2012 SC Companion: High Performance Computing, Networking Storage and Analysis最新文献_第3页

A Static Binary Instrumentation Threading Model for Fast Memory Trace Collection 用于快速内存跟踪收集的静态二进制检测线程模型

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.101

M. Laurenzano, Joshua Peraza, L. Carrington, Ananta Tiwari, W. A. Ward, R. Campbell

{"title":"A Static Binary Instrumentation Threading Model for Fast Memory Trace Collection","authors":"M. Laurenzano, Joshua Peraza, L. Carrington, Ananta Tiwari, W. A. Ward, R. Campbell","doi":"10.1109/SC.Companion.2012.101","DOIUrl":"https://doi.org/10.1109/SC.Companion.2012.101","url":null,"abstract":"In order to achieve a high level of performance, data intensive applications such as the real-time processing of surveillance feeds from unmanned aerial vehicles will require the strategic application of multi/many-core processors and coprocessors using a hybrid of inter-process message passing (e.g. MPI and SHMEM) and intra-process threading (e.g. pthreads and OpenMP). To facilitate program design decisions, memory traces gathered through binary instrumentation can be used to understand the low-level interactions between a data intensive code and the memory subsystem of a multi-core processor or many-core co-processor. Toward this end, this paper introduces the addition of threading support for PMaCs Efficient Binary Instrumentation Toolkit for Linux/x86 (PEBIL) and compares PEBILs threading model to the threading models of two other popular Linux/x86 binary instrumentation platforms - Pin and Dyninst - on both theoretical and empirical grounds. The empirical comparisons are based on experiments which collect memory address traces for the OpenMP-threaded implementations of the NASA Advanced Supercomputing Parallel Benchmarks (NPBs). This work shows that the overhead of collecting full memory address traces for multithreaded programs is higher in PEBIL (7.7x) than in Pin (4.7x), both of which are significantly lower than Dyninst (897x). This work also shows that PEBIL, uniquely, is able to take advantage of interval-based sampling of a memory address trace by rapidly disabling and re-enabling instrumentation at the transitions into and out of sampling periods in order to achieve significant decreases in the overhead of memory address trace collection. For collecting the memory address streams of each of the NPBs at a 10% sampling rate, PEBIL incurs an average slowdown of 2.9x compared to 4.4x with Pin and 897x with Dyninst.","PeriodicalId":6346,"journal":{"name":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","volume":"584 1","pages":"741-745"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"77215158","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: 7

Scalable Multi-Instance Learning Approach for Mapping the Slums of the World 绘制世界贫民窟地图的可扩展多实例学习方法

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.117

Ranga Raju Vatsavai

引用次数: 5

Performance and Power Characteristics of Matrix Multiplication Algorithms on Multicore and Shared Memory Machines 矩阵乘法算法在多核和共享存储器上的性能和功耗特性

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.87

Yonghong Yan, J. Kemp, Xiaonan Tian, A. Malik, B. Chapman

{"title":"Performance and Power Characteristics of Matrix Multiplication Algorithms on Multicore and Shared Memory Machines","authors":"Yonghong Yan, J. Kemp, Xiaonan Tian, A. Malik, B. Chapman","doi":"10.1109/SC.Companion.2012.87","DOIUrl":"https://doi.org/10.1109/SC.Companion.2012.87","url":null,"abstract":"For many scientific applications, dense matrix multiplication is one of the most important and computation intensive linear algebra operations. An efficient matrix multiplication on high performance and parallel computers requires optimizations on how matrices are decomposed and exchanged between computational nodes to reduce communication and synchronization overhead, as well as to efficiently exploit the memory hierarchy within a node to improve both spatial and temporal data locality. In this paper, we presented our studies of performance, cache behavior, and energy efficiency of multiple parallel matrix multiplication algorithms on a multicore desktop computer and a medium-size shared memory machine, both being considered as referenced sizes of nodes to create a medium- and largescale computational clusters for high performance computing used in industry and national laboratories. Our results highlight both the performance and energy efficiencies, and also provide implications on the memory and resources pressures of those algorithms. We hope this could help users choose the appropriate implementations according to their specific data sets when composing larger-scale scientific applications that use parallel matrix multiplication kernels on a node.","PeriodicalId":6346,"journal":{"name":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","volume":"56 1","pages":"626-632"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"89024911","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

Experiences with OpenMP, PGI, HMPP and OpenACC Directives on ISO/TTI Kernels 具有在ISO/TTI内核上使用OpenMP, PGI, HMPP和OpenACC指令的经验

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.95

Sayan Ghosh, Terrence Liao, H. Calandra, B. Chapman

{"title":"Experiences with OpenMP, PGI, HMPP and OpenACC Directives on ISO/TTI Kernels","authors":"Sayan Ghosh, Terrence Liao, H. Calandra, B. Chapman","doi":"10.1109/SC.Companion.2012.95","DOIUrl":"https://doi.org/10.1109/SC.Companion.2012.95","url":null,"abstract":"GPUs are slowly becoming ubiquitous devices in High Performance Computing, as their capabilities to enhance the performance per watt of compute intensive algorithms as compared to multicore CPUs have been identified. The primary shortcoming of a GPU is usability, since vendor specific APIs are quite different from existing programming languages, and it requires a substantial knowledge of the device and programming interface to optimize applications. Hence, lately a growing number of higher level programming models are targeting GPUs to alleviate this problem. The ultimate goal for a high-level model is to expose an easy-to-use interface for the user to offload compute intensive portions of code (kernels) to the GPU, and tune the code according to the target accelerator to maximize overall performance with a reduced development effort. In this paper, we share our experiences of three of the notable high-level directive based GPU programming models - PGI, CAPS and OpenACC (from CAPS and PGI) on an Nvidia M2090 GPU. We analyze their performance and programmability against Isotropic (ISO)/Tilted Transversely Isotropic (TTI) finite difference kernels, which are primary components in the Reverse Time Migration (RTM) application used by oil and gas exploration for seismic imaging of the sub-surface. When ported to a single GPU using the mentioned directives, we observe an average 1.5-1.8x improvement in performance for both ISO and TTI kernels, when compared with optimized multi-threaded CPU implementations using OpenMP.","PeriodicalId":6346,"journal":{"name":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","volume":"1 1","pages":"691-700"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90149910","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: 17

Abstract: Cascaded TCP: BIG Throughput for BIG DATA Applications in Distributed HPC 摘要:级联TCP:分布式高性能计算中大数据应用的大吞吐量

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.229

Umar Kalim, M. Gardner, Eric J. Brown, Wu-chun Feng

引用次数: 2

Poster: GPU Accelerated Ultrasonic Tomography Using Propagation and Backpropagation Method 海报:使用传播和反向传播方法的GPU加速超声断层扫描

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.249

P. Bello, Yuanwei Jin, E. Lu

引用次数: 2

A Network-Aware Object Storage Service 支持网络感知的对象存储服务

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.79

Shigetoshi Yokoyama, Nobukazu Yoshioka, Motonobu Ichimura

引用次数: 3

Abstract: Advances in Gyrokinetic Particle in Cell Simulation for Fusion Plasmas to Extreme Scale 摘要:陀螺动力学粒子在极端尺度聚变等离子体细胞模拟中的研究进展

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.243

Bei Wang, S. Ethier, W. Tang, K. Ibrahim, Kamesh Madduri, Samuel Williams, L. Oliker, T. Williams

{"title":"Abstract: Advances in Gyrokinetic Particle in Cell Simulation for Fusion Plasmas to Extreme Scale","authors":"Bei Wang, S. Ethier, W. Tang, K. Ibrahim, Kamesh Madduri, Samuel Williams, L. Oliker, T. Williams","doi":"10.1109/SC.Companion.2012.243","DOIUrl":"https://doi.org/10.1109/SC.Companion.2012.243","url":null,"abstract":"The Gyrokinetic Particle-in-cell (PIC) method has been successfully applied in studies of low-frequency microturbulence in magnetic fusion plasmas. While the excellent scaling of PIC codes on modern computing platforms is well established, significant challenges remain in achieving high on-chip concurrency for the new path to exascale systems. In addressing associated issues, it is necessary to deal with the basic gather-scatter operation and the relatively low computational intensity in the PIC method. Significant advancements have been achieved in optimizing gather-scatter operations in the gyrokinetic PIC method for next-generation multi-core CPU and GPU architectures. In particular, we will report on new techniques that improve locality, reduce memory conflict, and efficiently utilize shared memory on GPU's. Performance benchmarks on two high-end computing platforms -- the IBM BlueGene/Q (Mira) system at the Argonne Leadership Computing Facility (ALCF) and the Cray XK6 (Titan Dev) with the latest GPU at Oak Ridge Leadership Computing Facility (OLCF) - will be presented.","PeriodicalId":6346,"journal":{"name":"2012 SC Companion: High Performance Computing, Networking Storage and Analysis","volume":"15 1","pages":"1439-1440"},"PeriodicalIF":0.0,"publicationDate":"2012-11-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90656932","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

Abstract: An Exascale Workload Study 摘要:百亿亿次负载研究

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.261

Prasanna Balaprakash, Darius Buntinas, Anthony Chan, Apala Guha, Rinku Gupta, S. Narayanan, A. Chien, P. Hovland, B. Norris

引用次数: 2

A Parallel Unstructured Mesh Infrastructure 并行非结构化网格基础设施

2012 SC Companion: High Performance Computing, Networking Storage and Analysis Pub Date : 2012-11-10 DOI: 10.1109/SC.Companion.2012.135

E. Seol, Cameron W. Smith, D. Ibanez, M. Shephard

引用次数: 13