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24 篇 会议

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检索条件"任意字段=18th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming, PPoPP 2013"

共 24 条记录，以下是1-10 订阅

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POSTER: Stream-K:Work-centric parallel Decomposition for Dense Matrix-Matrix Multiplication on the GPU 28

POSTER: Stream-K:Work-centric Parallel Decomposition for Den...

引用

28th acm sigplan Annual symposium on principles and practice of parallel programming, ppopp 2023

作者： Osama, Muhammad Merrill, Duane Cecka, Cris Garland, Michael Owens, John D. University of California Davis United States 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. © 2023 Owner/Author.

关键词： Graphics processing unit

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POSTER: ParGeo: A Library for parallel Computational Geometry 27

POSTER: ParGeo: A Library for Parallel Computational Geometr...

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27th acm sigplan symposium on principles and practice of parallel programming (ppopp)

作者： Wang, Yiqiu Yu, Shangdi Dhulipala, Laxman Gu, Yan Shun, Julian IMIT CSAIL Riverside CA USA

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... 详细信息

ISBN: (纸本)9781450392044

关键词： Computational geometry

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ppopp 2020 - Proceedings of the 2020 25th acm sigplan symposium on principles and practice of parallel programming

PPoPP 2020 - Proceedings of the 2020 25th ACM SIGPLAN Sympos...

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Journal Track at 18th International Semantic Web Conference, JT@ISWC 2019

the proceedings contain 13 papers. the topics discussed include: a guided walk into link key candidate extraction with relational concept analysis;reflections on profiling and cataloguing the content of SPARQL endpoints using SPORTAL;reflections on: modeling linked open statistical data;reflections on: DCAT-AP representation of Czech national open data catalog and its impact;reflections on: deep learning for noise-tolerant RDFS reasoning;reflections on: finding melanoma drugs through a probabilistic knowledge graph;reflections on: knowledge graph fact prediction via knowledge-enriched tensor factorization;the semantic sensor network ontology, revamped;and reflections on: knowmore - knowledge base augmentation with structured web markup.

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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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ppopp 2013 - Proceedings of the 2013 acm sigplan symposium on principles and practice of parallel programming

PPoPP 2013 - Proceedings of the 2013 ACM SIGPLAN Symposium o...

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18th acm sigplan symposium on principles and practice of parallel programming, ppopp 2013

ISBN: (纸本)9781450319225

the proceedings contain 45 papers. the topics discussed include: a peta-scalable CPU-GPU algorithm for global atmospheric simulations;adoption protocols for fanout-optimal fault-tolerant termination detection;betweenness centrality: algorithms and implementations;complexity analysis and algorithm design for reorganizing data to minimize non-coalesced memory accesses on GPU;fast concurrent queues for x86 processors;FASTLANE: improving performance of software transactional memory for low thread counts;Ligra: a lightweight graph processing framework for shared memory;ownership passing: efficient distributed memory programming on multi-core systems;parallel suffix array and least common prefix for the GPU;Streamscan: fast scan algorithms for GPUs without global barrier synchronization;using hardware transactional memory to correct and simplify a readers-writer lock algorithm;and exploring different automata representations for efficient regular expression matching on GPUs.

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Reducing Contention through Priority Updates 13

Reducing Contention Through Priority Updates

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

作者： Shun, Julian Blelloch, Guy E. Fineman, Jeremy T. Gibbons, Phillip B. Carnegie Mellon Univ Pittsburgh PA 15213 USA Georgetown Univ Washington DC 20057 USA Intel Labs Pittsburgh PA USA

No abstract available.

ISBN: (纸本)9781450319225

No abstract available.

关键词： Experimentation Performance parallel programming Contention

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Correct and efficient work-stealing for weak memory models 13

Correct and efficient work-stealing for weak memory models

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18th acm sigplan symposium on principles and practice of parallel programming, ppopp 2013

作者： Lê, Nhat Minh Pop, Antoniu Cohen, Albert Zappa Nardelli, Francesco INRIA ENS Paris Paris France

ISBN: (纸本)9781450319225

Chase and Lev's concurrent deque is a key data structure in shared-memory parallel programming and plays an essential role in work-stealing schedulers. We provide the first correctness proof of an optimized implementation of Chase and Lev's deque on top of the POWER and ARM architectures: these provide very relaxed memory models, which we exploit to improve performance but considerably complicate the reasoning. We also study an optimized x86 and a portable C11 implementation, conducting systematic experiments to evaluate the impact of memory barrier optimizations. Our results demonstrate the benefits of hand tuning the deque code when running on top of relaxed memory models. © 2013 acm.

关键词： parallel programming

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parallel programming with big operators 13

Parallel programming with big operators

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18th acm sigplan symposium on principles and practice of parallel programming, ppopp 2013

作者： Park, Changhee Steele Jr., Guy L. Tristan, Jean-Baptiste KAIST Seoul Korea Republic of Oracle Labs. Burlington MA United States

ISBN: (纸本)9781450319225

In the sciences, it is common to use the so-called "big operator" notation to express the iteration of a binary operator (the reducer) over a collection of values. Such a notation typically assumes that the reducer is associative and abstracts the iteration process. Consequently, from a programming point-of-view, we can organize the reducer operations to minimize the depth of the overall reduction, allowing a potentially parallel evaluation of a big operator expression. We believe that the big operator notation is indeed an effective construct to express parallel computations in the Generate/Map/Reduce programming model, and our goal is to introduce it in programming languages to support parallel programming. the effective definition of such a big operator expression requires a simple way to generate elements, and a simple way to declare algebraic properties of the reducer (such as its identity, or its commutativity). In this poster, we want to present an extension of Scala with support for big operator expressions. We show how big operator expressions are defined and how the API is organized to support the simple definition of reducers with their algebraic properties. © 2013 Authors.

关键词： parallel programming

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ZOOMM: A parallel web browser engine for multicore mobile devices 13

ZOOMM: A parallel web browser engine for multicore mobile de...

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18th acm sigplan symposium on principles and practice of parallel programming, ppopp 2013

作者： Caşcaval, CǍlin Fowler, Seth Montesinos-Ortego, Pablo Piekarski, Wayne Reshadi, Mehrdad Robatmili, Behnam Weber, Michael Bhavsar, Vrajesh Qualcomm Research Silicon Valley Santa Clara CA United States

ISBN: (纸本)9781450319225

We explore the challenges in expressing and managing concurrency in browsers on mobile devices. Browsers are complex applications that implement multiple standards, need to support legacy behavior, and are highly dynamic and interactive. We present ZOOMM, a highly concurrent web browser engine prototype and show how concurrency is effectively exploited at different levels: speed up computation performance, preload network resources, and preprocess resources outside the critical path of page loading. On a dual-core Android mobile device we demonstrate that ZOOMM is two times faster than the native WebKit based browser when loading the set of pages defined in the Vellamo benchmark. © 2013 acm.

关键词： Web browsers

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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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