Towards Efficient Software-Defined Accelerator-Rich Systems

迈向高效的软件定义加速器丰富的系统

• 批准号: RGPIN-2019-04613
• 负责人: Fang, Zhenman
• 金额: $ 2.4万
• 依托单位: Simon Fraser University
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2021
• 资助国家: 加拿大
• 起止时间: 2021-01-01 至 2022-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=737740
• 关键词: Towards; Efficient; Software; Defined; Accelerator

With the significant slowdown of general-purpose CPU scaling, the computing industry has been actively exploring specialized and programmable hardware accelerators, such as GPUs and FPGAs, to bring orders-of-magnitude performance and energy gains for key applications such as machine learning, computational genomics, and scientific computing. While GPUs have already made a great success in the past decade, recently FPGAs have attracted increasing attention in datacenters: in the past year, Amazon, Alibaba, and Huawei all announced the public access of their FPGA-enabled cloud. However, there are two major challenges that impede the wide adoption of such heterogeneous systems with FPGAs in software applications. First, it is very difficult to program them since traditionally the use of FPGAs has been limited to a small group of hardware experts. Second, it is nontrivial to figure out whether a software application can get better performance on a system with FPGA or its strong competitor GPU. The long-term goal of this project is to enable the wide adoption of heterogeneous systems with FPGA accelerators in the vast software community, by developing efficient programming and runtime support for such systems to provide competitive performance, performance/watt, and/or performance/dollar. First, we will develop a software developer friendly programming framework that achieves efficient end-to-end system performance. To reduce the significant hardware expertise required by existing high-level synthesis (HLS) based accelerator designs, we plan to characterize common HLS programming patterns, automate their code transformation and design space exploration. Instead of focusing on a single accelerator design, we plan to provide system-level compilation and optimization that optimizes the communication between multiple accelerators, their memory system, and the host CPU cores. Second, we will develop an analytical model and program analysis tools to provide early-stage guidance in selecting FPGA or GPU acceleration for a given application. Our first step is to port widely recognized GPU (FPGA) benchmarks to FPGAs (GPUs), and quantitatively compare their performance. Based on the in-depth breakdown analysis, we will build an analytical model, develop program analysis tools and apply machine learning techniques to extract model factors from the application source code to guide the FPGA and GPU selection. The proposed research will develop critical technologies, reusable methodology and tools for efficient software-defined heterogeneous systems, to enable the software industry to continue scaling in post-Moore's law era. It will significantly accelerate the computing efficiency of driver applications that are key to Canada's economy and security, such as machine learning, personalized healthcare, scientific computing, and big data analytics. Moreover, it will provide many research and development opportunities to train our next-generation professionals.

随着通用CPU扩展速度的显著放缓，计算行业一直在积极探索专用和可编程硬件加速器，如gpu和fpga，为机器学习、计算基因组学和科学计算等关键应用带来数量级的性能和能量收益。虽然gpu在过去十年已经取得了巨大的成功，但最近fpga在数据中心引起了越来越多的关注：在过去的一年里，亚马逊、阿里巴巴和华为都宣布公开访问他们的fpga云。然而，有两个主要的挑战阻碍了fpga在软件应用中广泛采用这种异构系统。首先，很难对它们进行编程，因为传统上fpga的使用仅限于一小群硬件专家。其次，要弄清楚一个软件应用程序是否可以在FPGA或其强大的竞争对手GPU的系统上获得更好的性能是很重要的。该项目的长期目标是通过为这些系统开发高效的编程和运行时支持来提供具有竞争力的性能、性能/瓦特和/或性能/美元，从而使具有FPGA加速器的异构系统在庞大的软件社区中得到广泛采用。首先，我们将开发一个软件开发人员友好的编程框架，实现高效的端到端系统性能。为了减少现有的基于高级综合（high-level synthesis， HLS）的加速器设计所需的大量硬件专业知识，我们计划描述常见的HLS编程模式，自动化它们的代码转换和设计空间探索。我们不关注单个加速器设计，而是计划提供系统级编译和优化，以优化多个加速器、它们的内存系统和主机CPU内核之间的通信。其次，我们将开发一个分析模型和程序分析工具，为特定应用选择FPGA或GPU加速提供早期指导。我们的第一步是将广泛认可的GPU （FPGA）基准移植到FPGA (GPU)，并定量比较它们的性能。基于深入的细分分析，我们将构建分析模型，开发程序分析工具，并应用机器学习技术从应用程序源代码中提取模型因素，以指导FPGA和GPU的选择。拟议的研究将为高效的软件定义异构系统开发关键技术、可重用方法和工具，使软件行业能够在后摩尔定律时代继续扩展。它将显著加快对加拿大经济和安全至关重要的驾驶应用程序的计算效率，如机器学习、个性化医疗保健、科学计算和大数据分析。此外，它将提供许多研发机会，以培养我们的下一代专业人才。