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检索条件"任意字段=14th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming"

共 382 条记录，以下是61-70 订阅

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Scaling implicit parallelism via dynamic control replication 21

Scaling implicit parallelism via dynamic control replication

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26th acm sigplan symposium on principles and practice of parallel programming, PPoPP 2021

作者： Bauer, Michael Lee, Wonchan Slaughter, Elliott Jia, Zhihao Di Renzo, Mario Papadakis, Manolis Shipman, Galen McCormick, Patrick Garland, Michael Aiken, Alex NVIDIA United States Slac National Accelerator Laboratory United States Carnegie Mellon University United States Sapienza University of Rome Italy Los Alamos National Laboratory United States Stanford University United States

ISBN: (纸本)9781450382946

We present dynamic control replication, a run-time program analysis that enables scalable execution of implicitly parallel programs on large machines through a distributed and efficient dynamic dependence analysis. Dynamic control replication distributes dependence analysis by executing multiple copies of an implicitly parallel program while ensuring that they still collectively behave as a single execution. By distributing and parallelizing the dependence analysis, dynamic control replication supports efficient, on-the-fly computation of dependences for programs with arbitrary control flow at scale. We describe an asymptotically scalable algorithm for implementing dynamic control replication that maintains the sequential semantics of implicitly parallel programs. An implementation of dynamic control replication in the Legion runtime delivers the same programmer productivity as writing in other implicitly parallel programming models, such as Dask or TensorFlow, while providing better performance (11.4X and 14.9X respectively in our experiments), and scalability to hundreds of nodes. We also show that dynamic control replication provides good absolute performance and scaling for HPC applications, competitive in many cases with explicitly parallel programming systems. © 2021 acm.

关键词： parallel programming

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Advanced synchronization techniques for task-based runtime systems 21

Advanced synchronization techniques for task-based runtime s...

引用

26th acm sigplan symposium on principles and practice of parallel programming, PPoPP 2021

作者： Álvarez, David Sala, Kevin Maroñas, Marcos Roca, Aleix Beltran, Vincenç Barcelona Supercomputing Center Barcelona Spain

ISBN: (纸本)9781450382946

Task-based programming models like OmpSs-2 and OpenMP provide a flexible data-flow execution model to exploit dynamic, irregular and nested parallelism. Providing an efficient implementation that scales well with small granularity tasks remains a challenge, and bottlenecks can manifest in several runtime components. In this paper, we analyze the limiting factors in the scalability of a task-based runtime system and propose individual solutions for each of the challenges, including a wait-free dependency system and a novel scalable scheduler design based on delegation. We evaluate how the optimizations impact the overall performance of the runtime, both individually and in combination. We also compare the resulting runtime against state of the art OpenMP implementations, showing equivalent or better performance, especially for fine-grained tasks. © 2021 acm.

关键词： parallel programming

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On the parallel I/O optimality of linear algebra kernels: Near-optimal LU factorization 21

On the parallel I/O optimality of linear algebra kernels: Ne...

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26th acm sigplan symposium on principles and practice of parallel programming, PPoPP 2021

作者： Kwasniewski, Grzegorz Ben-Nun, Tal Ziogas, Alexandros Nikolaos Schneider, Timo Besta, MacIej Hoefler, Torsten Timo Schneider Maciej Besta Torsten Hoefler Department of Computer Science Eth Zurich Switzerland

ISBN: (纸本)9781450382946

Dense linear algebra kernels are fundamental components of many scientific computing applications. In this work we present a novel method of deriving parallel I/O lower bounds for this broad family of programs. Based on the X-Partitioning abstraction, our method explicitly captures inter-statement dependencies. Applying our analysis to LU factorization, we derive COnfLUX, an LU algorithm with the parallel I/O cost of N3/([EQUATION]) communicated elements per processor - only 1/3× over our established lower bound. We evaluate COnfLUX on various problem sizes, demonstrating empirical results that match our theoretical analysis, communicating less than Cray ScaLAPACK, SLATE, and the asymptotically-optimal CANDMC library. Running on 1,024 nodes of Piz Daint, COnfLUX communicates 1.6× less than the second-best implementation and is expected to communicate 2.1× less on a full-scale run on Summit. © 2021 Owner/Author.

关键词： Factorization

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BiPart: A parallel and deterministic hypergraph partitioner 21

BiPart: A parallel and deterministic hypergraph partitioner

引用

26th acm sigplan symposium on principles and practice of parallel programming, PPoPP 2021

作者： Maleki, Sepideh Agarwal, Udit Burtscher, Martin Pingali, Keshav The University of Texas Austin United States Texas State University United States

ISBN: (纸本)9781450382946

Hypergraph partitioning is used in many problem domains including VLSI design, linear algebra, Boolean satisfiability, and data mining. Most versions of this problem are NP-complete or NP-hard, so practical hypergraph partitioners generate approximate partitioning solutions for all but the smallest inputs. One way to speed up hypergraph partitioners is to exploit parallelism. However, existing parallel hypergraph partitioners are not deterministic, which is considered unacceptable in domains like VLSI design where the same partitions must be produced every time a given hypergraph is partitioned. In this paper, we describe BiPart, the first deterministic, parallel hypergraph partitioner. Experimental results show that BiPart outperforms state-of-the-art hypergraph partitioners in runtime and partition quality while generating partitions deterministically. © 2021 acm.

关键词： Data mining

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DAPPLE: A pipelined data parallel approach for training large models 21

DAPPLE: A pipelined data parallel approach for training larg...

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26th acm sigplan symposium on principles and practice of parallel programming, PPoPP 2021

作者： Fan, Shiqing Rong, Yi Meng, Chen Cao, Zongyan Wang, Siyu Zheng, Zhen Wu, Chuan Long, Guoping Yang, Jun Xia, Lixue Diao, Lansong Liu, Xiaoyong Lin, Wei Alibaba Group China

ISBN: (纸本)9781450382946

It is a challenging task to train large DNN models on sophisticated GPU platforms with diversified interconnect capabilities. Recently, pipelined training has been proposed as an effective approach for improving device utilization. However, there are still several tricky issues to address: improving computing efficiency while ensuring convergence, and reducing memory usage without incurring additional computing costs. We propose DAPPLE, a synchronous training framework which combines data parallelism and pipeline parallelism for large DNN models. It features a novel parallelization strategy planner to solve the partition and placement problems, and explores the optimal hybrid strategies of data and pipeline parallelism. We also propose a new runtime scheduling algorithm to reduce device memory usage, which is orthogonal to re-computation approach and does not come at the expense of training throughput. Experiments show that DAPPLE planner consistently outperforms strategies generated by PipeDream's planner by up to 3.23× speedup under synchronous training scenarios, and DAPPLE runtime outperforms GPipe by 1.6× speedup of training throughput and saves 12% of memory consumption at the same time. © 2021 acm.

关键词： Scheduling algorithms

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An ownership policy and deadlock detector for promises 21

An ownership policy and deadlock detector for promises

引用

26th acm sigplan symposium on principles and practice of parallel programming, PPoPP 2021

作者： Voss, Caleb Sarkar, Vivek Georgia Institute of Technology United States

ISBN: (纸本)9781450382946

Task-parallel programs often enjoy deadlock freedom under certain restrictions, such as the use of structured join operations, as in Cilk and X10, or the use of asynchronous task futures together with deadlock-avoiding policies such as Known Joins or Transitive Joins. However, the promise, a popular synchronization primitive for parallel tasks, does not enjoy deadlock-freedom guarantees. Promises can exhibit deadlock-like bugs;however, the concept of a deadlock is not currently well-defined for promises. To address these challenges, we propose an ownership semantics in which each promise is associated to the task which currently intends to fulfill it. Ownership immediately enables the identification of bugs in which a task fails to fulfill a promise for which it is responsible. Ownership further enables the discussion of deadlock cycles among tasks and promises and allows us to introduce a robust definition of deadlock-like bugs for promises. Cycle detection in this context is non-trivial because it is concurrent with changes in promise ownership. We provide a lock-free algorithm for precise runtime deadlock detection. We show how to obtain the memory consistency criteria required for the correctness of our algorithm under TSO and the Java and C++ memory models. An evaluation compares the execution time and memory usage overheads of our detection algorithm on benchmark programs relative to an unverified baseline. Our detector exhibits a 12% (1.12×) geometric mean time overhead and a 6% (1.06×) geometric mean memory overhead, which are smaller overheads than in past approaches to deadlock cycle detection. © 2021 acm.

关键词： parallel programming

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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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parallel binary code analysis 21

Parallel binary code analysis

引用

26th acm sigplan symposium on principles and practice of parallel programming, PPoPP 2021

作者： Meng, Xiaozhu Anderson, Jonathon M. Mellor-Crummey, John Krentel, Mark W. Miller, Barton P. Milaković, Sran Department of Computer Science Rice University Houston TX United States Computer Sciences Department University of Wisconsin-Madison MadisonWI United States

ISBN: (纸本)9781450382946

Binary code analysis is widely used to help assess a program's correctness, performance, and provenance. Binary analysis applications often construct control flow graphs, analyze data flow, and use debugging information to understand how machine code relates to source lines, inlined functions, and data types. To date, binary analysis has been single-threaded, which is too slow for convenient use in performance tuning workflows where it is used to help attribute performance to complex applications with large binaries. this paper describes our design and implementation for accelerating the task of constructing control flow graphs (CFGs) from binaries by using multithreading. Prior research focuses on algorithms for analysis of challenging code constructs encountered while constructing CFGs, including functions sharing code, jump tables, non-returning functions, and tail calls. these algorithms are described from a program analysis perspective and are not suitable for direct parallel implementation. We abstract the task of constructing CFGs as repeated applications of several core CFG operations that include creating functions, basic blocks, and edges. We then derive CFG operation dependency, commutativity, and monotonicity. these operation properties guide our design of a new parallel analysis for constructing CFGs. Using 64 threads, we achieved as much as 25× speedup for constructing CFGs and 8× for a performance analysis tool that leverages our new analysis to recover program structure. © 2021 acm.

关键词： Graphic methods

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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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ParGeo: a library for parallel computational geometry 22

ParGeo: a library for parallel computational geometry

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

作者： Yiqiu Wang Shangdi Yu Laxman Dhulipala Yan Gu Julian Shun MIT CSAIL UC Riverside

We present ParGeo, a multicore library for computational geometry algorithms. We describe two of the algorithms from ParGeo, convex hull and the smallest enclosing ball, and present a short evaluation of all implement...

ISBN: (纸本)9781450392044

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