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Streamline Integration Using MPI-Hybrid parallelism on a Large Multicore Architecture

IEEE TRANSACTIONS ON VISUALIZATION AND COMPUTER GRAPHICS 2011年第11期17卷 1702-1713页

作者： Camp, David Garth, Christoph Childs, Hank Pugmire, Dave Joy, Kenneth I. Univ Calif Davis Lawrence Berkeley Natl Lab Davis CA 95616 USA Univ Calif Davis Dept Comp Sci Davis CA 95616 USA Oak Ridge Natl Lab Oak Ridge TN 37831 USA

Streamline computation in a very large vector field data set represents a significant challenge due to the nonlocal and data-dependent nature of streamline integration. In this paper, we conduct a study of the performance characteristics of hybrid parallel programming and execution as applied to streamline integration on a large, multicore platform. With multicore processors now prevalent in clusters and supercomputers, there is a need to understand the impact of these hybrid systems in order to make the best implementation choice. We use two MPI-based distribution approaches based on established parallelization paradigms, parallelize over seeds and parallelize over blocks, and present a novel MPI-hybrid algorithm for each approach to compute streamlines. Our findings indicate that the work sharing between cores in the proposed MPI-hybrid parallel implementation results in much improved performance and consumes less communication and I/O bandwidth than a traditional, nonhybrid distributed implementation.

关键词： Concurrent programming parallel programming modes of computation parallelism and concurrency picture/image generation display algorithms

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Prediction of Optimal parallelism Level in Wide Area Data Transfers

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2011年第12期22卷 2033-2045页

作者： Yildirim, Esma Yin, Dengpan Kosar, Tevfik SUNY Buffalo Dept Comp Sci & Engn Buffalo NY 14260 USA Louisiana State Univ Dept Comp Sci Baton Rouge LA 70803 USA Louisiana State Univ Ctr Computat & Technol Baton Rouge LA 70803 USA

Wide area data transfer may be a major bottleneck for the end-to-end performance of distributed applications. A practical way of increasing the wide area throughput at the application layer is using multiple parallel streams. Although increased number of parallel streams may yield much better performance than using a single stream, overwhelming the network by opening too many streams may have an inverse effect. The congestion created by excess number of streams may cause a drop down in the throughput achieved. Hence, it is important to decide on the optimal number of streams without congesting the network. Predicting this "optimum" number is not straightforward, since it depends on many parameters specific to each individual transfer. Generic models that try to predict this number either rely too much on historical information or fail to achieve accurate predictions. In this paper, we present a set of new models which aim to approximate the optimal number with least history information and lowest prediction overhead. An algorithm is introduced to select the best combination of historic information to do the prediction for evaluation purposes as well as optimizing prediction by reducing error rate. We measure the feasibility and accuracy of the proposed prediction models by comparing to actual GridFTP data transfer by using little historical information and have seen that we could predict the throughput of parallel streams accurately and find a very close approximation of the optimal stream number.

关键词： Distributed applications modeling and prediction parallelism and concurrency network protocols

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Improving parallelism in Git and GNU Compiler Collection: Strategies, Difficulties, and Lessons Learned

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IEEE SOFTWARE 2021年第5期38卷 92-100页

作者： Bernardino, Matheus Tavares Belinassi, Giuliano Meirelles, Paulo Guerra, Eduardo Martins Goldman, Alfredo Univ Sao Paulo Comp Sci Sao Paulo Brazil Univ Fed Sao Paulo Sao Paulo Brazil Free Univ Bozen Bolzano Bolzano Italy Univ Sao Paulo Sao Paulo Brazil

This article presents approaches for performance improvements into two large and well-known open source projects, Git and GNU Compiler Collection, using parallel programming. We share the difficulties faced and the st... 详细信息

关键词： Parallel Programming Program Compilers Public Domain Software Git GNU Compiler Collection Parallel Programming Similar Parallelization Processes parallelism Performance Improvements Open Source Projects Optimization Parallel Processing Instruction Sets Open Source Software Performance Evaluation parallelism and concurrency Parallel Systems Compilers Version Control GCC Git Conversion From Sequential To Parallel Forms

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The Monte Carlo method in science and engineering

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COMPUTING IN SCIENCE & ENGINEERING 2006年第2期8卷 9-19页

作者： Amar, JG Univ Toledo Dept Phys & Astron Toledo OH 43606 USA

Since 1953, researchers have applied the Monte Carlo method to a wide range of areas. Specialized algorithms have also been developed to extend the method's applicability and efficiency. The author describes some ... 详细信息

关键词： Simulation Theory Markov Processes Monte Carlo parallelism and concurrency

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Fast Parallel Markov Clustering in Bioinformatics Using Massively Parallel Computing on GPU with CUDA and ELLPACK-R Sparse Format

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IEEE-ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2012年第3期9卷 679-692页

作者： Bustamam, Alhadi Burrage, Kevin Hamilton, Nicholas A. Univ Indonesia Dept Math Depok 16424 Indonesia Univ Oxford Comp Lab Oxford OX1 3QD England Univ Queensland Inst Mol Biosci St Lucia Qld 4072 Australia

Markov clustering (MCL) is becoming a key algorithm within bioinformatics for determining clusters in networks. However, with increasing vast amount of data on biological networks, performance and scalability issues are becoming a critical limiting factor in applications. Meanwhile, GPU computing, which uses CUDA tool for implementing a massively parallel computing environment in the GPU card, is becoming a very powerful, efficient, and low-cost option to achieve substantial performance gains over CPU approaches. The use of on-chip memory on the GPU is efficiently lowering the latency time, thus, circumventing a major issue in other parallel computing environments, such as MPI. We introduce a very fast Markov clustering algorithm using CUDA (CUDA-MCL) to perform parallel sparse matrix-matrix computations and parallel sparse Markov matrix normalizations, which are at the heart of MCL. We utilized ELLPACK-R sparse format to allow the effective and fine-grain massively parallel processing to cope with the sparse nature of interaction networks data sets in bioinformatics applications. As the results show, CUDA-MCL is significantly faster than the original MCL running on CPU. Thus, large-scale parallel computation on off-the-shelf desktop-machines, that were previously only possible on supercomputing architectures, can significantly change the way bioinformaticians and biologists deal with their data.

关键词： Markov clustering graphs and networks GPU computing PPI networks CUDA ELLPACK-R sparse format scalable parallel programming parallelism and concurrency performance evaluation bioinformatics

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Architectural support for thread communications in multi-core processors

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PARALLEL COMPUTING 2011年第1期37卷 26-41页

作者： Varoglu, Sevin Jenks, Stephen Univ Calif Irvine Dept Elect Engn & Comp Sci Irvine CA 92717 USA

In the ongoing quest for greater computational power, efficiently exploiting parallelism is of paramount importance. Architectural trends have shifted from improving single-threaded application performance, often achieved through instruction level parallelism (ILP), to improving multithreaded application performance by supporting thread level parallelism (TLP). Thus, multi-core processors incorporating two or more cores on a single die have become ubiquitous. To achieve concurrent execution on multi-core processors, applications must be explicitly restructured to exploit parallelism, either by programmers or compilers. However, multithreaded parallel programming may introduce overhead due to communications among threads. Though some resources are shared among processor cores, current multi-core processors provide no explicit communications support for multithreaded applications that takes advantage of the proximity between cores. Currently, inter-core communications depend on cache coherence, resulting in demand-based cache line transfers with their inherent latency and overhead. In this paper, we explore two approaches to improve communications support for multithreaded applications. Prepushing is a software controlled data forwarding technique that sends data to destination's cache before it is needed, eliminating cache misses in the destination's cache as well as reducing the coherence traffic on the bus. Software Controlled Eviction (SCE) improves thread communications by placing shared data in shared caches so that it can be found in a much closer location than remote caches or main memory. Simulation results show significant performance improvement with the addition of these architecture optimizations to multi-core processors. (C) 2010 Elsevier B.V. All rights reserved.

关键词： Multi-core processors parallelism and concurrency Shared memory

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An OpenMP Extension that Supports Thread-Level Speculation

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2016年第1期27卷 78-91页

作者： Aldea, Sergio Estebanez, Alvaro Llanos, Diego R. Gonzalez-Escribano, Arturo Univ Valladolid Dept Informat Campus Miguel Delibes E-47011 Valladolid Spain

OpenMP directives are the de-facto standard for shared-memory parallel programming. However, OpenMP does not guarantee the correctness of the parallel execution of a given loop if runtime data dependences arise. Consequently, many highly-parallel regions cannot be safely parallelized with OpenMP due to the possibility of a dependence violation. In this paper, we propose to augment OpenMP capabilities, by adding thread-level speculation (TLS) support. Our contribution is threefold. First, we have defined a new speculative clause for variables inside parallel loops. This clause ensures that all accesses to these variables will be carried out according to sequential semantics. Second, we have created a new, software-based TLS runtime library to ensure correctness in the parallel execution of OpenMP loops that include speculative variables. Third, we have developed a new GCC plugin, which seamlessly translates our OpenMP speculative clause into calls to our TLS runtime engine. The result is the ATLaS C Compiler framework, which takes advantage of TLS techniques to expand OpenMP functionalities, and guarantees the sequential semantics of any parallelized loop.

关键词： parallelism and concurrency code generation thread-level speculation optimistic parallelization

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A SCALABLE MESSAGE PASSING INTERFACE IMPLEMENTATION OF AN AD-HOC PARALLEL I/O SYSTEM

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INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2010年第2期24卷 164-184页

作者： Isaila, Florin Garcia Blas, Francisco Javier Carretero, Jesus Liao, Wei-keng Choudhary, Alok Univ Carlos III Madrid Leganes 28911 Spain Northwestern Univ Dept Elect Engn & Comp Sci Robert R McCormick Sch Engn & Appl Sci Evanston IL USA

In this paper we present the novel design, implementation, and evaluation of an ad-hoc parallel I/O system (AHPIOS). AHPIOS is the first scalable parallel I/O system completely implemented in the Message Passing Interface (MPI). The MPI implementation brings the advantages of portability, scalability and high performance. AHPIOS allows MPI applications to dynamically manage and scale distributed partitions in a convenient way. The configuration of both the MPI-IO and the storage management system is unified and allows for a tight integration of the optimizations of these layers. AHPIOS partitions are elastic: they conveniently scale up and down with the number of resources. We develop two collective I/O strategies, which leverage a two-tiered cooperative cache in order to exploit the spatial locality of data-intensive parallel applications. The file access latency is hidden from the applications through an asynchronous data staging strategy. The two-tiered cooperative cache scales with both the number of processors and storage resources. Our experimental section demonstrates that, with various optimizations, integrated AHPIOS offers a substantial performance benefit over the traditional MPI-IO solutions on both PVFS or Lustre parallel file systems.

关键词： parallel I/O parallel systems distributed file systems parallelism and concurrency

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Frequent Item Computation on a Chip

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IEEE TRANSACTIONS ON KNOWLEDGE AND DATA ENGINEERING 2011年第8期23卷 1169-1181页

作者： Teubner, Jens Mueller, Rene Alonso, Gustavo Swiss Fed Inst Technol Dept Comp Sci Syst Grp Zurich Switzerland

Computing frequent items is an important problem by itself and as a subroutine in several data mining algorithms. In this paper, we explore how to accelerate the computation of frequent items using field-programmable gate arrays (FPGAs) with a threefold goal: increase performance over existing solutions, reduce energy consumption over CPU-based systems, and explore the design space in detail as the constraints on FPGAs are very different from those of traditional software-based systems. We discuss three design alternatives, each one of them exploiting different FPGA features and each one providing different performance/scalability trade-offs. An important result of the paper is to demonstrate how the inherent massive parallelism of FPGAs can improve performance of existing algorithms but only after a fundamental redesign of the algorithms. Our experimental results show that, e. g., the pipelined solution we introduce can reach more than 100 million tuples per second of sustained throughput (four times the best available results to date) by making use of techniques that are not available to CPU-based solutions. Moreover, and unlike in software approaches, the high throughput is independent of the skew of the Zipf distribution of the input and at a far lower energy cost.

关键词： Data mining reconfigurable hardware parallelism and concurrency

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IMPLEMENTATION AND EVALUATION OF FILE WRITE-BACK AND PREFETCHING FOR MPI-IO OVER GPFS

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INTERNATIONAL JOURNAL OF HIGH PERFORMANCE COMPUTING APPLICATIONS 2010年第1期24卷 78-92页

作者： Garcia Blas, Javier Isaila, Florin Carretero, Jesus Singh, David Garcia-Carballeira, Felix Univ Carlos III Madrid Leganes 28911 Spain

In this paper we present the implementation of an open-source MPI-IO interface for the General Parallel File System (GPFS). Our solution includes the design and implementation of GPFS-based write-back and prefetching modules, which have been integrated in ROMIO. A collective file write strategy based on GPFS data-shipping, and a view-based collective I/O mechanism, relying on GPFS mechanisms, are at the core of the novel optimizations proposed in this paper. View-based collective I/O includes a thread-based flushing method implementing a write-back policy for latency hiding, and a prefetching method, based on GPFS hints, to increase small read access performance. Performance evaluations show that our implementation achieves high-performance and hides the latency of file accesses through the combination of view-based collective file accesses, and the overlapping of computation, communication and I/O. This is especially true for collective and small-size access patterns, which are very frequent in parallel scientific applications.

关键词： parallel I/O parallel systems distributed file systems parallel programming parallelism and concurrency

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