SHF: Medium: Energy Efficient Computing on GPU-based Heterogeneous Systems

当前设计未来多处理器的趋势是在单个平台上集成数百个内核和硬件加速器（如gpu）。然而，随着系统规模的扩大，这些异构多处理器的功率和能耗大大超出了预算。该项目的主要重点是开发可以在GPU中实现的节能技术，并与cpu进行适当的协调。通过将当前CPU技术扩展到GPU，可以降低基于GPU的异构多处理器的能耗。该项目为GPU核心缩放和动态电压频率缩放（DVFS）开发了新的运行时技术，并将它们与算法和数据结构的基本变化相结合，以提高能源效率。现有的性能和能耗模型假设处理核心的利用率为100%，并且在执行期间与内存访问完全重叠。考虑到算法、数据结构、缓存和不同应用的内存合并，该项目开发了一个更准确的能源效率模型。正在开发一个运行时系统，用于监控GPU核心和内存利用率，以及执行应用程序时的能耗。运行时通过预测动态调整内核数量和/或频率级别，但在执行过程中继续进行修正。将运行时扩展到由CPU和GPU组成的异构系统。目前用于科学计算应用的DVFS技术不能完全消除松弛，因此不是能量最优的。利用算法的特点，开发了一种用于线性代数应用的频率调度技术，以达到更好的能源效率。该项目在划分和设计应用程序任务的同时优化了能源效率。在不失一般性的前提下，以Cholesky分解为例，提出了一种节能调度方法。该项目开发的软件产品可以很容易地应用于现有的大规模异构计算机，执行适用于国防、能源和关键基础设施项目的科学应用程序。此外，天气预报和结构动力学等应用程序也被开发出来，对社会产生重大影响。将研究内容整合到研究生课程中，培养学生设计和编程异构系统的能力。该项目旨在培养包括女学生在内的高素质博士研究生。加州大学河滨分校以其大量的西班牙裔学生而闻名，加州大学河滨分校是一所为少数族裔服务的大学。该项目支持招募未被充分代表的少数民族和女学生。

The current trend in designing future multiprocessors is to integrate hundreds of cores and hardware accelerators (HAs), such as GPUs, on a single platform. However, as the system size scales, the power and energy consumption of these heterogeneous multiprocessors vastly exceed the budget. The primary focus of the project is to develop energy efficient techniques that can be implemented in the GPU with proper coordination with the CPUs. Energy reduction in GPU-based heterogeneous multiprocessors can be achieved by extending current CPU techniques to GPU. The project develops new runtime techniques for GPU core scaling and Dynamic Voltage and Frequency scaling (DVFS), and combines them with basic changes in algorithms and data structures to improve energy efficiency. Existing models for performance and energy consumption assume 100% utilization of the processing cores and perfect overlap with memory access during execution. This project develops a more accurate model for energy efficiency taking into account the algorithm, data structure, caching and memory coalescing for different applications. A runtime system is being developed that monitors the GPU core and memory utilizations together with the energy consumption while executing an application. The runtime adjusts the number of cores and/or frequency level dynamically through prediction, but continues to make corrections as the execution proceeds. The runtime is extended to heterogeneous systems consisting of both CPU and GPU.The current DVFS techniques for scientific computing applications cannot fully eliminate slacks, therefore, are not energy optimal. By leveraging the algorithmic characteristics, a frequency scheduling technique is developed for linear algebra applications to achieve better energy efficiency.The project optimizes the energy efficiency while partitioning and designing tasks of an application. Without loss of generality, Cholesky factorization is used as an example and an energy efficient scheduler is developed. The project develops software products that can be readily applied to existing large scale heterogeneous computers executing scientific applications that are suitable for defense, energy and critical infrastructure projects. Also applications like weather forecasting and structural dynamics are developed that have a great impact on society. The research content is integrated to graduate courses to provide training to students for designing and programming heterogeneous systems. The project aims to produce very high quality Ph.D. graduates including female students. The University of California, Riverside is known for its large proportion of Hispanic students, and UCR is a minority-serving institution. The project supports recruiting underrepresented minority and female students.