2019 International Conference on High Performance Computing & Simulation (HPCS)最新文献_第7页

Towards Runtime Analytics in a Parallel Performance System 面向并行性能系统的运行时分析

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188097

A. Malony, Srinivasan Ramesh, K. Huck, Chad Wood, S. Shende

引用次数: 0

New CAD Tools to ConFigure Tree-Based Embedded FPGA 新的CAD工具配置基于树的嵌入式FPGA

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188201

Hajer Saidi, M. Turki, Z. Marrakchi, M. Saleh, M. Abid

引用次数: 5

Performance Analysis of Compressed Batch Matrix Operations on Small Matrices 小矩阵上压缩批处理矩阵运算的性能分析

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188206

B. Gravelle, B. Norris

引用次数: 0

Miniaturized Implantable Coplanar Waveguide Antenna for Biomedical Applications 生物医学应用的微型植入式共面波导天线

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188130

A. Damaj, H. M. E. Misilmani, S. A. Chahine

引用次数: 1

Staged deployment of interactive multi-application HPC workflows 交互式多应用程序HPC工作流的阶段部署

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188104

W. Klijn, Sandra Díaz-Pier, A. Morrison, A. Peyser

{"title":"Staged deployment of interactive multi-application HPC workflows","authors":"W. Klijn, Sandra Díaz-Pier, A. Morrison, A. Peyser","doi":"10.1109/HPCS48598.2019.9188104","DOIUrl":"https://doi.org/10.1109/HPCS48598.2019.9188104","url":null,"abstract":"Running scientific workflows on a supercomputer can be a daunting task for a scientific domain specialist. Workflow management solutions (WMS) are a standard method for reducing the complexity of application deployment on high performance computing (HPC) infrastructure. We introduce the design for a middleware system that extends and combines the functionality from existing solutions in order to create a high-level, staged usercentric operation/deployment model. This design addresses the requirements of several use cases in the life sciences, with a focus on neuroscience. In this manuscript we focus on two use cases: 1) three coupled neuronal simulators (for three different space/time scales) with in-transit visualization and 2) a closed-loop workflow optimized by machine learning, coupling a robot with a neural network simulation. We provide a detailed overview of the application-integrated monitoring in relationship with the HPC job. We present here a novel usage model for large scale interactive multi-application workflows running on HPC systems which aims at reducing the complexity of deployment and execution, thus enabling new science.","PeriodicalId":371856,"journal":{"name":"2019 International Conference on High Performance Computing & Simulation (HPCS)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"115829708","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: 2

Weighted Two-Levels Secret Sharing Scheme for Multi-Clouds Data Storage with Increased Reliability 提高可靠性的多云数据存储加权两级秘密共享方案

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188057

Vanessa Miranda-López, Andrei Tchernykh, M. Babenko, Viktor Andreevich Kuchukov, M. Deryabin, E. Golimblevskaia, Egor Shiryaev, A. Avetisyan, R. Rivera-Rodríguez, G. Radchenko, E. Talbi

{"title":"Weighted Two-Levels Secret Sharing Scheme for Multi-Clouds Data Storage with Increased Reliability","authors":"Vanessa Miranda-López, Andrei Tchernykh, M. Babenko, Viktor Andreevich Kuchukov, M. Deryabin, E. Golimblevskaia, Egor Shiryaev, A. Avetisyan, R. Rivera-Rodríguez, G. Radchenko, E. Talbi","doi":"10.1109/HPCS48598.2019.9188057","DOIUrl":"https://doi.org/10.1109/HPCS48598.2019.9188057","url":null,"abstract":"Cloud storages delivered as services are made available to the general public. As cloud storage provider prices become low, they have moved into the mainstream of storage technology. However, there are various factors that cause many potential users do not use it intensively. There exist high risks for confidentiality, integrity, and availability violation associated with the loss of information, denial of access, technical failures, etc. In this article, we propose a two-level secret sharing scheme (TL-SSS) based on a residue number system (RNS) for a configurable, reliable, and secure distributed data storage. RNS moduli of a special type increase the reliability of the data storage system and reduce the computational complexity of the data encoding and decoding from linear-logarithmic to linear. TL-SSS is the weighted data access structure. It creates and distributes data shares according to the characteristics of the cloud storages under various scenarios. We provide a solution that improves system reliability without reduction of the security level. In contrast to classical solutions, it can restore the data with less available shares than the state-of-the-art approaches.","PeriodicalId":371856,"journal":{"name":"2019 International Conference on High Performance Computing & Simulation (HPCS)","volume":"8 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124369922","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: 2

Towards Co-execution on Commodity Heterogeneous Systems: Optimizations for Time-Constrained Scenarios 面向商品异构系统的协同执行:时间约束场景的优化

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188188

Raúl Nozal, J. L. Bosque, R. Beivide

{"title":"Towards Co-execution on Commodity Heterogeneous Systems: Optimizations for Time-Constrained Scenarios","authors":"Raúl Nozal, J. L. Bosque, R. Beivide","doi":"10.1109/HPCS48598.2019.9188188","DOIUrl":"https://doi.org/10.1109/HPCS48598.2019.9188188","url":null,"abstract":"Heterogeneous systems are present from powerful supercomputers, to mobile devices, including desktop computers, thanks to their excellent performance and energy consumption. The ubiquity of these architectures in both desktop systems and medium-sized service servers allow enough variability to exploit a wide range of problems, such as multimedia workloads, video encoding, image filtering and inference in machine learning. Due to the heterogeneity, some efforts have been done to reduce the programming effort and preserve performance portability, but these systems include a set of challenges. The context in which applications offload the workload along with the management overheads introduced when doing co-execution, penalize the performance gains under time-constrained scenarios. Therefore, this paper proposes optimizations for the EngineCL runtime to reduce the penalization when co-executing in commodity systems, as well as algorithmic improvements when load balancing. An exhaustive experimental evaluation is performed, showing optimization improvements of 7.5% and 17.4% for binary and ROI-based offloading modes, respectively. Thanks to all the optimizations, the new load balancing algorithm is always the most efficient scheduling configuration, achieving an average efficiency of 0.84 under a pessimistic scenario.","PeriodicalId":371856,"journal":{"name":"2019 International Conference on High Performance Computing & Simulation (HPCS)","volume":"71 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124617289","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: 4

Performance Counters based Power Modeling of Mobile GPUs using Deep Learning 基于性能计数器的深度学习移动gpu功耗建模

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188139

Nadjib Mammeri, Markus Neu, S. Lal, B. Juurlink

{"title":"Performance Counters based Power Modeling of Mobile GPUs using Deep Learning","authors":"Nadjib Mammeri, Markus Neu, S. Lal, B. Juurlink","doi":"10.1109/HPCS48598.2019.9188139","DOIUrl":"https://doi.org/10.1109/HPCS48598.2019.9188139","url":null,"abstract":"GPUs have recently become important computational units on mobile devices, resulting in heterogeneous devices that can run a variety of parallel processing applications. While developing and optimizing such applications, estimating power consumption is of immense importance as energy efficiency has become the key design constraint to optimize for on these platforms. In this work, we apply deep learning techniques in building a predictive model for estimating power consumption of parallel applications on a heterogeneous mobile SoC. Our model is an artificial neural network (NN) trained using CPU and GPU hardware performance counters along with measured power data. The model is trained and evaluated with data collected using a set of graphics OpenGL workloads as well as OpenCL compute benchmarks. Our evaluations show that our model can achieve accurate power estimates with a mean relative error of 4.47% with respect to real power measurements. When compared to other models, our NN model is about 3.3x better than a statistical linear regression model and 2x better than a state-of-the-art NN model.","PeriodicalId":371856,"journal":{"name":"2019 International Conference on High Performance Computing & Simulation (HPCS)","volume":"29 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114718804","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: 2

A Weighted Centroid Localization Algorithm for Wireless Sensor Networks 一种无线传感器网络加权质心定位算法

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188226

Abdelali Hadir, K. Zine-dine, M. Bakhouya

引用次数: 1

Auto-tuning methodology for configuration and application parameters of hybrid CPU + GPU parallel systems based on expert knowledge 基于专家知识的CPU + GPU混合并行系统配置与应用参数自动调优方法

2019 International Conference on High Performance Computing & Simulation (HPCS) Pub Date : 2019-07-01 DOI: 10.1109/HPCS48598.2019.9188060

P. Czarnul, P. Rosciszewski

{"title":"Auto-tuning methodology for configuration and application parameters of hybrid CPU + GPU parallel systems based on expert knowledge","authors":"P. Czarnul, P. Rosciszewski","doi":"10.1109/HPCS48598.2019.9188060","DOIUrl":"https://doi.org/10.1109/HPCS48598.2019.9188060","url":null,"abstract":"Auto-tuning of configuration and application parameters allows to achieve significant performance gains in many contemporary compute-intensive applications. Feasible search spaces of parameters tend to become too big to allow for exhaustive search in the auto-tuning process. Expert knowledge about the utilized computing systems becomes useful to prune the search space and new methodologies are needed in the face of emerging heterogeneous computing architectures. In this paper we propose an auto-tuning methodology for hybrid CPU/GPU applications that takes into account previous execution experiences, along with an automated tool for iterative testing of chosen combinations of configuration, as well as application-related parameters. Experimental results, based on a parallel similarity search application executed on three different CPU + GPU parallel systems, show that the proposed methodology allows to achieve execution times worse by only up to 8% compared to a search algorithm that performs a full search over combinations of application parameters, while taking only up to 26% time of the latter.","PeriodicalId":371856,"journal":{"name":"2019 International Conference on High Performance Computing & Simulation (HPCS)","volume":"143 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2019-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122183389","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: 4