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Empirical Autotuning of Two-level parallel Linear Algebra Routines on Large cc-NUMA Systems

Empirical Autotuning of Two-level Parallel Linear Algebra Ro...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： Jesús C'mara Javier Cuenca Domingo Giménez Antonio M. Vidal Universidad de Murcia Murcia Murcia ES Department of Engineere and Technology of Computers University of Murcia Murcia Spain Department of Informatic and Systems University of Murcia Murcia Spain Department of Informatic Systems and Computing Polytechnic University of Valencia Valencia Spain

In large cc-NUMA systems the efficient use of the different levels of the memory hierarchy is not an easy task, and the performance of multithreading implementations of the libraries decreases when the number of cores used increases, so producing an important lost of efficiency. To alleviate this problem, routines with multilevel parallelism can be developed by combining OpenMP and BLAS parallelism. In that way, higher performance can be achieved, but it is necessary to develop some autotuning technique for the appropriate selection of the number of threads to use at each level. The selection can be made through theoretical models of the execution time or some installation methodology. This work analyses some installation techniques for a two-level matrix multiplication routine, with the aim of developing a valid methodology for other linear algebra routines in large cc-NUMA systems. The basic ideas of the two-level parallelisation and the installation methodology are discussed and some experimental results are commented on.

关键词： Instruction sets Linear algebra Libraries Educational institutions Multithreading Prototypes

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A parallel Packet Processing Runtime System On Multi-Core Network Processors

A Parallel Packet Processing Runtime System On Multi-Core Ne...

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The Eleventh international symposium on distributed computing and Applications to Business,Engineering and Science(DCABES2012)(第十一届分布式计算及其应用国际学术研讨会)(原名:电子商务、工程及科学领域的分布式计算和应用国际学术研讨会)

作者： Yunchun Li Lianqiang Shan Xinxin Qiao Beijing Key laboratory of network technology Computer Science Department BeiHang University BeijingChina

More and more network devices and chips commonly apply multi-core architectures to meet increasingly performance *** the lack of efficient program level parallelism and workload allocation in the packet processing system greatly limits the utilization of multi-core *** this paper,we propose a parallel packet processing runtime system and explore an affinity-based packet scheduler with the goal of raising load balancing and decreasing cache *** can use the system that handles the allocation of processing tasks to simplify the implementation of new *** experiment results show task distributor and scheduler can achieve a better compromise between load balancing and cache affinity in the parallel packet processing system.

关键词： multi-core load banlance parallel packet process

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Sparse Matrix-vector Multiplication on GPGPU Clusters: A New Storage Format and a Scalable Implementation

Sparse Matrix-vector Multiplication on GPGPU Clusters: A New...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Moritz Kreutzer Georg Hager Gerhard Wellein Holger Fehske Achim Basermann Alan R. Bishop Erlangen Regional Computing Center Erlangen Germany Ernst Moritz Arndt University슠of Greifswald Greifswald Germany Simulation and Software Technology German AeroSpace Center (DLR) Cologne Germany Theory Simulation and Computation Directorate Los Alamos National Laboratory Los Alamos NM USA

ISBN: (纸本)9781467309745

Sparse matrix-vector multiplication (spMVM) is the dominant operation in many sparse solvers. We investigate performance properties of spMVM with matrices of various sparsity patterns on the nVidia "Fermi" class of GPGPUs. A new "padded jagged diagonals storage" (pJDS) format is proposed which may substantially reduce the memory overhead intrinsic to the widespread ELLPACK-R scheme while making no assumptions about the matrix structure. In our test scenarios the pJDS format cuts the overall spMVM memory footprint on the GPGPU by up to 70%, and achieves 91% to 130% of the ELLPACK-R performance. Using a suitable performance model we identify performance bottlenecks on the node level that invalidate some types of matrix structures for efficient multi-GPGPU parallelization. For appropriate sparsity patterns we extend previous work on distributed-memory parallel spMVM to demonstrate a scalable hybrid MPI-GPGPU code, achieving efficient overlap of communication and computation.

关键词： Instruction sets Sparse matrices Vectors Bandwidth Error correction codes Kernel Computational modeling

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Scientific Application Performance on HPC, Private and Public Cloud Resources: A Case Study Using Climate, Cardiac Model Codes and the NPB Benchmark Suite

Scientific Application Performance on HPC, Private and Publi...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Peter E. Strazdins Jie Cai Muhammad Atif Joseph Antony Research School of Computer Science Australian National University Canberra ACT Australia National Computational Infrastructure Australian National University Canberra ACT Australia

ISBN: (纸本)9781467309745

The ubiquity of on-demand cloud computing resources enables scientific researchers to dynamically provision and consume compute and storage resources in response to science needs. Whereas traditional HPC compute resources are often centrally managed with a priori CPU-time allocations and use policies. A long term goal of our work is to assess the efficacy of preserving the user environment (compilers, support libraries, runtimes and application codes) available at a traditional HPC facility for deployment into a VM environment, which can then be subsequently used in both private and public scientific clouds. This would afford greater flexibility to users in choosing hardware resources that suit their science needs better, as well as aiding them in transitioning onto private/public cloud resources. In this paper we present work in-progress performance results for a set of benchmark kernels and scientific applications running in a traditional HPC environment, a private VM cluster and an Amazon HPC EC2 cluster. These are the OSU MPI micro-benchmark, the NAS parallel macro-benchmarks and two large scientific application codes (the UK Met Office's MetUM global climate model and the Chaste multi-scale computational biology code) respectively. We discuss parallel scalability and runtime information obtained using the IPM performance monitoring framework for MPI applications. We were also able to successfully build application codes in a traditional HPC environment and package these into VMs which ran on both private and public cloud resources.

关键词： Benchmark testing Cloud computing Virtual machine monitors Bandwidth Hardware Clouds Computational modeling

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Heterogeneous Task Scheduling for Accelerated OpenMP

Heterogeneous Task Scheduling for Accelerated OpenMP

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international symposium on parallel and distributed Processing (IPDPS)

作者： Thomas R.W. Scogland Barry Rountree Wu-chun Feng Bronis R. de Supinski Department of Computer Science Virginia Polytechnic Institute and State University Blacksburg VA USA Center for Applied Scientific Computing Lawrence Livemore National Laboratory Livermore CA USA

Heterogeneous systems with CPUs and computational accelerators such as GPUs, FPGAs or the upcoming Intel MIC are becoming mainstream. In these systems, peak performance includes the performance of not just the CPUs but also all available accelerators. In spite of this fact, the majority of programming models for heterogeneous computing focus on only one of these. With the development of Accelerated Open MP for GPUs, both from PGI and Cray, we have a clear path to extend traditional Open MP applications incrementally to use GPUs. The extensions are geared toward switching from CPU parallelism to GPU parallelism. However they do not preserve the former while adding the latter. Thus computational potential is wasted since either the CPU cores or the GPU cores are left idle. Our goal is to create a runtime system that can intelligently divide an accelerated Open MP region across all available resources automatically. This paper presents our proof-of-concept runtime system for dynamic task scheduling across CPUs and GPUs. Further, we motivate the addition of this system into the proposed Open MP for Accelerators standard. Finally, we show that this option can produce as much as a two-fold performance improvement over using either the CPU or GPU alone.

关键词： Graphics processing unit Dynamic scheduling Integrated circuits Acceleration Schedules Programming Runtime

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Analysis and Optimization of Data Import with Hadoop

Analysis and Optimization of Data Import with Hadoop

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Weijia Xu Wei Luo Nicholas Woodward Texas Advanced Computing Center University of Texas Austin USA Beijing Document Service Beijing China School of Information University of Texas Austin USA

ISBN: (纸本)9781467309745

Data driven research has become an important part of scientific discovery in an increasing number of disciplines. In many cases, the sheer volume of data to be processed requires not only state-of-the-art computing resources but also carefully tuned and specifically developed software. These requirements are often associated with huge operational costs and significant expertise in software development. Due to its simplicity for the user and effectiveness at processing big data, Hadoop has become a popular software platform for large-scale data analysis. Using a Hadoop cluster in a remote shared infrastructure enables users to avoid the costs of maintaining a physical infrastructure. An inevitable step in using dynamically constructed Hadoop cluster is the initial importing of the data. This process is not trivial, particularly when the size of the data is large. In this paper, we evaluate the costs of importing large-scale data into a Hadoop cluster. We present a detailed analysis of the default data importing implementation in Hadoop and conduct a practical evaluation. Our evaluation includes tests with different hardware configurations, such as different network protocol and disk configurations. We also propose an implementation to improve the performance of importing data into a Hadoop cluster wherein the data is accessed directly by Data nodes during the import process.

关键词： Pipelines Sockets Data models File systems Throughput Hardware Computational modeling

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Multithreaded Algorithms for Maximum Matching in Bipartite Graphs

Multithreaded Algorithms for Maximum Matching in Bipartite G...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Ariful Azad Mahantesh Halappanavar Sivasankaran Rajamanickam Erik G. Boman Arif Khan Alex Pothen Purdue University USA Pacific Northwest National Laboratory USA Sandia National Laboratories USA

ISBN: (纸本)9781467309752

We design, implement, and evaluate algorithms for computing a matching of maximum cardinality in a bipartite graph on multicore and massively multithreaded computers. As computers with larger numbers of slower cores dominate the commodity processor market, the design of multithreaded algorithms to solve large matching problems becomes a necessity. Recent work on serial algorithms for the matching problem has shown that their performance is sensitive to the order in which the vertices are processed for matching. In a multithreaded environment, imposing a serial order in which vertices are considered for matching would lead to loss of concurrency and performance. But this raises the question: {\em Would parallel matching algorithms on multithreaded machines improve performance over a serial algorithm?}We answer this question in the affirmative. We report efficient multithreaded implementations of three classes of algorithms based on their manner of searching for augmenting paths: breadth-first-search, depth-first-search, and a combination of both. The Karp-Sipser initialization algorithm is used to make the parallel algorithms practical. We report extensive results and insights using three shared-memory platforms (a 48-core AMD Opteron, a 32-coreIntel Nehalem, and a 128-processor Cray XMT) on a representative set of real-world and synthetic graphs. To the best of our knowledge, this is the first study of augmentation-based parallel algorithms for bipartite cardinality matching that demonstrates good speedups on multithreaded shared memory multiprocessors.

关键词： Instruction sets parallel algorithms Algorithm design and analysis Multicore processing Bipartite graph Arrays Heuristic algorithms

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Fast Sparse Matrix Matrix Product Based on ELLR-T and GPU computing

Fast Sparse Matrix Matrix Product Based on ELLR-T and GPU Co...

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international symposium on parallel and distributed Processing with Applications, ISPA

作者： F. V'zquez G. Ortega J.J. Fern'ndez I. García E.M. Garzón Supercomputing and Algorithms Group Associated Unit CSIC-UAL University of Almería Almeria Spain Supercomputing and Algorithms Group Associated Unit CSIC-UAL Agrifood Campus of International Excellence (ceiA3) Ctra Almería Spain Dpt. of Comput. Archit Univ. of Málaga Málaga Spain

A wide range of applications in engineering and scientific computing are based on the computation of matrices products, where one of them is sparse. The computational requirements of these operations are very high when dimensions of the matrices increase. The goal of this work is the acceleration of the sparse matrix matrix product (SpMM) on Graphics Processing Units (GPUs). The operation SpMM can be computed by a set of sparse matrix vector operations (SpMV). However, this approach does not reach optimal performance because it cannot benefit from the large value of the ratio computation/memory access associated to the SpMM operation. In this work a routine called FastSpMM is described and its performance evaluated. FastSpMM can be considered as an extension of the ELLRT routine to compute SpMV on GPUs which is based on the ELLPACK-R storage format for sparse matrices. FastSpMM combines the high ratio computation/memory access with the advantages of ELLR-T to exploit the GPU architecture. The CUSPARSE library, supplied by NVIDIA, which also includes routines to compute SpMM on GPUs is used in this work as a reference for performance comparison. Experimental evaluations based on a representative set of test matrices show that FastSpMM outperforms the corresponding CUSPARSE routine in terms of performance.

关键词： Sparse matrices Graphics processing unit Instruction sets Computer architecture Libraries Acceleration Vectors

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Power-Efficient Schemes via Workload Characterization on the Intel's Single-Chip Cloud Computer

Power-Efficient Schemes via Workload Characterization on the...

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IEEE international symposium on parallel and distributed Processing Workshops and Phd Forum (IPDPSW)

作者： Gustavo A. Chaparro-Baquero Qi Zhou Chen Liu Jie Tang Shaoshan Liu Florida International University Miami FL USA Beijing Institute of Technology Beijing China Microsoft Redmond WA USA

ISBN: (纸本)9781467309745

The objective of this work is to evaluate the viability of implementing workload-aware dynamic power management schemes on a many-core platform, aiming at reducing power consumption for high performance computing (HPC) application. Two approaches were proposed to achieve the desired target. First approach is an off-line scheduling scheme where core voltage and frequency are set up beforehand based on the workload characterization of the application. The second approach is an on-line scheduling scheme, where core voltage and frequency are controlled based on a workload detection algorithm. Experiments were conducted using the 48-core Intel Single-chip Cloud Computer (SCC), running a parallelized Fire Spread Monte Carlo Simulation program. Both schemes were compared against a performance-driven, but non-power-aware management scheme. The results indicate that our schemes are able to reduce the power consumption up to 29\% with mild impact on the system performance.

关键词： Frequency domain analysis Computational modeling Vegetation Power demand Monte Carlo methods Power measurement Gears

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Intelligent distributed computing V: Proceedings of the 5th international symposium on Intelligent distributed computing - IDC 2011, Delft, the ... 2011

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2011年

作者： Frances M T Brazier Kees Nieuwenhuis Gregor Pavlin Martijn Warnier Costin Bădică

ISBN: (纸本)9783642240126

This book represents the combined peer-reviewed proceedings of the Fifth international symposium on Intelligent distributed computing -- IDC 2011 and of the Third international Workshop on Multi-Agent Systems Technology and Semantics -- MASTS 2011. Both events were held in Delft, The Netherlands during October 5-7, 2011. The 33 contributions published in this book address many topics related to theory and applications of intelligent distributed computing and multi-agent systems, including: adaptive and autonomous distributed systems, agent programming, ambient assisted living systems, business process modeling and verification, cloud computing, coalition formation, decision support systems, distributed optimization and constraint satisfaction, gesture recognition, intelligent energy management in WSNs, intelligent logistics, machine learning, mobile agents, parallel and distributed computational intelligence, parallel evolutionary computing, trust metrics and security, scheduling in distributed heterogenous computing environments, semantic Web service composition, social simulation, and software agents for WSNs.

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