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CC* Compute: A high-performance GPU cluster for accelerated research

CC* 计算：用于加速研究的高性能 GPU 集群

基本信息

批准号：
1925717
负责人：
Kris Delaney
金额：
$ 39.48万
依托单位：
University of California-Santa Barbara
依托单位国家：
美国
项目类别：
Standard Grant
财政年份：
2019
资助国家：
美国
起止时间：
2019-10-01 至 2021-09-30
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1925717&HistoricalAwards=false
关键词：
CC Compute performance GPU cluster

项目摘要

The exponential growth of computing power and the emergence of high-performance computing paradigms has revolutionized all fields of science and engineering. Graphics processing unit (GPU) hardware, a type of highly parallel co-processor originally designed for generating 3D scenes in video games, has been increasingly leveraged over the last decade to dramatically accelerate scientific computing workloads. This project is for the acquisition of a GPU compute cluster consisting of 24 state-of-the-art NVIDIA Tesla V100 32 GB GPUs with fast inter-GPU communication. The resource is housed at the University of California, Santa Barbara (UCSB), and is accessible to researchers across campus and externally through a connection to the Pacific Research Platform/Nautilus federated systems network.Initial research activities on the facility span the computational realm, including: a new type of multi-scale molecular simulation for predicting structural and thermodynamic properties of complex polymeric solution formulations; a materials characterization thrust involving crystal orientation indexing with real-time instrument feedback control; and the development of a scalable Neural Architecture Search framework for automatic generation of Deep Neural Network models for scientific applications of machine learning. The cluster provides a significant resource for educating the next generation of computational scientists in the latest GPU-computing techniques. Undergraduates, high-school students, and K-12 teachers will also have access via existing campus-sponsored programs: Research Experience for Teachers (RET), California Alliance for Minority Participation (CAMP), and the Center for Science and Engineering Partnerships (CSEP). These programs serve to provide training and increase the number of under-represented students in STEM fields.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.

计算能力的指数级增长和高性能计算范式的出现彻底改变了所有科学和工程领域。图形处理单元（GPU）硬件是一种高度并行的协处理器，最初设计用于在视频游戏中生成3D场景，在过去十年中，GPU硬件越来越多地被用来显著加速科学计算工作负载。该项目旨在收购一个GPU计算集群，该集群由24个最先进的NVIDIA Tesla V100 32 GB GPU组成，具有快速的GPU间通信。该资源位于加州大学圣巴巴拉分校（UCSB），校园内和外部的研究人员均可通过连接到太平洋研究平台/Nautilus联邦系统网络访问该设施的初始研究活动跨越计算领域，包括：一种新型的多尺度分子模拟，用于预测复杂聚合物溶液配方的结构和热力学性质;一个材料表征的推力，涉及晶体取向索引与实时仪器反馈控制;和一个可扩展的神经架构搜索框架的开发，用于自动生成深度神经网络模型的机器学习的科学应用。该集群为教育下一代计算科学家提供了最新的GPU计算技术的重要资源。本科生，高中生和K-12教师也将通过现有的校园赞助计划获得：教师研究经验（RET），加州少数民族参与联盟（CAMP）和科学与工程合作中心（CSEP）。该奖项反映了NSF的法定使命，并通过使用基金会的智力价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

期刊论文数量（23）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Molecularly Informed Field Theories from Bottom-up Coarse-Graining

DOI：
10.1021/acsmacrolett.1c00013
发表时间：
2021-04-22
期刊：
ACS MACRO LETTERS
影响因子：
7.015
作者：
Sherck, Nicholas;Shen, Kevin;Fredrickson, Glenn H.
通讯作者：
Fredrickson, Glenn H.

Industrial, large-scale model predictive control with structured neural networks

使用结构化神经网络进行工业大规模模型预测控制

DOI：
10.1016/j.compchemeng.2021.107291
发表时间：
2021
期刊：
Computers & Chemical Engineering
影响因子：
4.3
作者：
Kumar, Pratyush;Rawlings, James B.;Wright, Stephen J.
通讯作者：
Wright, Stephen J.

Quantifying Polypeptoid Conformational Landscapes through Integrated Experiment and Simulation

DOI：
10.1021/acs.macromol.1c00550
发表时间：
2021-06
期刊：
Macromolecules
影响因子：
5.5
作者：
Sally Jiao;Audra J. DeStefano;Jacob I. Monroe;M. Barry;Nicholas Sherck;Thomas Casey;R. Segalman;Songi Han;M. Shell
通讯作者：
Sally Jiao;Audra J. DeStefano;Jacob I. Monroe;M. Barry;Nicholas Sherck;Thomas Casey;R. Segalman;Songi Han;M. Shell

Eliminating Redundant Computation in Noisy Quantum Computing Simulation

消除噪声量子计算模拟中的冗余计算

DOI：
10.1109/dac18072.2020.9218666
发表时间：
2020
期刊：
2020 57th ACM/IEEE Design Automation Conference (DAC
影响因子：
0
作者：
Li, Gushu;Ding, Yufei;Xie, Yuan
通讯作者：
Xie, Yuan

APNN-TC: Accelerating Arbitrary Precision Neural Networks on Ampere GPU Tensor Cores

DOI：
10.1145/3458817.3476157
发表时间：
2021-06
期刊：
SC21: International Conference for High Performance Computing, Networking, Storage and Analysis
影响因子：
0
作者：
Boyuan Feng;Yuke Wang;Tong Geng;Ang Li;Yufei Ding
通讯作者：
Boyuan Feng;Yuke Wang;Tong Geng;Ang Li;Yufei Ding