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检索条件"任意字段=6th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming"

共 379 条记录，以下是121-130 订阅

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Data Transformations Enabling Loop Vectorization on Multithreaded Data parallel Architectures

Data Transformations Enabling Loop Vectorization on Multithr...

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15th acm sigplan symposium on principles and practice of parallel programming

作者： Jang, Byunghyun Mistry, Perhaad Schaa, Dana Dominguez, Rodrigo Kaeli, David Northeastern Univ Dept ECE Boston MA 02115 USA

ISBN: (纸本)9781605587080

Loop vectorization, a key feature exploited to obtain high performance on Single Instruction Multiple Data (SIMD) vector architectures, is significantly hindered by irregular memory access patterns in the data stream. this paper describes data transformations that allow us to vectorize loops targeting massively multithreaded data parallel architectures. We present a mathematical model that captures loop-based memory access patterns and computes the most appropriate data transformations in order to enable vectorization. Our experimental results show that the proposed data transformations can significantly increase the number of loops that can be vectorized and enhance the data-level parallelism of applications. Our results also show that the overhead associated with our data transformations can be easily amortized as the size of the input data set increases. For the set of high performance benchmark kernels studied, we achieve consistent and significant performance improvements (up to 11.4X) by applying vectorization using our data transformation approach.

关键词： Loop Vectorization Data Transformation GPGPU

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Leveraging parallel Nesting in Transactional Memory

Leveraging Parallel Nesting in Transactional Memory

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15th acm sigplan symposium on principles and practice of parallel programming

作者： Barreto, Joao Dragojevic, Aleksandar Ferreira, Paulo Guerraoui, Rachid Kapalka, Michal Univ Tecn Lisboa INESC ID Lisbon Portugal Swiss Fed Inst Technol CH-1015 Lausanne Switzerland

ISBN: (纸本)9781605587080

Exploiting the emerging reality of affordable multi-core architectures goes through providing programmers with simple abstractions that would enable them to easily turn their sequential programs into concurrent ones that expose as much parallelism as possible. While transactional memory promises to make concurrent programming easy to a wide programmer community, current implementations either disallow nested transactions to run in parallel or do not scale to arbitrary parallel nesting depths. this is an important obstacle to the central goal of transactional memory, as programmers can only start parallel threads in restricted parts of their code. this paper addresses the intrinsic difficulty behind the support for parallel nesting in transactional memory, and proposes a novel solution that, to the best of our knowledge, is the first practical solution to meet the lowest theoretical upper bound known for the problem. Using a synthetic workload configured to test parallel transactions on a multi-core machine, a practical implementation of our algorithm yields substantial speed-ups (up to 22x with 33 threads) relatively to serial nesting, and shows that the time to start and commit transactions, as well as to detect conflicts, is independent of nesting depth.

关键词： Algorithms Nested parallel programs fork-join work-stealing transactional memory

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Fine-grain parallel megabase sequence comparison with multiple heterogeneous GPUs 14

Fine-grain parallel megabase sequence comparison with multip...

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2014 19th acm sigplan symposium on principles and practice of parallel programming, PPoPP 2014

作者： De Sandes, Edans F.O. Miranda, Guillermo Melo, Alba C.M.A. Martorell, Xavier Ayguadé, Eduard University of Brasilia Brazil Universitat Politècnica de Catalunya Barcelona Supercomputing Center Spain

ISBN: (纸本)9781450326568

this paper proposes and evaluates a parallel strategy to execute the exact Smith-Waterman (SW) algorithm for megabase DNA sequences in heterogeneous multi-GPU platforms. In our strategy, the computation of a single huge SW matrix is spread over multiple GPUs, which communicate border elements to the neighbour, using a circular buffer mechanism that hides the communication overhead. We compared 4 pairs of human-chimpanzee homologous chromosomes using 2 different GPU environments, obtaining a performance of up to 140.36 GCUPS (Billion of cells processed per second) with 3 heterogeneous GPUS.

关键词： Graphics processing unit

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Practical parallel Hypergraph Algorithms 20

Practical Parallel Hypergraph Algorithms

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25th acm sigplan symposium on principles and practice of parallel programming (PPoPP)

作者： Shun, Julian MIT CSAIL Cambridge MA 02139 USA

ISBN: (纸本)9781450368186

While there has been significant work on parallel graph processing, there has been very surprisingly little work on high-performance hypergraph processing. this paper presents a collection of efficient parallel algorithms for hypergraph processing, including algorithms for betweenness centrality, maximal independent set, k-core decomposition, hyper-trees, hyperpaths, connected components, PageRank, and single-source shortest paths. For these problems, we either provide new parallel algorithms or more efficient implementations than prior work. Furthermore, our algorithms are theoretically-efficient in terms of work and depth. To implement our algorithms, we extend the Ligra graph processing framework to support hypergraphs, and our implementations benefit from graph optimizations including switching between sparse and dense traversals based on the frontier size, edge-aware parallelization, using buckets to prioritize processing of vertices, and compression. Our experiments on a 72-core machine and show that our algorithms obtain excellent parallel speedups, and are *** faster than algorithms in existing hypergraph processing frameworks.

关键词： parallel algorithms

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parallelizing algorithms for symbolic computation using ||MAPLE|| 93

Parallelizing algorithms for symbolic computation using ||MA...

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4th acm sigplan symposium on principles and practice of parallel programming, PPOPP 1993

作者： Siegl, Kurt Johannes Kepler University LinzA-4040 Austria

ISBN: (纸本)0897915895

||MAPLE|| (speak: parallel Maple) is a portable system for parallel symbolic computation. the system is built as an interface between the parallel declarative programming language Strand and the sequential computer algebra system Maple, thus providing the elegance of Strand and the power of the existing sequential algorithms in Maple. the implementation of difFerent parallel programming paradigms shows that it is fairly easy to parallelize even complex algebraic algorithms using this system. Sample applications (among them algorithms solving multivariate nonlinear equation systems) are implemented on various parallel architectures. For example a straightforward parallelization of the complex and important problem of real root isolation has been parallelized using a generic Strand program of fewer than 20 Unes of code and a slight modification of 5 lines in the original sequential Maple source. Even with such a simple modification we gained a speed-up of 5 times, that is better than those reported by others in the literature.

关键词： Logic programming

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A Comparison of programming Models for Multiprocessors with Explicitly Managed Memory Hierarchies

A Comparison of Programming Models for Multiprocessors with ...

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14th acm sigplan symposium on principles and practice of parallel programming

作者： Schneider, Scott Yeom, Jae-Seung Rose, Benjamin Linford, John C. Sandu, Adrian Nikolopoulos, Dimitrios S. Virginia Tech Dept Comp Sci Blacksburg VA 24060 USA

ISBN: (纸本)9781605583976

On multiprocessors with explicitly managed memory hierarchies (EMM), software has the responsibility of moving data in and out of fast local memories. this task can be complex and error-prone even for expert programmers. Before we can allow compilers to handle the complexity for us, we must identify the abstractions that are general enough to allow us to write applications with reasonable effort, yet specific enough to exploit the vast on-chip memory bandwidth of EMM multi-processors. To this end, we compare two programming models against hand-tuned codes on the STI Cell, paying attention to programmability and performance. the first programming model, Sequoia, abstracts the memory hierarchy as private address spaces, each corresponding to a parallel task. the second, Cellgen, is a new framework which provides OpenMP-like semantics and the abstraction of a shared address spaces divided into private and shared data. We compare three applications programmed using these models against their hand-optimized counterparts in terms of abstractions, programming complexity, and performance.

关键词： Design Languages Cell BE Explicitly Managed Memory Hierarchies programming Models

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Work-stealing with Configurable Scheduling Strategies 13

Work-stealing with Configurable Scheduling Strategies

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18th acm sigplan symposium on principles and practice of parallel programming

作者： Wimmer, Martin Traeff, Jesper Larsson Cederman, Daniel Tsigas, Philippas Vienna Univ Technol Fac Informat Res Grp Parallel Comp A-1040 Vienna Austria Chalmers Univ Technol S-41296 Gothenburg Sweden

ISBN: (纸本)9781450319225

Work-stealing systems are typically oblivious to the nature of the tasks they are scheduling. they do not know or take into account how long a task will take to execute or how many subtasks it will spawn. Moreover, task execution order is typically determined by an underlying task storage data structure, and cannot be changed. there are thus possibilities for optimizing task parallel executions by providing information on specific tasks and their preferred execution order to the scheduling system. We investigate generalizations of work-stealing and introduce a framework enabling applications to dynamically provide hints on the nature of specific tasks using scheduling strategies. Strategies can be used to independently control both local task execution and steal order. Strategies allow optimizations on specific tasks, in contrast to more conventional scheduling policies that are typically global in scope. Strategies are composable and allow different, specific scheduling choices for different parts of an application simultaneously. We have implemented a work-stealing system based on our strategy framework. A series of benchmarks demonstrates beneficial effects that can be achieved with scheduling strategies.

关键词： Algorithms Work-stealing scheduler hints strategies priorities

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Automatic Problem Size Sensitive Task Partitioning on Heterogeneous parallel Systems 13

Automatic Problem Size Sensitive Task Partitioning on Hetero...

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18th acm sigplan symposium on principles and practice of parallel programming

作者： Grasso, Ivan Kofler, Klaus Cosenza, Biagio Fahringer, thomas Univ Innsbruck Inst Informat A-6020 Innsbruck Austria

ISBN: (纸本)9781450319225

In this paper we propose a novel approach which automatizes task partitioning in heterogeneous systems. Our framework is based on the Insieme Compiler and Runtime infrastructure [1]. the compiler translates a single-device OpenCL program into a multi-device OpenCL program. the runtime system then performs dynamic task partitioning based on an offline-generated prediction model. In order to derive the prediction model, we use a machine learning approach that incorporates static program features as well as dynamic, input sensitive features. Our approach has been evaluated over a suite of 23 programs and achieves performance improvements compared to an execution of the benchmarks on a single CPU and a single GPU only.

关键词： Languages Algorithms Performance heterogeneous computing compilers GPU task partitioning code analysis machine learning runtime system

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POSTER: RadiK: Scalable Radix Top-K Selection on GPUs 24

POSTER: RadiK: Scalable Radix Top-K Selection on GPUs

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29th acm sigplan Annual symposium on principles and practice of parallel programming (PPoPP)

作者： Li, Yifei Zhou, Bole Zhang, Jiejing Wei, Xuechao Li, Yinghan Chen, Yingda Alibaba Grp Hangzhou Peoples R China

ISBN: (纸本)9798400704352

By identifying the.. largest or smallest elements in a set of data, top-k selection is critical for modern high-performance databases and machine learning systems, especially with large data volumes. However, previous studies on its GPU implementation are mostly merge-based and rely heavily on the high-speed but size-limited on-chip memory, thereby resulting in a restricted upper bound on... this paper introduces RadiK, a highly optimized GPU-parallel radix top-k selection that is scalable with.., input length, and batch size. With a carefully designed optimization framework targeting high memory bandwidth and resource utilization, RadiK supports far larger.. than the prior art, achieving up to 2.5x speedup for non-batch queries and up to 4.8x speedup for batch queries. We also propose a lightweight refinement that strengthens the robustness of RadiK against skewed distributions by adaptively scaling the input elements.

关键词： Top-K Radix Select GPU-parallel Algorithm

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PPoPP'08 - Proceedings of the 2008 acm sigplan symposium on principles and practice of parallel programming

PPoPP'08 - Proceedings of the 2008 ACM SIGPLAN Symposium on ...

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13th acm sigplan symposium on principles and practice of parallel programming, PPoPP'08

ISBN: (纸本)9781595939609

the proceedings contain 42 papers. the topics discussed include: automatic data movement and computation mapping for multi-level parallel architectures with explicitly managed memories;type inference for locality analysis of distributed data structures;quasi-static scheduling for safe futures;scalable packet classification using interpreting: a cross-platform multi-core solution;FastForward for efficient pipeline parallelism: a cache-optimized concurrent lock-free queue;matrix product on heterogeneous master-worker platforms;high performance dense linear algebra on a spatially distributed processor;optimization principles and application performance evaluation of a multithreaded GPU using CUDA;a case study in SIMD text processing with parallel bit streams: UTF-8 to UTF-16 transcoding;programming with tiles;design and implementation of a high-performance MPI for C# and the common language infrastructure;and a portable runtime interface for multi-level memory hierarchies.

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