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检索条件"任意字段=9th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming"

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parallel algorithms for masked sparse matrix-matrix products 22

Parallel algorithms for masked sparse matrix-matrix products

引用

Proceedings of the 27th acm sigplan symposium on principles and practice of parallel programming

作者： Srđan Milaković Oguz Selvitopi Israt Nisa Zoran Budimlić Aydin Buluç Rice University Lawrence Berkeley Nat. Laboratory AWS AI

ISBN: (纸本)9781450392044

Computing the product of two sparse matrices (SpGEMM) is a fundamental operation in various combinatorial and graph algorithms as well as various bioinformatics and data analytics applications for computing inner-product similarities. For an important class of algorithms, only a subset of the output entries are needed, and the resulting operation is known as Masked SpGEMM since a subset of the output entries is considered to be "masked out". In this work, we investigate various novel algorithms and data structures for this rather challenging and important computation, and provide guidelines on how to design a fast Masked-SpGEMM for shared-memory architectures.

关键词： GraphBLAS

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Compilers and parallel computing systems 08

Compilers and parallel computing systems

引用

Proceedings of the 13th acm sigplan symposium on principles and practice of parallel programming

作者： Frances Allen IBM T. J. Watson Research Center Yorktown Height NY USA

ISBN: (纸本)9781595937957

Increasing the delivered performance of computers by running programs in parallel is an old idea with a new urgency. Multi cores (multi processors) on chips have emerged as a way to increase performance wherever chips are used. the talk will focus on the role programming languages and compilers must play in delivering parallel performance to users and applications. the speaker's personal experiences with languages and compilers for high performance systems will provide the basis for her observations. the talk is intended to encourage the exploration of new approaches.

关键词： keynote talk abstract

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A parallel branch-and-bound algorithm with history-based domination 22

A parallel branch-and-bound algorithm with history-based dom...

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Proceedings of the 27th acm sigplan symposium on principles and practice of parallel programming

作者： Taspon Gonggiatgul Ghassan Shobaki Pinar Muyan-Özçelik California State University

ISBN: (纸本)9781450392044

In this paper, we describe a parallel Branch-and-Bound (B&B) algorithm with a history-based domination technique, and we apply it to the Sequential Ordering Problem (SOP). To the best of our knowledge, the proposed algorithm is the first parallel B&B algorithm that includes a history-based domination technique and is the first parallel B&B algorithm for solving the SOP using a pure B&B approach. the proposed algorithm takes a pool-based approach and employs a collection of novel techniques that we have developed to achieve effective parallel exploration of the solution space, including parallel history domination, history table memory management, and a thread restart technique. the proposed algorithm was experimentally evaluated using the SOPLIB and TSPLIB benchmarks. the results show that using ten threads with a time limit of one hour on the medium-difficulty instances, the proposed algorithm gives a geometric-mean speedup of 19.9 on SOPLIB and 10.23 on TSPLIB, with super-linear speedups up to 65x seen on 17 instances.

关键词： NP-complete problems

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LB-HM: load balance-aware data placement on heterogeneous memory for task-parallel HPC applications 22

LB-HM: load balance-aware data placement on heterogeneous me...

引用

Proceedings of the 27th acm sigplan symposium on principles and practice of parallel programming

作者： Zhen Xie Jie Liu Sam Ma Jiajia Li Dong Li University of California College of William & Mary

ISBN: (纸本)9781450392044

the emergence of heterogeneous memory (HM) provides a cost-effective and high-performance solution to memory-consuming HPC applications. However, using HM, wisely migrating data objects on it is critical for high performance. In this work, we introduce a load balance-aware page management system, named LB-HM. LB-HM introduces task semantics during memory profiling, rather than being application-agnostic. Evaluating with a set of memory-consuming HPC applications, we show that we show that LB-HM reduces existing load imbalance and leads to an average of 17.1% and 15.4% (up to 26.0% and 23.2%) performance improvement, compared with a hardware-based solution and an industry-quality software-based solution on Optane-based HM.

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Automatic synthesis of parallel unix commands and pipelines with KumQuat 22

Automatic synthesis of parallel unix commands and pipelines ...

引用

Proceedings of the 27th acm sigplan symposium on principles and practice of parallel programming

作者： Jiasi Shen Martin Rinard Nikos Vasilakis MIT

ISBN: (纸本)9781450392044

We present KumQuat, a system for automatically generating data-parallel implementations of Unix shell commands and pipelines. the generated parallel versions split input streams, execute multiple instantiations of the original pipeline commands to process the splits in parallel, then combine the resulting parallel outputs to produce the final output stream. KumQuat automatically synthesizes the combine operators, with a domain-specific combiner language acting as a strong regularizer that promotes efficient inference of correct combiners. We present experimental results that show that these combiners enable the effective parallelization of our benchmark scripts.

关键词： automatic parallelization program synthesis

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Stream-K: Work-Centric parallel Decomposition for Dense Matrix-Matrix Multiplication on the GPU 23

Stream-K: Work-Centric Parallel Decomposition for Dense Matr...

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Proceedings of the 28th acm sigplan Annual symposium on principles and practice of parallel programming

作者： Muhammad Osama Duane Merrill Cris Cecka Michael Garland John D. Owens University of California Davis NVIDIA Corporation

ISBN: (纸本)9798400700156

We introduce Stream-K, a work-centric parallelization of matrix multiplication (GEMM) and related computations in dense linear algebra. Whereas contemporary decompositions are primarily tile-based, our method operates by partitioning an even share of the aggregate inner loop iterations among physical processing elements. this provides a near-perfect utilization of computing resources, regardless of how efficiently the output tiling for any given problem quantizes across the underlying processing elements.

关键词： GPU matrix-multiplication load-balancing

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Fast parallel Exact Inference on Bayesian Networks 23

Fast Parallel Exact Inference on Bayesian Networks

引用

Proceedings of the 28th acm sigplan Annual symposium on principles and practice of parallel programming

作者： Jiantong Jiang Zeyi Wen Atif Mansoor Ajmal Mian The University of Western Australia Hong Kong University of Science and Technology (Guangzhou)

ISBN: (纸本)9798400700156

Bayesian networks (BNs) are attractive, because they are graphical and interpretable machine learning models. However, exact inference on BNs is time-consuming, especially for complex problems. To improve the efficiency, we propose a fast BN exact inference solution named Fast-BNI on multi-core CPUs. Fast-BNI enhances the efficiency of exact inference through hybrid parallelism that tightly integrates coarse- and fine-grained parallelism. We also propose techniques to further simplify the bottleneck operations of BN exact inference. Fast-BNI source code is freely available at https://***/jjiantong/FastBN.

关键词： junction tree inference bayesian networks

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Beyond parallel programming with domain specific languages 14

Beyond parallel programming with domain specific languages

引用

Proceedings of the 19th acm sigplan symposium on principles and practice of parallel programming

作者： Kunle Olukotun Stanford University Stanford CA USA

ISBN: (纸本)9781450326568

Today, almost all computer architectures are parallel and heterogeneous; a combination of multiple CPUs, GPUs and specialized processors. this creates a challenging problem for application developers who want to develop high performance programs without the effort required to use low-level, architecture specific parallel programming models (e.g. OpenMP for CMPs, CUDA for GPUs, MPI for clusters). Domain-specific languages (DSLs) are a promising solution to this problem because they can provide an avenue for high-level application-specific abstractions with implicit parallelism to be mapped directly to low level architecture-specific programming models; providing both high programmer productivity and high execution *** this talk I will describe an approach to building high performance DSLs, which is based on DSL embedding in a general purpose programming language, metaprogramming and a DSL infrastructure called Delite. I will describe how we transform DSL programs into efficient first-order low-level code using domain specific optimization, parallelism and locality optimization with parallel patterns, and architecture-specific code generation. All optimizations and transformations are implemented in Delite: an extensible DSL compiler infrastucture that significantly reduces the effort required to develop new DSLs. Delite DSLs for machine learning, data querying, graph analysis, and scientific computing all achieve performance competitive with manually parallelized C++ code.

关键词： domain specific languages

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CuPBoP: A Framework to Make CUDA Portable 23

CuPBoP: A Framework to Make CUDA Portable

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Proceedings of the 28th acm sigplan Annual symposium on principles and practice of parallel programming

作者： Ruobing Han Jun Chen Bhanu Garg Jeffrey Young Jaewoong Sim Hyesoon Kim Georgia Institute of Technology Seoul National University

ISBN: (纸本)9798400700156

CUDA, as one of the most popular choices for GPU programming, can be executed only on NVIDIA GPUs. To execute CUDA on non-NVIDIA devices, researchers have proposed to translate CUDA to other programming languages. However, this approach cannot achieve high coverage due to the challenges in source-to-source *** propose a framework, CuPBoP, that executes CUDA programs on non-NVIDIA devices without relying on other programming languages. CuPBoP consists of two parts. the compilation part applies transformations on CUDA host/kernel IRs. the runtime part consists of the runtime libraries for CUDA built-in functions. For the CPU backends, compared with the existing frameworks, CuPBoP achieves the highest coverage on all CPUs that we evaluate (x86, aarch64, RISC-V).We make CuPBoP publicly available to inspire more works in this area 1.

关键词： AArch64 X86 portable programming CUDA RISC-V

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Performance portable C++ programming with RAJA 19

Performance portable C++ programming with RAJA

引用

Proceedings of the 24th symposium on principles and practice of parallel programming

作者： David Beckingsale Richard Hornung Tom Scogland Arturo Vargas Lawrence Livermore National Laboratory

ISBN: (纸本)9781450362252

With the rapid change of computing architectures, and variety of programming models; the ability to develop performance portable applications has become of great importance. this is particularly true in large production codes where developing and maintaining hardware specific versions is *** simplify the development of performance portable code, we introduce RAJA, our C++ library that allows developers to write single-source applications that can target multiple hardware and programming model back-ends. We provide a thorough introduction to all of RAJA features, and walk through some hands-on examples that will allow attendees to understand how RAJA might benefit their own applications. Attendees should bring a laptop computer to participate in the hands-on *** tutorial will introduce attendees to RAJA, a C++ library for developing performance portable applications. Attendees will learn how to write performance portable code that can execute on a range of programming models (OpenMP, CUDA, Intel TBB, and HCC) and hardware (CPU, GPU, Xeon Phi).Specifically, attendees will learn how to convert existing C++ applications to use RAJA, and how to use RAJA's programming abstractions to expose existing parallelism in their applications without complex algorithm rewrites. We will also cover specific guidelines for using RAJA in a large application, including some common "gotchas" and how to handle memory management. Finally, attendees will learn how to categorize loops to allow for simple and systematic performance tuning on any architecture.

关键词： parallel programming

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