，从而快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.