SI2-SSE: BONSAI: An Open Software Infrastructure for Parallel Autotuning of Computational Kernels

今天，大多数超级计算机通过使用多核处理器来加速计算，以解决科学和工程中的重要问题。虽然这降低了硬件系统的成本，但它大大增加了编写和优化（“调优”）软件的复杂性。该项目扩展了以前资助的NSF项目：Benchtesting Environment for Automated Software Tuning（BEAST）计划，以创建一个允许半自动优化软件的软件工具包，从而减少编程开销。这个项目，BEAST OpeN软件自动调整基础设施（BONSAI）将大大提高科学家和工程师的效率，开发快速有效的程序来解决他们的问题。邦赛得到了各计算机处理器制造公司和学术机构的大力支持。BONSAI系统将作为开源软件提供给学术和商业使用，许多学生将接受使用该软件的培训。混合系统的出现和日益增长的主导地位，包括加速器处理器，如GPU和协处理器，使得在大多数研究应用中执行大部分工作的不同计算内核的性能优化变得更加困难。BONSAI项目旨在通过开发一个软件基础设施来创建一个变革性的解决方案，该基础设施使用并行混合机器来实现GPU加速器和众核协处理器计算内核的大规模自动调整扫描。该系统将超越仅仅测量运行时，允许收集和分析来自硬件性能计数器和功率计的大量数据。该系统将采用模块化架构，并依赖于标准数据格式和接口，以便轻松连接主流工具进行数据分析和可视化。BONSAI项目将利用BEAST项目的经验，该项目建立了成功的自动调整方法并验证了自动调整工作流程。邦赛将为社区提供一个软件环境，用于将并行资源应用于计算内核的调优和性能分析。具体来说，这项工作将围绕以下目标进行组织：（1）强化和扩展名为BeastLang的编程语言，该语言是在先前的研究中创建的，作为定义自动调优基础设施生成和探索的搜索空间的一种方式。BeastLang使用户能够创建参数化的内核规范，对内核本身、编译工具和目标硬件之间的相互作用进行编码。它将与BONSAI的其他组件集成，增强和扩展其Python语法，改进其编译器，并补充一个运行时，该运行时支持自动调优过程的多路并行性。(2)开发和测试一个基准测试引擎，使用大量GPU加速器或众核协处理器进行大规模并行调优扫描。这个引擎将支持并行编译和并行测试的结果内核，使用许多分布式内存节点和多线程在每个节点，动态负载平衡。它将从硬件计数器和可能的能量计中产生大量的性能信息，以及关于不同指令类的编译代码饱和度的信息。(3)开发和测试用于收集、预处理和分析邦赛性能数据的软件基础设施。该系统将a）简化检测内核的任务，并提供简单的接口，用于选择要收集的具有合理默认值的度量; B）简化从各种开放源代码和供应商特定库收集硬件计数器和性能数据的过程;以及c）提供允许用户快速有效地将输出数据转换为可使用主流工具轻松读取和分析的格式的工具比如R和Python。(4)记录并举例说明使用BONSAI来调整各种不同类型的内核的过程。这些模型案例研究将讨论如何应用BeastLang创建参数化内核模板，如何调用并行基准测试引擎来生成和探索搜索空间，以及如何分析和可视化从引擎操作中收集的数据，以获得可以纠正或改进另一次迭代过程的见解。BONSAI项目有可能从根本上改变自动调优研究：1）使来自广泛计算学科的广大开发人员都可以使用自动调优，而不是少数几个具有魔法的人在串行执行的范围内建立成功的实验。2)改变对自动调优的普遍看法，使其不仅是生成快速代码的手段，而且是对复杂的软件和硬件交互进行性能分析和推理的通用技术，并将该技术定位为硬件-软件协同设计的主要工具之一。3)提高人们对探索被忽视的计算途径的兴趣，例如探索非正统的数据布局，并挑战传统软件界面的现状。BONSAI有潜力将自动调优带到软件开发的最前沿，并帮助将自动调优定位为软件工程的支柱。

Most supercomputers today accelerate the computations by using processors with many cores to solve important problems in science and engineering. Although this reduces the cost of the hardware system, it greatly increases the complexity of writing and optimizing ("tuning") software. This project extends a previously funded NSF project: Benchtesting Environment for Automated Software Tuning (BEAST) program to create a software toolkit that allows for semi-automatic optimization of software, thereby reducing the programming overhead. This project, BEAST OpeN Software Autotuning Infrastructure (BONSAI) will greatly increase the efficiency of scientists and engineers to develop fast and efficient programs to solve their problems. BONSAI has tremendous support from various computer processor manufacturing companies and academic institutions. The BONSAI system will be available as open-source software for academic and commercial use and many students will be trained in using the software.The emergence and growing dominance of hybrid systems that incorporate accelerator processors, such as GPUs and coprocessors, have made it far more difficult to optimize the performance of the different computational kernels that do the majority of the work in most research applications. The BONSAI project aims to create a transformative solution to this problem by developing a software infrastructure that uses parallel hybrid machines to enable large autotuning sweeps on computational kernels for GPU accelerators and many-core coprocessors. The system will go beyond just measuring runtimes, allowing for collection and analysis of non-trivial amount of data from hardware performance counters and power meters. The system will have a modular architecture and rely on standard data formats and interfaces to easily connect with mainstream tools for data analytics and visualization. The BONSAI project will leverage the experiences of the BEAST project, which established a successful autotuning methodology and validated an autotuning workflow. BONSAI will equip the community with a software environment for applying parallel resources to the tuning and performance analysis of computational kernels. Specifically, the work will be organized around the following objectives: (1) Harden and extend the programming language called BeastLang, which was created during prior research as a way of defining the search space that the autotuning infrastructure generates and explores. BeastLang enables users to create parameterized kernel specifications that encode the interplay between the kernels themselves, the compilation tools, and the target hardware. It will be integrated with the other components of BONSAI, have its Python syntax enhanced and extended, its compiler improved, and be supplemented with a runtime that supports it with multi-way parallelism for the autotuning process. (2) Develop and test a benchtesting engine for making large scale parallel tuning sweeps, using large numbers of GPU accelerators or many-core coprocessors. This engine will support both parallel compilation and parallel tests of the resulting kernels, using many distributed memory nodes and multithreading within each node, with dynamic load balancing. It will produce an extensive collection of performance information from hardware counters, and possibly energy meters, as well as collection of information about the saturation of the compiled code with different classes of instructions. (3) Develop and test a software infrastructure for collecting, preprocessing, and analyzing BONSAI performance data. The system will a) simplify the task of instrumenting the kernel and provide a simple interface for selecting the metrics to be collected with sensible defaults; b) simplify the process of collecting hardware counters and performance data from various open source and vendor specific libraries; and c) provide tools that allow the user to quickly and efficiently transform output data to a format that can be easily read and analyzed using mainstream tools such as R and Python. (4) Document and illustrate the process of using BONSAI to tune various different types of kernels. These model case studies will include discussions of how BeastLang was applied to create the parameterized kernel stencil, how the parallel benchtesting engine is invoked to generate and explore the search space, and how the data collected from the operation of the engine can be analyzed and visualized to gain insights that can correct or refine the process for another iteration. The BONSAI project has the potential to fundamentally transform autotuning research by: 1) Making autotuning accessible to a broad audience of developers from a broad range of computing disciplines, as opposed to a few selected individuals with the wizardry to set up a successful experiment within the confines of serial execution. 2) Changing the general perception of autotuning as not just the means of producing fast code, but as a general technique for performance analysis and reasoning about the complex software and hardware interactions, and positioning the technique as one of primary tools for hardware-software co-design. 3) Boosting interest in exploring neglected avenues of computing, such as exploration of unorthodox data layouts, and challenge the status quo of legacy software interfaces. BONSAI has the potential to bring autotuning to the forefront of software development and to help position autotuning as a pillar of software engineering.