CDS&E: An Effective Thermal Simulation Methodology for GPGPUs Enabled by Data-Driven Model Reduction

近年来，由于科学、工程和统计计算的需要，对通用图形处理单元（gpgpu）的需求迅速增加。与此同时，gpgpu也迅速成为全球数据中心的重要组成部分。由于最近社交网络、电影流媒体、在线购物、大数据、物联网等的爆炸式增长，数据中心的数量正在急剧增长。在每个GPGPU中运行数百或数千个内核，除非采用有效的冷却，否则严重的加热会严重降低GPGPU的性能，可靠性和能源效率。然而，数据中心的有效冷却需要大量的能源消耗。为了缓解这些问题，需要有效的热管理和GPGPU运行的热感知任务调度，然而，这需要一个精确的仿真工具，能够在合理的空间分辨率下提供有效的动态热预测。目前，缺乏能够在合理的分辨率下提供高效率和准确性的热模拟工具。提出的工作旨在开发一种基于简化学习算法的高效仿真方法，该算法能够预测gpgpu中具有高分辨率的准确动态温度分布。随着这种新方法在GPGPU中实现，有效的热管理和任务调度将成为可能，并将提高GPGPU的性能和可靠性。这也将提高冷却、计算和流媒体方面的能源节约，并最大限度地减少对地球环境的压力。该项目还将有助于跨学科的劳动力培训，并为学生应对GPGPU计算中加热问题的新挑战做好准备。与提议的工作相关的研究将被整合到pi教授的几门课程中。课程项目将由博士和从事拟议工作的本科生开发。这将为本科生和研究生提供超越课本和讲座的有用学习经验。pi还将扩大和整合几项正在进行的活动，以扩大代表性不足群体对STEM的参与，例如通过Co-PI的NSF REU网站。将做出特别努力，从PI大学附近的印第安保留地招募和指导印第安人加入STEM活动，并在STEM领域追求自己的职业生涯。该项目的目标是开发一种多块仿真方法，用于高效，准确地预测源自简化学习算法的gpgpu的动态热剖面。为了减少模拟空间和计算时间，同时保持精确的热解，将GPGPU的域结构投影到由简化学习方法获得的一组基函数描述的功能空间中。然而，这种投影学习过程需要为整个GPGPU收集大量的热数据，并且在计算上是令人望而却步的。因此，应用域分解将GPGPU域划分为数百个较小的通用构建块。这种构建块方法可以更有效地训练基函数来开发多块热模型。与直接数值模拟相比，该方法可将半导体芯片热模拟的计算时间减少几个数量级。目前，gpgpu的热模拟依赖于高效的紧凑电阻-电容（RC）热模型，该模型提供了较差的分辨率和不准确的热分布。期望开发的热模拟模型比紧凑的RC模型更有效。此外，多块方法具有有效并行计算的天然优势。本项目将在GPGPU的数百核中实现所开发的多块模型，以实现并行GPGPU计算，从而进一步加快GPGPU的热模拟速度。该奖项反映了美国国家科学基金会的法定使命，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。

Demands for general purpose graphics processing units (GPGPUs) in recent years have increased rapidly due to the needs for scientific, engineering and statistical computing. Meanwhile, GPGPUs are also quickly becoming an essential part of data centers around the globe. The number of data centers are growing drastically due to the recent explosion of social networking, movie streaming, online shopping, big data, internet of things, etc. With hundreds or thousands of cores running in each GPGPU, severe heating is a serious challenge which can significantly degrade GPGPU performance, reliability and energy efficiency unless effective cooling is employed. However, effective cooling of data centers requires enormous expenditure of energy. To ease all these problems, effective thermal management and thermal-aware task scheduling for GPGPU operation are needed, which however requires an accurate simulation tool that is able to offer efficient dynamic thermal prediction with a reasonable spatial resolution. Currently, there is a lack of thermal simulation tools that offer high efficiency and accuracy with a reasonable resolution. The proposed work aims to develop an efficient simulation methodology based on a reduced learning algorithm that is capable of predicting accurate dynamic temperature distributions with a high resolution in GPGPUs. With this novel approach implemented in GPGPUs, effective thermal management and task scheduling will become possible and will improve GPGPU performance and reliability. This will also improve energy savings in cooling, computing and streaming and minimize the earth’s environmental stress. This project will also contribute to interdisciplinary workforce training and prepare students for the emerging challenge of heating problems in GPGPU computing. Research related to the proposed work will be integrated into several courses taught by the PIs. Course projects will be developed by the Ph.D. and undergraduate students working on the proposed work. This will offer undergraduate and graduate students a useful learning experience beyond the textbooks and lectures. The PIs will also expand and integrate several ongoing activities to broaden participation of underrepresented groups in STEM, e.g. through the Co-PI's NSF REU site. A special effort will be made to recruit and mentor Native Americans from an Indian Reservation near the PI’s university to join STEM activities and to pursue their careers in STEM.The goal of this project is to develop a multi-block simulation methodology for efficient, accurate prediction of dynamic thermal profiles of GPGPUs derived from a reduced learning algorithm. To reduce simulation space and thus the computational time while maintaining accurate thermal solution, the domain structure of a GPGPU is projected onto a functional space described by a set of basis functions obtained from the reduced learning method. This projection learning process however requires collection of massive amounts of thermal data for the entire GPGPU and is computationally prohibitive. Domain decomposition is therefore applied to partition the GPGPU domain into hundreds of smaller generic building blocks. This building-block approach enables more efficient training of the basis functions to develop the multi-block thermal model. This methodology offers a reduction in the computational time by several orders of magnitude for thermal simulation of semiconductor chips, compared with the direct numerical simulation. Currently, thermal simulations of GPGPUs rely on the efficient compact resistance-capacitance (RC) thermal model that provides poor resolution and inaccurate thermal profiles. It is expected that the developed thermal simulation model will be even more efficient than the compact RC model. Also, the multi-block approach possesses a natural advantage of effective parallel computing. This project will implement the developed multi-block model in hundreds of cores in a GPGPU to perform parallel GPGPU computing that will further speed up the thermal simulation of GPGPUs.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.