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.

计算机体系结构通信的改进将对计算行业产生广泛影响，并将对加拿大产生重大好处。通讯的改善将使新的、更大的计算机系统成为可能，从医学到经济学等广泛学科的研究人员都可以利用这些系统。我的研究将提供更大的计算能力，使这些领域的研究人员能够解决社会上一些最紧迫的问题。在计算机系统领域，迫切需要HQP。接受这个项目培训的学生将获得架构、软件、编译器、算法和电路方面的专业知识，并受到众多加拿大公司的追捧。**在过去的几十年里，计算机行业每一代技术(每18-24个月)都会使每个芯片的晶体管数量翻一番；这就是众所周知的摩尔定律。这些高晶体管密度形成了电源墙，限制了通用处理器中时钟频率的调节速度。因此，英特尔、AMD和IBM等处理器供应商现在在单个芯片上集成多个处理器以提高性能，而不是速度和/或复杂性更高的单个内核。因此，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。**要利用这些多核的计算能力，核之间的通信是必不可少的。与任何团队工作活动一样，当所有团队成员频繁沟通并共同工作时，会取得最好的进展；并行计算机体系结构也是如此。通信在整个系统性能中起着至关重要的作用。我目前的研究重点是计算机体系结构和软件技术，以简化和提高核心之间的通信效率。架构不佳的通信结构可能会导致计算系统的性能和功率瓶颈。据预测，在未来几代计算系统中，通信所消耗的能量将超过计算所消耗的能量。尽管多核计算让我们暂时避开了电源问题，但电源再次成为一个关键问题；暗硅，即无法同时为芯片的所有部分供电的问题，将在几代技术中成为现实。通过高效地将数据移入/移出硬件加速器，通信将在协调暗硅系统中发挥至关重要的作用。**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效。通过观察和学习应用程序的运行时行为，我们可以定制通信结构以提供更高的能效。**2.在性能和能效之间权衡精确度的硬件和软件优化。近似计算建议通过允许计算中出现一些错误来节省电力。例如，图像处理可以容忍一些人眼看不到的错误。我们将探索近似计算对通信需求的影响，并探索这些应用程序对通信错误的容忍度。**3.探索通信在暗硅架构和硬件优化中的作用，以促进改进的通信。在当前的片上网络体系结构中，通常很难关闭网络的一个子集。我们将探索专门设计为部分断电以支持暗硅的新拓扑和体系结构。