2012 SC Companion: High Performance Computing, Networking Storage and Analysis最新文献_第7页

DS-CUDA: A Middleware to Use Many GPUs in the Cloud Environment DS-CUDA:在云环境中使用多个gpu的中间件

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.146

Minoru Oikawa, A. Kawai, K. Nomura, K. Yasuoka, Kazuyuki Yoshikawa, T. Narumi

{"title":"DS-CUDA: A Middleware to Use Many GPUs in the Cloud Environment","authors":"Minoru Oikawa, A. Kawai, K. Nomura, K. Yasuoka, Kazuyuki Yoshikawa, T. Narumi","doi":"10.1109/SC.Companion.2012.146","DOIUrl":"https://doi.org/10.1109/SC.Companion.2012.146","url":null,"abstract":"GPGPU (General-purpose computing on graphics processing units) has several difficulties when used in cloud environment, such as narrow bandwidth, higher cost, and lower security, compared with computation using only CPUs. Most high performance computing applications require huge communication between nodes, and do not fit a cloud environment, since network topology and its bandwidth are not fixed and they affect the performance of the application program. However, there are some applications for which little communication is needed, such as molecular dynamics (MD) simulation with the replica exchange method (REM). For such applications, we propose DS-CUDA (Distributed-shared compute unified device architecture), a middleware to use many GPUs in a cloud environment with lower cost and higher security. It virtualizes GPUs in a cloud such that they appear to be locally installed GPUs in a client machine. Its redundant mechanism ensures reliable calculation with consumer GPUs, which reduce the cost greatly. It also enhances the security level since no data except command and data for GPUs are stored in the cloud side. REM-MD simulation with 64 GPUs showed 58 and 36 times more speed than a locally-installed GPU via InfiniBand and the Internet, respectively.","PeriodicalId":6346,"journal":{"name":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","volume":"132 1","pages":"1207-1214"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"80011066","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 73

Explosive Charge Blowing a Hole in a Steel Plate Animation 炸药在钢板上炸出一个洞动画

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.364

Bradley Carvey, Nathan Fabian, D. Rogers

引用次数: 0

Poster: Numeric Based Ordering for Preconditioned Conjugate Gradient 海报:预条件共轭梯度的基于数值的排序

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.309

J. Booth

引用次数: 1

Load Balanced Parallel GPU Out-of-Core for Continuous LOD Model Visualization 负载均衡并行GPU核外连续LOD模型可视化

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.37

Chao Peng, Peng Mi, Yong Cao

引用次数: 5

Poster: Matrix Decomposition Based Conjugate Gradient Solver for Poisson Equation 海报:基于矩阵分解的泊松方程共轭梯度求解器

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.287

Hang Liu, J. Seo, R. Mittal

引用次数: 3

High Performance Implementation of an Econometrics and Financial Application on GPUs 基于gpu的计量经济学和金融应用的高性能实现

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.138

M. Creel, M. Zubair

引用次数: 14

A Python HPC Framework: PyTrilinos, ODIN, and Seamless Python高性能计算框架:PyTrilinos, ODIN和Seamless

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.83

K. W. Smith, W. Spotz, S. Ross-Ross

引用次数: 1

Towards Improving the Communication Performance of CRESTA's Co-Design Application NEK5000 提高CRESTA协同设计应用NEK5000的通信性能

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.92

Michael Schliephake, E. Laure

引用次数: 3

The PEPPHER Composition Tool: Performance-Aware Dynamic Composition of Applications for GPU-Based Systems PEPPHER组合工具:基于gpu系统的应用程序的性能感知动态组合

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.97

Usman Dastgeer, Lu Li, C. Kessler

{"title":"The PEPPHER Composition Tool: Performance-Aware Dynamic Composition of Applications for GPU-Based Systems","authors":"Usman Dastgeer, Lu Li, C. Kessler","doi":"10.1109/SC.Companion.2012.97","DOIUrl":"https://doi.org/10.1109/SC.Companion.2012.97","url":null,"abstract":"The PEPPHER component model defines an environment for annotation of native C/C++ based components for homogeneous and heterogeneous multicore and manycore systems, including GPU and multi-GPU based systems. For the same computational functionality, captured as a component, different sequential and explicitly parallel implementation variants using various types of execution units might be provided, together with metadata such as explicitly exposed tunable parameters. The goal is to compose an application from its components and variants such that, depending on the run-time context, the most suitable implementation variant will be chosen automatically for each invocation. We describe and evaluate the PEPPHER composition tool, which explores the application's components and their implementation variants, generates the necessary low-level code that interacts with the runtime system, and coordinates the native compilation and linking of the various code units to compose the overall application code. With several applications, we demonstrate how the composition tool provides a high-level programming front-end while effectively utilizing the task-based PEPPHER runtime system (StarPU) underneath.","PeriodicalId":6346,"journal":{"name":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","volume":"2 1","pages":"711-720"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"84090326","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 20

Towards Energy Efficient Data Intensive Computing Using IEEE 802.3az 使用IEEE 802.3az实现节能数据密集型计算

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.112

Dimitar Pavlov, Joris Soeurt, P. Grosso, Zhiming Zhao, K. V. D. Veldt, Hao Zhu, C. D. Laat

{"title":"Towards Energy Efficient Data Intensive Computing Using IEEE 802.3az","authors":"Dimitar Pavlov, Joris Soeurt, P. Grosso, Zhiming Zhao, K. V. D. Veldt, Hao Zhu, C. D. Laat","doi":"10.1109/SC.Companion.2012.112","DOIUrl":"https://doi.org/10.1109/SC.Companion.2012.112","url":null,"abstract":"Energy efficiency is an increasingly important requirement for computing and communication systems, especially with their increasing pervasiveness. The IEEE 802.3az protocol reduces the network energy consumption by turning active copper Ethernet links to a low power model when no traffic exists. However, the effect of 802.3az heavily depends on the network traffic patterns which makes system level energy optimization challenging. In clusters, distributed data intensive applications that generate heavy network traffic are common, and in turn the required network devices can consume large amounts of energy. In this research, we examined the 802.3az technology with the goal of applying it in clusters. We defined an energy budget calculator that takes energy-efficient Ethernet into account by including the energy models derived from tests of 802.3az enabled devices. The calculator is an integral tool in a global strategy to optimize the energy usage of applications in a high performance computing environment. We show a few practical examples of how real applications can better plan their execution by integrating this knowledge in their decision strategies.","PeriodicalId":6346,"journal":{"name":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","volume":"1 1","pages":"806-810"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86001164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 3