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检索条件"任意字段=2003 ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming"

共 560 条记录，以下是181-190 订阅

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Lock-free channels for programming via communicating sequential processes 19

Lock-free channels for programming via communicating sequent...

引用

24th acm sigplan symposium on principles and practice of parallel programming, PPoPP 2019

作者： Koval, Nikita Alistarh, Dan Elizarov, Roman IST Austria Austria JetBrains Austria

ISBN: (纸本)9781450362252

Traditional concurrent programming involves manipulating shared mutable state. Alternatives to this programming style are communicating sequential processes (CSP) [1] and actor [2] models, which share data via explicit communication. Rendezvous channel is the common abstraction for communication between several processes, where senders and receivers perform a rendezvous handshake as a part of their protocol (senders wait for receivers and vice versa). Additionally to this, channels support the select expression. In this work, we present the first eficient lock-free channel algorithm, and compare it against Go [3] and Kotlin [4] baseline implementations. © 2019 Copyright held by the owner/author(s).

关键词： Locks (fasteners)

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

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

引用

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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Qlisp: Experience and new directions

Qlisp: Experience and new directions

引用

1988 acm/sigplan Conference on parallel programming: Experience with Applications, Languages and Systems, PPEALS 1988

作者： Goldman, Ron Gabriel, Richard P. Lucid Inc. United States

ISBN: (纸本)0897912764

Qlisp, a dialect of Common Lisp, has been proposed as a multiprocessing programming language which is suitable for studying the styles of parallel programming at the medium-grain level. An initial version of Qlisp has been implemented on a multiprocessor and a number of experiments with it conducted. This paper describes the implementation, reports on some of the experiments, and presents some new constructs that are suggested from programming experience with Qlisp. © 1988 acm.

关键词： parallel programming

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parallel Determinacy Race Detection for Futures 20

Parallel Determinacy Race Detection for Futures

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

作者： Xu, Yifan Singer, Kyle Lee, I-Ting Angelina Washington Univ St Louis St Louis MO 63130 USA

ISBN: (纸本)9781450368186

The use of futures can generate arbitrary dependences in the computation, making it difficult to detect races efficiently. Algorithms proposed by priorwork to detect races on programs with futures all have to execute the program sequentially. We propose F-Order, the first known parallel race detection algorithm that detects races on programs that use futures. Given a computation with work T-1 and span T-infinity, our algorithm detects races in time O((T-1 lg (k) over cap + k(2))/ P + T-infinity(k + lg r lg (k) over cap)) on P processors, where k is the number of future operations, r is the maximum number of readers per memory location, and (k) over cap is the maximum number of future operations done by a single future task, which is typically small. We have also implemented a prototype system based on the proposed algorithm and empirically demonstrates its practical efficiency and scalability.

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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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Copperhead: Compiling an Embedded Data parallel Language 11

Copperhead: Compiling an Embedded Data Parallel Language

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16th acm symposium on principles and practice of parallel programming

作者： Catanzaro, Bryan Garland, Michael Keutzer, Kurt Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9781450301190

Modern parallel microprocessors deliver high performance on applications that expose substantial fine-grained data parallelism. Although data parallelism is widely available in many computations, implementing data parallel algorithms in low-level languages is often an unnecessarily difficult task. The characteristics of parallel microprocessors and the limitations of current programming methodologies motivate our design of Copperhead, a high-level data parallel language embedded in Python. The Copperhead programmer describes parallel computations via composition of familiar data parallel primitives supporting both flat and nested data parallel computation on arrays of data. Copperhead programs are expressed in a subset of the widely used Python programming language and interoperate with standard Python modules, including libraries for numeric computation, data visualization, and analysis. In this paper, we discuss the language, compiler, and runtime features that enable Copperhead to efficiently execute data parallel code. We define the restricted subset of Python which Copperhead supports and introduce the program analysis techniques necessary for compiling Copperhead code into efficient low-level implementations. We also outline the runtime support by which Copperhead programs interoperate with standard Python modules. We demonstrate the effectiveness of our techniques with several examples targeting the CUDA platform for parallel programming on GPUs. Copperhead code is concise, on average requiring 3.6 times fewer lines of code than CUDA, and the compiler generates efficient code, yielding 45-100% of the performance of hand-crafted, well optimized CUDA code.

关键词： Python Data parallelism GPU Algorithms Design Performance

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Model and compilation strategy for out-of-core data parallel programs

Model and compilation strategy for out-of-core data parallel...

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Proceedings of the 5th acm sigplan symposium on principles and practice of parallel programming

作者： Bordawekar, Rajesh Choudhary, Alok Kennedy, Ken Koelbel, Charles Paleczny, Michael Syracuse Univ Syracuse United States

It is widely acknowledged in high-performance computing circles that parallel input/output needs substantial improvement in order to make scalable computers truly usable. We present a data storage model that allows processors independent access to their own data and a corresponding compilation strategy that integrates data-parallel computation with data distribution for out-of-core problems. Our results compare several communication methods and I/O optimizations using two out-of-core problems, Jacobi iteration and LU factorization.

关键词： parallel processing systems

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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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Toward Terabyte Pattern Mining An Architecture-conscious Solution 07

Toward Terabyte Pattern Mining An Architecture-conscious Sol...

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acm sigplan symposium on principles and practice of parallel programming

作者： Buehrer, Gregory Parthasarathy, Srinivasan Tatikonda, Shirish Kurc, Tahsin Saltz, Joel Ohio State Univ Columbus OH 43210 USA

ISBN: (纸本)9781595936028

We present a strategy for mining frequent itemsets from terabyte-scale data sets on cluster systems. The algorithm embraces the holistic notion of architecture-conscious data mining, taking into account the capabilities of the processor, the memory hierarchy and the available network interconnects. Optimizations have been designed for lowering communication costs using compressed data structures and a succinct encoding. Optimizations for improving cache, memory and I/O utilization using pruning and tiling techniques, and smart data placement strategies are also employed. We leverage the extended memory space and computational resources of a distributed message-passing cluster to design a scalable solution, where each node can extend its meta structures beyond main memory by leveraging 64-bit architecture support. Our solution strategy is presented in the context of FPGrowth, a well-studied and rather efficient frequent pattern mining algorithm. Results demonstrate that the proposed strategy result in near-linear scaleup on up to 48 nodes.

关键词： itemset mining data mining parallel out of core

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Stack-based parallel recursion on graphics processors

Stack-based parallel recursion on graphics processors

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2009 acm sigplan symposium on principles and practice of parallel programming, PPoPP'09

作者： Yang, Ke He, Bingsheng Luo, Qiong Sander, Pedro V. Shi, Pedro V. Zhejiang Univ. China HKUST Hong Kong

ISBN: (纸本)9781605583976

Recent research has shown promising results on using graphics processing units (GPUs) to accelerate general-purpose computation. However, today's GPUs do not support recursive functions. As a result, for inherently recursive algorithms such as tree traversal, GPU programmers need to explicitly use stacks to emulate the recursion. parallelizing such stack-based implementation on the GPU increases the programming difficulty;moreover, it isunclear how to improve the efficiency of such parallel implementations. As a first step to address both ease of programming and efficiency issues, we propose three parallel stack implementation alternatives that differ in the granularity of stack sharing. Taking tree traversals as an example, we study the performance tradeoffs between these alternatives and analyze their behaviors in various situations. Our results could be useful to both GPU programmers and GPU compiler writers. © 2009 acm.

关键词： Graphics processing unit

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