Intelligently orchestrating communication in many-core architectures

智能编排多核架构中的通信

• 批准号: RGPIN-2014-06033
• 负责人: EnrightJerger, Natalie
• 金额: $ 2.26万
• 依托单位: University of Toronto
• 依托单位国家: 加拿大
• 项目类别: Discovery Grants Program - Individual
• 财政年份: 2019
• 资助国家: 加拿大
• 起止时间: 2019-01-01 至 2020-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=683930
• 关键词: Intelligently; orchestrating; communication; many; core

Improvement in communication for computer architectures will have broad impact on the computing industry and will have significant benefits to Canada. Improved communication will enable novel, larger computer systems that can be leveraged by researchers in a wide range of disciplines from medicine to economics. My research will provide greater computational capabilities to enable the researchers in these fields to solve some of society's most pressing issues. There is a critical need for HQP in the area of computer systems. Students trained on this project will gain expertise in architecture, software, compilers, algorithms and circuits and be highly sought after by numerous Canadian companies.**Over the last several decades, the computer industry has doubled the number of transistors per chip with each technology generation (every 18-24 months); this is known as Moore's Law. These high transistor densities have created a power wall, limiting the rate of clock frequency scaling in general purpose processors. As a result, processor vendors such as Intel, AMD and IBM now incorporate multiple processors on a single chip to improve performance, rather than a single core with increased speed and/or complexity. As a result, parallel architectures based on multi-core technology are ubiquitous--they can be found across all types of computer systems from servers to cellphones.**To leverage the computational capabilities of these multiple cores, communication between cores is essential. As with any teamwork activity, the best progress is made when all team members are communicating frequently and working together; it is much the same with parallel computer architectures. Communication plays a critical role in overall system performance. My current research agenda focuses on computer architectural and software techniques to streamline and improve the efficiency of the communication between cores. Poorly architected communication fabrics can lead to performance and power bottlenecks for computing systems. It is projected that the energy expended on communication will exceed that consumed by computation in future generations of computing systems. Although multi-core computing has allowed us to temporarily side-step power issues, power is once again a critical issue; dark silicon, or the inability to power on all parts of the chip simultaneously will become a reality in just a few technology generations. Communication will play a vital role in orchestrating dark silicon systems by efficiently moving data to/from hardware accelerators specifically designed to run computations in the most power-efficient manner.**The long term goals of this research are to**1. Leverage online learning techniques to optimize the performance and energy-efficiency of the communication fabric. By observing and learning from an application's runtime behaviour, we can tailor the communication fabric to provide greater energy efficiency.**2. Hardware and software optimizations that trade-off accuracy for performance and energy efficiency. Approximate computing proposes to save power by allowing some error to emerge in computations. For example, image processing can tolerate some error that will not be noticeable to the human eye. We will explore the implications of approximate computing on communication requirements and explore the tolerance of these applications to communication errors.**3. Explore the role of communication in dark silicon architectures and hardware optimizations to facilitate improved communication. In current on-chip network architectures, it is often difficult to power-down a subset of the network. We will explore new topologies and architectures that are specifically designed to be partially powered down to support dark silicon.

计算机架构的通信改善将对计算行业产生广泛的影响，并将对加拿大带来重大好处。改进的沟通将使研究人员可以在从医学到经济学的各个学科中利用新颖，更大的计算机系统。我的研究将提供更大的计算能力，以使这些领域的研究人员能够解决一些社会最紧迫的问题。在计算机系统领域，HQP迫切需要。接受该项目培训的学生将获得建筑，软件，编译器，算法和电路的专业知识，并受到众多加拿大公司的高度追捧。这被称为摩尔定律。这些高晶体管密度创建了一个电源墙，限制了通用处理器中时钟频率缩放的速率。结果，英特尔，AMD和IBM等处理器供应商现在在单个芯片上合并了多个处理器，以提高性能，而不是具有提高速度和/或复杂性的单个核心。结果，基于多核技术的并行体系结构无处不在 - 它们可以在从服务器到手机的所有类型的计算机系统中找到。与任何团队合作的活动一样，当所有团队成员经常交流并共同努力时，取得了最佳进步。并行计算机体系结构与众不同。沟通在整体系统性能中起着至关重要的作用。我目前的研究议程专注于计算机架构和软件技术，以简化和提高核心之间通信的效率。架构不佳的通信织物可以导致计算系统的性能和功率瓶颈。预计在通信上消耗的能量将超过未来一代计算系统中计算所消耗的能量。尽管多核计算使我们能够暂时侧键盘问题，但功率再次成为关键问题。黑暗的硅，或者无法同时在芯片上的所有部分中的动力将在仅几代技术中成为现实。通过有效地将数据转移到专门设计用于以最有效的方式运行计算的硬件加速器的数据中，沟通将在编排黑暗硅系统中起着至关重要的作用。**这项研究的长期目标是** 1。利用在线学习技术来优化通信结构的性能和能源效率。通过观察和从应用程序的运行时行为中学习，我们可以调整通信结构以提供更大的能源效率。** 2。硬件和软件优化，以取决于性能和能源效率的精确度。近似计算建议通过允许在计算中出现一些错误来节省功率。例如，图像处理可以容忍某些误差，而这些错误对人眼而言不会引起人们的注意。我们将探讨近似计算对通信需求的含义，并探讨这些应用程序对通信错误的公差。** 3。探索沟通在深色硅体系结构和硬件优化中的作用，以促进改善沟通。在当前的芯片网络体系结构中，通常很难为网络的一个子集提供动力。我们将探索专门设计的新拓扑和体系结构，以部分支持黑暗硅。