Enabling a Quantum Monte Carlo application for the DEEP architecture

A. Emerson, F. Affinito
{"title":"Enabling a Quantum Monte Carlo application for the DEEP architecture","authors":"A. Emerson, F. Affinito","doi":"10.1109/HPCSim.2015.7237075","DOIUrl":null,"url":null,"abstract":"In the DEEP project a prototype Exascale system consisting of a standard Intel Xeon cluster linked to a “Booster” part containing Intel Xeon Phi nodes connected in a high-speed network, is being designed and constructed. In order to evaluate this novel architecture, expected to be available in the second half of 2015, a number of grand challenge applications in computational science and engineering are being modified and optimised. In this study we report on the efforts made by the Cineca project partner and DEEP support staff to enable one of these applications, the TurboRVB Quantum Monte Carlo simulation program, which can be used to study complex phenomena in materials such as superconductivity. The modified code, based on an implementation of the OmpSs offload task model, has been successfully tested on the MareNostrum supercomputer at the Barcelona Supercomputing Center.","PeriodicalId":134009,"journal":{"name":"2015 International Conference on High Performance Computing & Simulation (HPCS)","volume":"44 6 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2015-07-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"2015 International Conference on High Performance Computing & Simulation (HPCS)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/HPCSim.2015.7237075","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
引用次数: 1

Abstract

In the DEEP project a prototype Exascale system consisting of a standard Intel Xeon cluster linked to a “Booster” part containing Intel Xeon Phi nodes connected in a high-speed network, is being designed and constructed. In order to evaluate this novel architecture, expected to be available in the second half of 2015, a number of grand challenge applications in computational science and engineering are being modified and optimised. In this study we report on the efforts made by the Cineca project partner and DEEP support staff to enable one of these applications, the TurboRVB Quantum Monte Carlo simulation program, which can be used to study complex phenomena in materials such as superconductivity. The modified code, based on an implementation of the OmpSs offload task model, has been successfully tested on the MareNostrum supercomputer at the Barcelona Supercomputing Center.
查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
为DEEP架构启用量子蒙特卡罗应用程序
在DEEP项目中,正在设计和建造一个Exascale原型系统,该系统由一个标准的Intel Xeon集群与一个“Booster”部分组成,该部分包含连接在高速网络中的Intel Xeon Phi节点。为了评估这种预计在2015年下半年可用的新架构,计算科学和工程领域的一些重大挑战应用正在被修改和优化。在这项研究中,我们报告了Cineca项目合作伙伴和DEEP支持人员为实现其中一个应用程序所做的努力,TurboRVB量子蒙特卡罗模拟程序可用于研究材料中的复杂现象,如超导性。修改后的代码基于OmpSs卸载任务模型的实现,已经在巴塞罗那超级计算中心的MareNostrum超级计算机上成功地进行了测试。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
自引率
0.00%
发文量
0
期刊最新文献
Transient performance evaluation of cloud computing applications and dynamic resource control in large-scale distributed systems A security framework for population-scale genomics analysis Deep learning with shallow architecture for image classification A new reality requiers new ecosystems Investigation of DVFS based dynamic reliability management for chip multiprocessors
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1