CNS Core Small: Simultaneous and Heterogeneous Multithreading

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采用硬件加速器。计算机中异类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碍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都是空闲的，浪费功率，消耗能量，缩短电池寿命，但对计算没有贡献。这个拟议的项目，同步和异类多线程(SHMT)，将重新审视整个系统体系结构堆栈，包括内存系统、操作系统、编程语言和应用程序，以实现所有可用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材，并为我们开展主要研究活动的代表不足的少数族裔服务机构创造研究机会。要在不同的处理器上同时执行并行计算，SHMT需要解决以下挑战：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，尽管精度不同；以及每个计算资源的硬件特性，提供用于数据共享和交换的机制和抽象，以最大限度地减少因对多种类型处理资源的不同数据格式化需求而产生的开销，以及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。具体地说，拟议项目包含以下主要任务。(1)一个编程框架和一组应用程序支持SHMT模型。(2)智能运行系统保证了结果的质量。(3)创新的内存子系统有助于在不同计算资源上执行并发任务。这一奖项反映了NSF的法定使命，并通过使用基金会的智力优势和更广泛的影响审查标准进行评估，被认为值得支持。

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.