CNS Core Small: Simultaneous and Heterogeneous Multithreading

CNS Core Small：同时异构多线程

• 批准号: 2231877
• 负责人: Hung-Wei Tseng
• 金额: $ 59.3万
• 依托单位: University of California-Riverside
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-10-01 至 2026-09-30
• 项目状态: 未结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2231877&HistoricalAwards=false
• 关键词: CNS; Core; Small; Simultaneous; Heterogeneous

The significance of artificial intelligence (AI), machine learning (ML), data mining, and virtual/augmented/mixed reality (VR/AR/MR) in our everyday life has led to the adoption of hardware accelerators in modern computers. The existence of heterogeneous processing models in a computer is now ubiquitous. However, the entrenched computing and programming approaches and frameworks prevent the concurrent use of hardware accelerators. As a result, most hardware circuits are idle when executing applications, wasting power, consuming energy, and reducing battery life but making no contributions to computation. This proposed project, simultaneous and heterogeneous multithreading (SHMT), will revisit the entire stack of system architectures, including memory systems, operating systems, programming languages, and applications, to enable the concurrent use of all available hardware resources. SHMT will improve the utilization of precious hardware resources, reduce the energy consumption of computational tasks, shorten the execution time of applications, and lead to better user experiences on modern accelerator-rich computer systems. The experience of developing SHMT will help innovate educational materials in related computer engineering courses and create research opportunities for under-represented minority-serving institutions where we perform main research activities.Enabling simultaneous execution of parallel computation on heterogeneous processors requires SHMT to tackle challenges in allowing the programming framework to generate code segments where each performs parallel partitions of calculation for a task, extending the runtime system to ensure the qualities of execution results despite the various precisions, and hardware characteristics of each computing resource, providing mechanisms and abstractions for data sharing and exchanging in minimizing the overhead from the divergent data formatting demands on multiple types of processing resources, and potentially optimizing the scheduling policies and mechanisms to provide higher flexibility in task allocation. Specifically, the proposed project contains the following major tasks. (1) A programming framework and a set of applications support the SHMT model. (2) An intelligent runtime system guarantees the quality of results. (3) An innovative memory subsystem facilitates concurrent task execution on diverse computing resources. And potentially (4) A runtime system maximizes efficiency.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

人工智能（AI）、机器学习（ML）、数据挖掘和虚拟/增强/混合现实（VR/AR/MR）在我们日常生活中的重要性导致了现代计算机中硬件加速器的采用。计算机中异构处理模型的存在现在无处不在。然而，根深蒂固的计算和编程方法和框架阻止了硬件加速器的并发使用。因此，大多数硬件电路在执行应用程序时处于空闲状态，浪费功率，消耗能量，减少电池寿命，但对计算没有贡献。这个项目，同步和异构多线程（SHMT），将重新审视整个堆栈的系统架构，包括内存系统，操作系统，编程语言和应用程序，使所有可用的硬件资源的并发使用。SHMT将提高宝贵硬件资源的利用率，降低计算任务的能耗，缩短应用程序的执行时间，并在现代加速器丰富的计算机系统上带来更好的用户体验。开发SHMT的经验将有助于创新相关计算机工程课程的教育材料，并为我们开展主要研究活动的代表性不足的少数民族服务机构创造研究机会。实现在异构处理器上同时执行并行计算需要SHMT应对挑战，允许编程框架生成代码段，每个代码段执行任务的并行计算分区，扩展运行时系统以确保执行结果的质量，而不管每个计算资源的各种精度和硬件特性，提供用于数据共享和交换的机制和抽象以最小化来自对多种类型的处理资源的不同数据格式化需求的开销，并且潜在地优化调度策略和机制以在任务分配中提供更高的灵活性。具体而言，拟议项目包括以下主要任务。(1)一个编程框架和一组应用程序支持SHMT模型。(2)智能运行时系统保证了结果的质量。(3)一个创新的内存子系统促进了在不同计算资源上的并发任务执行。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。