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也正在迅速成为地球仪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。由于每个GPGPU中运行数百或数千个核心，严重的发热是一个严重的挑战， 可能会显著降低GPGPU的性能、可靠性和能源效率，除非采用有效的冷却。 然而，数据中心的有效冷却需要巨大的能量消耗。 为了缓解所有这些问题，需要针对GPGPU操作的有效热管理和热感知任务调度，然而，这需要能够以合理的空间分辨率提供有效的动态热预测的精确仿真工具。 目前，缺乏热模拟工具，提供高效率和准确性与合理的分辨率。所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，该算法能够在GPGPU中以高分辨率预测准确的动态温度分布。 通过在GPGPU中实现这种新方法，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、计算和流媒体的节能效果，并最大限度地减少地球的环境压力。 该项目还将有助于跨学科的劳动力培训，并为学生应对GPGPU计算中出现的加热问题的挑战做好准备。与拟议工作有关的研究将纳入PI教授的几门课程。课程项目将由博士开发。和本科生工作的建议工作。这将为本科生和研究生提供超越教科书和讲座的有用的学习经验。PI还将扩大和整合几项正在进行的活动，以扩大STEM中代表性不足的群体的参与，例如通过Co-PI的NSF REU网站。一个特别的努力将作出招募和指导印第安人保留地附近的PI的大学加入STEM活动，并追求他们的职业生涯在STEM。该项目的目标是开发一个多块仿真方法，有效，准确的预测动态热配置文件的GPGPU来自减少学习算法。为了减少模拟空间，从而减少计算时间，同时保持准确的热解，GPGPU的域结构被投影到由从减少的学习方法获得的一组基函数描述的函数空间上。 然而，这种投影学习过程需要为整个GPGPU收集大量的热数据，并且在计算上是禁止的。因此，域分解被应用于将GPGPU域划分成数百个较小的通用构建块。这种构建块方法能够更有效地训练基函数以开发多块热模型。 这种方法提供了一个减少计算时间的几个数量级的半导体芯片的热模拟，与直接数值模拟相比。 目前，GPGPU的热模拟依赖于高效紧凑的阻容（RC）热模型，该模型提供了较差的分辨率和不准确的热曲线。预计开发的热模拟模型将比紧凑的RC模型更有效。 此外，多块方法具有有效的并行计算的天然优势。该项目将在GPGPU的数百个核心中实现开发的多块模型，以执行并行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。

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.