2020 IEEE High Performance Extreme Computing Conference (HPEC)最新文献_第3页

Bandwidth Allocation in Silicon-Photonic Networks Using Application Instrumentation 基于应用仪器的硅光子网络带宽分配

2020 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2020-09-22 DOI: 10.1109/HPEC43674.2020.9286151

A. Narayan, A. Joshi, A. Coskun

引用次数: 0

Energy-Efficient Analysis of Synchrophasor Data using the NVIDIA Jetson Nano 使用NVIDIA Jetson Nano的同步相量数据的节能分析

2020 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2020-09-22 DOI: 10.1109/HPEC43674.2020.9286226

Suzanne J. Matthews, A. S. Leger

{"title":"Energy-Efficient Analysis of Synchrophasor Data using the NVIDIA Jetson Nano","authors":"Suzanne J. Matthews, A. S. Leger","doi":"10.1109/HPEC43674.2020.9286226","DOIUrl":"https://doi.org/10.1109/HPEC43674.2020.9286226","url":null,"abstract":"Smart Grid Technology is an important part of increasing resilience and reliability of power grids. Applying Phasor Measurement Units (PMUs) to obtain synchronized phasor measurements, or synchrophasors, provides more detailed, higher fidelity data that can enhance situational awareness by rapidly detecting anomalous conditions. However, sample rates of PMUs are up to three orders of magnitude faster than traditional telemetry, resulting in large datasets that require novel computing methods to process the data quickly and efficiently. This work aims to improve calculation speed and energy efficiency of anomaly detection by leveraging manycore computing on a NVIDIA Jetson Nano. This work translates an existing PMU anomaly detection scheme into a novel GPU-compute algorithm and compares the computational performance and energy efficiency of the GPU approach to serial and multicore CPU methods. The GPU algorithm was benchmarked on a real dataset of 11.3 million measurements derived from 8 PMUs from a 1:1000 scale emulation of a power grid, and two additional datasets derived from the original dataset. Results show that the GPU detection scheme is up to 51.91 times faster than the serial method, and over 13 times faster than the multicore method. Additionally, the GPU approach exhibits up to 92.3% run-time energy reduction compared to serial method and 78.4% reduction compared to the multicore approach.","PeriodicalId":168544,"journal":{"name":"2020 IEEE High Performance Extreme Computing Conference (HPEC)","volume":"13 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128063507","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: 0

TriC: Distributed-memory Triangle Counting by Exploiting the Graph Structure 利用图结构的分布式内存三角形计数

2020 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2020-09-22 DOI: 10.1109/HPEC43674.2020.9286167

Sayan Ghosh, M. Halappanavar

{"title":"TriC: Distributed-memory Triangle Counting by Exploiting the Graph Structure","authors":"Sayan Ghosh, M. Halappanavar","doi":"10.1109/HPEC43674.2020.9286167","DOIUrl":"https://doi.org/10.1109/HPEC43674.2020.9286167","url":null,"abstract":"Graph analytics has emerged as an important tool in the analysis of large scale data from diverse application domains such as social networks, cyber security and bioinformatics. Counting the number of triangles in a graph is a fundamental kernel with several applications such as detecting the community structure of a graph or in identifying important vertices in a graph. The ubiquity of massive datasets is driving the need to scale graph analytics on parallel systems. However, numerous challenges exist in efficiently parallelizing graph algorithms, especially on distributed-memory systems. Irregular memory accesses and communication patterns, low computation to communication ratios, and the need for frequent synchronization are some of the leading challenges. In this paper, we present TriC, our distributed-memory implementation of triangle counting in graphs using the Message Passing Interface (MPI), as a submission to the 2020 Graph Challenge competition. Using a set of synthetic and real-world inputs from the challenge, we demonstrate a speedup of up to 90 x relative to previous work on 32 processor-cores of a NERSC Cori node. We also provide details from distributed runs with up to 8192 processes along with strong scaling results. The observations presented in this work provide an understanding of the system-level bottlenecks at scale that specifically impact sparse-irregular workloads and will therefore benefit other efforts to parallelize graph algorithms.","PeriodicalId":168544,"journal":{"name":"2020 IEEE High Performance Extreme Computing Conference (HPEC)","volume":"84 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133799184","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: 16

Execution of Complete Molecular Dynamics Simulations on Multiple FPGAs 在多个fpga上执行完整的分子动力学模拟

2020 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2020-09-22 DOI: 10.1109/HPEC43674.2020.9286155

C. Pascoe, Lawrence C. Stewart, B. W. Sherman, Vipin Sachdeva, Martin C. Herbordt

{"title":"Execution of Complete Molecular Dynamics Simulations on Multiple FPGAs","authors":"C. Pascoe, Lawrence C. Stewart, B. W. Sherman, Vipin Sachdeva, Martin C. Herbordt","doi":"10.1109/HPEC43674.2020.9286155","DOIUrl":"https://doi.org/10.1109/HPEC43674.2020.9286155","url":null,"abstract":"We have modified the open source molecular dynamics (MD) simulation code OpenMM [1] to add support for running complete MD timesteps on a cluster of FPGAs. The overall structure of the application is shown in Figure 1. MD proceeds by calculating forces on individual particles and integrating those forces to update velocities/positions on a per timestep basis. A variety of forces apply to each particle and we subdivide them into three categories based on the computation requirements: range limited (RL), long range (LR), and bonded. RL interactions comprise Lennard Jones and electrostatic forces between all particle pairs within a radial cutoff. LR interactions comprise electrostatic forces beyond the RL cutoff, where pairwise computation would be too costly. We calculate LR forces using the Smooth Particle Mesh Ewald (PME) method, which uses 3D Fast Fourier Transforms (FFTs) to accelerate computation. Bonded interactions are the focus of future work. Kernels are coded in OpenCL for ease of hardware development and application integration. The design uses a mix of fixedpoint and single-/double-precision floating-point arithmetic where needed to maintain the same level of accuracy as CPU and GPU implementations. The ultimate goal of this project is to perform MD simulation of biologically-relevant systems within the context of drug discovery (i.e., periodic systems of 50,000–100,000 particles with approximate density of 1 atom per 10 cubic Å) with strong scaling performance greater than possible with other technologies such as GPUs.","PeriodicalId":168544,"journal":{"name":"2020 IEEE High Performance Extreme Computing Conference (HPEC)","volume":"28 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123419110","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: 5

Human balance models optimized using a large-scale, parallel architecture with applications to mild traumatic brain injury 使用大规模并行架构优化人体平衡模型，并应用于轻度创伤性脑损伤

2020 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2020-09-22 DOI: 10.1109/HPEC43674.2020.9286217

G. Ciccarelli, Michael Nolan, H. Rao, Tanya Talkar, A. O'Brien, G. Vergara-Diaz, R. Zafonte, T. Quatieri, R. McKindles, P. Bonato, A. Lammert

{"title":"Human balance models optimized using a large-scale, parallel architecture with applications to mild traumatic brain injury","authors":"G. Ciccarelli, Michael Nolan, H. Rao, Tanya Talkar, A. O'Brien, G. Vergara-Diaz, R. Zafonte, T. Quatieri, R. McKindles, P. Bonato, A. Lammert","doi":"10.1109/HPEC43674.2020.9286217","DOIUrl":"https://doi.org/10.1109/HPEC43674.2020.9286217","url":null,"abstract":"Static and dynamic balance are frequently disrupted through brain injuries. The impairment can be complex and for mild traumatic brain injury (mTBI) can be undetectable by standard clinical tests. Therefore, neurologically relevant modeling approaches are needed for detection and inference of mechanisms of injury. The current work presents models of static and dynamic balance that have a high degree of correspondence. Emphasizing structural similarity between the domains facilitates development of both. Furthermore, particular attention is paid to components of sensory feedback and sensory integration to ground mechanisms in neurobiology. Models are adapted to fit experimentally collected data from 10 healthy control volunteers and 11 mild traumatic brain injury volunteers. Through an analysis by synthesis approach whose implementation was made possible by a state-of-the-art high performance computing system, we derived an interpretable, model based feature set that could classify mTBI and controls in a static balance task with an ROC AUC of 0.72.","PeriodicalId":168544,"journal":{"name":"2020 IEEE High Performance Extreme Computing Conference (HPEC)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-09-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128466328","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 a Distributed Framework for Multi-Agent Reinforcement Learning Research 多智能体强化学习的分布式框架研究

2020 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2020-09-22 DOI: 10.1109/HPEC43674.2020.9286212

Yutai Zhou, Shawn Manuel, Peter Morales, Sheng Li, Jaime Peña, R. Allen

引用次数: 0

Incremental Streaming Graph Partitioning 增量流图分区

2020 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2020-09-22 DOI: 10.1109/HPEC43674.2020.9286181

L. Durbeck, P. Athanas

引用次数: 2

A Dynamically Configurable Network for Software-Defined Hardware 软件定义硬件的动态可配置网络

2020 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2020-09-22 DOI: 10.1109/HPEC43674.2020.9286148

William Butera

引用次数: 0

Denial of Service in CPU-GPU Heterogeneous Architectures CPU-GPU异构架构中的拒绝服务

2020 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2020-09-22 DOI: 10.1109/HPEC43674.2020.9286228

Hao Wen, W. Zhang

引用次数: 0

OpenCL Performance on the Intel Heterogeneous Architecture Research Platform Intel异构架构研究平台上的OpenCL性能

2020 IEEE High Performance Extreme Computing Conference (HPEC) Pub Date : 2020-09-22 DOI: 10.1109/HPEC43674.2020.9286213

Steven Harris, R. Chamberlain, Christopher D. Gill

引用次数: 4