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检索条件"任意字段=17th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming"

共 368 条记录，以下是201-210 订阅

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INFINEL: An efficient GPU-based processing method for unpredictable large output graph queries 24

INFINEL: An efficient GPU-based processing method for unpred...

引用

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

作者： Park, Sungwoo Oh, Seyeon Kim, Min-Soo Korea Adv Inst Sci & Technol Seoul South Korea GraphAI Seoul South Korea

ISBN: (纸本)9798400704352

With the introduction of GPUs, which are specialized for iterative parallel computations, the execution of computationintensive graph queries using a GPU has seen significant performance improvements. However, due to the memory constraints of GPUs, there has been limited research on handling large-scale output graph queries with unpredictable output sizes on a GPU. Traditionally, two-phase methods have been used, where the query is re-executed after splitting it into sub-tasks while only considering the size of the output in a static manner. However, two-phase methods become highly inefficient when used with graph data with extreme skew, failing to maximize the GPU performance. this paper proposes INFINEL, which handles unpredictable large output graph queries in a one-phase method through chunk allocation per thread and kernel stop/restart methods. We also propose applicable optimization techniques due to the corresponding unique characteristics of operating with low time/space overhead and not heavily relying on the GPU output buffer size. through extensive experiments, we demonstrate that our one-phase method of INFINEL improves the performance by up to 31.5 times over the conventional twophase methods for triangle listing ULO query.

关键词： Graph query processing Large output parallel computing GPU

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Flattening and parallelizing irregular, recurrent loop nests 95

Flattening and parallelizing irregular, recurrent loop nests

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

作者： Ghuloum, Anwar M. Fisher, Allan L. Carnegie Mellon Univ Pittsburgh PA United States

ISBN: (纸本)9780897917001

Irregular loop nests in which the loop bounds are determined dynamically by indexed arrays are difficult to compile into expressive parallel constructs, such as segmented scans and reductions. In this paper, we describe a suite of transformations to automatically parallelize such irregular loop nests, even in the presence of recurrences. We describe a simple, general loop flattening transformation, along with new optimizations which make it a viable compiler transformation. A robust recurrence parallelization technique is coupled to the loop flattening transformation, allowing parallelization of segmented reductions, scans, and combining-sends over arbitrary associative operators. We discuss the implementation and performance results of the transformations in a parallelizing Fortran 77 compiler for the Cray C90 supercomputer. In particular, we focus on important sparse matrix-vector multiplication kernels, for one of which we are able to automatically derive an algorithm used by one of the fastest library routines available.

关键词： parallel processing systems

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Combining phase identification and statistic modeling for automated parallel benchmark generation 2015

Combining phase identification and statistic modeling for au...

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20th acm sigplan symposium on principles and practice of parallel programming, PPoPP 2015

作者： Jin, Ye Liu, Mingliang Ma, Xiaosong Liu, Qing Logan, Jeremy Podhorszki, Norbert Choi, Jong Youl Klasky, Scott NCSU United States QCRI Qatar Oak Ridge National Lab. United States

ISBN: (纸本)9781450332057

parallel application benchmarks are indispensable for evaluating/optimizing HPC software and hardware. However, it is very challenging and costly to obtain high-fidelity benchmarks reflecting the scale and complexity of state-of-the-art parallel applications. Hand-extracted synthetic benchmarks are time- and labor-intensive to create. Real applications themselves, while offering most accurate performance evaluation, are expensive to compile, port, reconfigure, and often plainly inaccessible due to security or ownership concerns. this work contributes APPRIME, a novel tool for trace-based automatic parallel benchmark generation. Taking as input standard communication-I/O traces of an application's execution, it couples accurate automatic phase identification with statistical regeneration of event parameters to create compact, portable, and to some degree reconfigurable parallel application benchmarks. Experiments with four NAS parallel Benchmarks (NPB) and three real scientific simulation codes confirm the fidelity of APPRIME benchmarks. they retain the original applications' performance characteristics, in particular the relative performance across platforms.

关键词： Benchmarking

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Array operation synthesis scheme to optimize Fortran 90 programs

Array operation synthesis scheme to optimize Fortran 90 prog...

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

作者： Hwang, Gwan-Hwan Lee, Jenq Kuen Ju, Dz-Ching Natl Tsing-Hua Univ Taiwan

An increasing number of programming languages, such as Fortran 90 and APL, are providing a rich set of intrinsic array functions and array expressions. these constructs which constitute an important part of data parallel languages provide excellent opportunities for compiler optimizations. In this paper, we present a new approach to combine consecutive data access patterns of array constructs into a composite access function to the source arrays. Our scheme is based on the composition of access functions, which is similar to a composition of mathematic functions. Our new scheme can handle not only data movements of arrays of different numbers of dimensions and segmented array operations but also masked array expressions and multiple sources array operations. As a result, our proposed scheme is the first synthesis scheme which can synthesize Fortran 90 RESHAPE, EOSHIFT, MERGE, and WHERE constructs together. Experimental results show speedups from 1.21 to 2.95 for code fragments from real applications on a Sequent multiprocessor machine by incorporating the proposed optimizations.

关键词： FORTRAN (programming language)

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Griffin: Uniting CPU and GPU in Information Retrieval Systems for Intra-Query parallelism 18

Griffin: Uniting CPU and GPU in Information Retrieval System...

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23rd acm sigplan symposium on principles and practice of parallel programming (PPoPP)

作者： Liu, Yang Wang, Jianguo Swanson, Steven WDC Res Chippenham Wilts England Univ Calif San Diego San Diego CA USA

ISBN: (纸本)9781450349826

Interactive information retrieval services, such as enterprise search and document search, must provide relevant results with consistent, low response times in the face of rapidly growing data sets and query loads. these growing demands have led researchers to consider a wide range of optimizations to reduce response latency, including query processing parallelization and acceleration with co-processors such as GPUs. However, previous work runs queries either on GPU or CPU, ignoring the fact that the best processor for a given query depends on the query's characteristics, which may change as the processing proceeds. We present Griffin, an IR systems that dynamically combines GPU- and CPU-based algorithms to process individual queries according to their characteristics. Griffin uses state-of-the-art CPU-based query processing techniques and incorporates a novel approach to GPU-based query evaluation. Our GPU-based approach, as far as we know, achieves the best available GPU search performance by leveraging a new compression scheme and exploiting an advanced merge-based intersection algorithm. We evaluate Griffin with real world queries and datasets, and show that it improves query performance by 10x compared to a highly optimized CPU-only implementation, and 1.5x compared to our GPU-approach running alone. We also find that Griffin helps reduce the 95th-, 99th-, and 99.9th-percentile query response time by 10.4x, 16.1x, and 26.8x, respectively.

关键词： Information Retrieval Search Engines Query Processing GPU parallel Algorithms

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parallel programming with Big Operators

Parallel Programming with Big Operators

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

作者： Park, Changhee Steele, Guy L., Jr. Tristan, Jean-Baptiste Korea Adv Inst Sci & Technol Taejon South Korea

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.

关键词： parallelism Mathematical notation Scala

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PiPAD: Pipelined and parallel Dynamic GNN Training on GPUs 23

PiPAD: Pipelined and Parallel Dynamic GNN Training on GPUs

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

作者： Wang, Chunyang Sun, Desen Bai, Yuebin Beihang University Beijing China

ISBN: (纸本)9798400700156

Dynamic Graph Neural Networks (DGNNs) have been widely applied in various real-life applications, such as link prediction and pandemic forecast, to capture both static structural information and temporal characteristics from dynamic graphs. Combining both time-dependent and -independent components, DGNNs manifest substantial parallel computation and data reuse potentials, but suffer from severe memory access inefficiency and data transfer overhead under the canonical one-graph-at-a-time training pattern. To tackle these challenges, we propose PiPAD, a Pipelined and parallel DGNN training framework for the end-to-end performance optimization on GPUs. From both algorithm and runtime level, PiPAD holistically reconstructs the overall training paradigm from the data organization to computation manner. Capable of processing multiple graph snapshots in parallel, PiPAD eliminates unnecessary data transmission and alleviates memory access inefficiency to improve the overall performance. Our evaluation across various datasets shows PiPAD achieves 1.22 × - 9.57× speedup over the state-of-the-art DGNN frameworks on three representative models. © 2023 acm.

关键词： Memory architecture

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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...

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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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SEP-Graph: Finding Shortest Execution Paths for Graph Processing under a Hybrid Framework on GPU 19

SEP-Graph: Finding Shortest Execution Paths for Graph Proces...

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24th acm sigplan symposium on principles and practice of parallel programming (PPoPP)

作者： Wang, Hao Geng, Liang Lee, Rubao Hou, Kaixi Zhang, Yanfeng Zhang, Xiaodong Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA Northeastern Univ Dept Comp Sci & Engn Shenyang Peoples R China United Parallel Comp Corp Atlanta DE USA Virginia Tech Dept Comp Sci Blacksburg VA USA

ISBN: (纸本)9781450362252

In general, the performance of parallel graph processing is determined by three pairs of critical parameters, namely synchronous or asynchronous execution mode (Sync or Async), Push or Pull communication mechanism (Push or Pull), and Data-driven or Topology-driven traversing scheme (DD or TD), which increases the complexity and sophistication of programming and system implementation of GPU. Existing graph-processing frameworks mainly use a single combination in the entire execution for a given application, but we have observed their variable and suboptimal performance. In this paper, we present SEP-Graph, a highly efficient software framework for graph-processing on GPU. the hybrid execution mode is automatically switched among three pairs of parameters, with an objective to achieve the shortest execution time in each iteration. We also apply a set of optimizations to SEP-Graph, considering the characteristics of graph algorithms and underlying GPU architectures. We show the effectiveness of SEP-Graph based on our intensive and comparative performance evaluation on NVIDIA 1080, P100, and V100 GPUs. Compared with existing and representative GPU graph-processing framework Groute and Gunrock, SEP-Graph can reduce execution time up to 45.8 times and 39.4 times.

关键词： Graph Algorithms GPU Hybrid

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LoPC: modeling contention in parallel algorithms

LoPC: modeling contention in parallel algorithms

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Proceedings of the 1997 6th acm sigplan symposium on principles and practice of parallel programming

作者： Frank, Matthew I. Agarwal, Anant Vernon, Mary K. Massachusetts Inst of Technology Cambridge United States

parallel algorithm designers need computational models that take first order system costs into account, but are also simple enough to use in practice. this paper introduces the LoPC model, which is inspired by the LogP model but accounts for contention for message processing resources in parallel algorithms on a multiprocessor or network of workstations. LoPC takes the L, o and P parameters directly from the LogP model and uses them to predict the cost of contention, C. this paper defines the LoPC model and derives the general form of the model for parallel applications that communicate via active messages. Model modifications for systems that implement coherent shared memory abstractions are also discussed. We carry out the analysis for two important classes of applications that have irregular communication. In the case of parallel applications with homogeneous all-to-any communication, such as sparse matrix computations, the analysis yields a simple rule of thumb and insight into contention costs. In the case of parallel client-server algorithms, the LoPC analysis provides a simple and accurate calculation of the optimal allocation of nodes between clients and servers. the LoPC estimates for these applications are shown to be accurate when compared against event driven simulation and against a sparse matrix computation on the MIT Alewife multiprocessor.

关键词： parallel algorithms

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