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

CDS

基本信息

批准号：
2003307
负责人：
Ming-Cheng Cheng
金额：
$ 37.5万
依托单位：
Clarkson University
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2020
资助国家：
美国
起止时间：
2020-07-01 至 2024-06-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=2003307&HistoricalAwards=false
关键词：
CDS&amp Effective Thermal Simulation Methodology

项目摘要

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.

近年来，由于科学、工程和统计计算的需求，通用图形处理单元（GPGPU）的需求迅速增加，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、在线购物、大数据、物联网等的爆炸式增长，GPU 正在急剧增长。每个 GPGPU 中运行着数百或数千个核心，严重的发热是一个严峻的挑战，可能会显着降低 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，为了缓解所有这些问题，需要针对 GPGPU 操作进行有效的热管理和热感知任务调度，而这需要能够实现的精确模拟工具。以合理的空间分辨率提供高效的动态热预测。目前，缺乏能够以合理的分辨率提供高效率和准确性的热模拟工具。本文的目的是开发一种基于简化高效学习算法的高效模拟方法。能够以高分辨率预测准确的动态温度分布通过在 GPGPU 中实施这种新颖的方法，有效的热管理和任务调度将成为可能，并且将提高 GPGPU 的性能和可靠性，这也将改善冷却、计算和流媒体的节能，并最大限度地减少地球的环境压力。与拟议工作相关的研究将被纳入由 PI 教授的几门课程中，课程项目将由博士生和本科生开发。致力于这将为本科生和研究生提供超出教科书和讲座的有用学习体验，PI 还将扩展和整合一些正在进行的活动，以扩大代表性不足的群体对 STEM 的参与，例如通过 Co-PI 的 NSF REU 网站。我们将做出特别努力，招募和指导 PI 大学附近的印第安保留地的美国原住民加入 STEM 活动，并在 STEM 领域追求他们的职业生涯。该项目的目标是开发一种多模块模拟方法通过简化的学习算法对 GPGPU 的动态热分布进行高效、准确的预测，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，将 GPGPU 的域结构投影到由一组基础描述的功能空间上。然而，这种投影学习过程需要收集整个 GPGPU 的大量热数据，因此计算域分解将 GPGPU 域划分为数百个较小的通用构建块。方法使更多与目前 GPGPU 的直接数值模拟相比，该方法可以将半导体芯片热模拟的计算时间减少几个数量级。高效的紧凑电阻电容（RC）热模型提供了较差的分辨率和不准确的热分布，预计开发的热仿真模型将比紧凑的 RC 模型更有效。这个项目的天然优势是有效的并行计算。将在 GPGPU 中的数百个核心中实施开发的多块模型，以执行并行 GPGPU 计算，这将进一步加快 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产权评估进行评估，认为值得支持优点和更广泛的影响审查标准。

项目成果

期刊论文数量（14）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

TDF: A compact file format plugin for FEniCS

TDF：FEniCS 的紧凑文件格式插件

DOI：
10.1016/j.softx.2023.101329
发表时间：
2023-05
期刊：
SoftwareX
影响因子：
3.4
作者：
Dowling, Anthony;Jiang, Lin;Cheng, Ming;Liu, Yu
通讯作者：
Liu, Yu

An effective physics simulation methodology based on a data-driven learning algorithm

基于数据驱动学习算法的有效物理模拟方法

DOI：
10.1145/3539781.3539799
发表时间：
2022-06
期刊：
PASC '22: Proceedings of the Platform for Advanced Scientific Computing Conference
影响因子：
0
作者：
Jiang, Lin;Veresko, Martin;Liu, Yu;Cheng, Ming
通讯作者：
Cheng, Ming

Quantum element method for quantum eigenvalue problems derived from projection-based model order reduction

基于投影的模型降阶导出的量子本征值问题的量子元法

DOI：
10.1063/5.0018698
发表时间：
2020-11-05
期刊：
AIP Advances
影响因子：
1.6
作者：
M. Cheng
通讯作者：
M. Cheng

Physics-driven proper orthogonal decomposition: A simulation methodology for partial differential equations

物理驱动的适当正交分解：偏微分方程的模拟方法

DOI：
10.1016/j.mex.2023.102204
发表时间：
2023-01
期刊：
MethodsX
影响因子：
1.9
作者：
Pulimeno, Alessandro;Coates;Veresko, Martin;Jiang, Lin;Cheng, Ming;Liu, Yu;Hou, Daqing
通讯作者：
Hou, Daqing

Chip-level Thermal Simulation for a Multicore Processor Using a Multi-Block Model Enabled by Proper Orthogonal Decomposition

使用通过适当正交分解实现的多模块模型对多核处理器进行芯片级热仿真

DOI：
发表时间：
2022-05
期刊：
InterSociety Conf. on Thermal and Thermomechanical Phenomena in Electronic Systems
影响因子：
0
作者：
Lin Jiang;Anthony Dowling;Yu Liu;Ming
通讯作者：
Ming