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检索条件"任意字段=8th ACM SIGPLAN Symposium on the Principles and Practice of Parallel Computing"

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Optimization of asynchronous graph processing on GPU with hybrid coloring model 2015

Optimization of asynchronous graph processing on GPU with hy...

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

作者： Shi, Xuanhua Liang, Junling Di, Sheng He, Bingsheng Jin, Hai Lu, Lu Wang, Zhixiang Luo, Xuan Zhong, Jianlong Services Computing Technology and System Lab. Cluster and Grid Computing Lab. School of Computer Science and Technology Huazhong University of Science and Technology China Argonne National Laboratory United States School of Computer Engineering Nanyang Technological University Singapore Singapore

ISBN: (纸本)9781450332057

Modern GPUs have been widely used to accelerate the graph processing for complicated computational problems regarding graph theory. Many parallel graph algorithms adopt the asynchronous computing model to accelerate the iterative convergence. Unfortunately, the consistent asynchronous computing requires locking or the atomic operations, leading to significant penalties/overheads when implemented on GPUs. To this end, coloring algorithm is adopted to separate the vertices with potential updating conflicts, guaranteeing the consistency/correctness of the parallel processing. We propose a light-weight asynchronous processing framework called Frog with a hybrid coloring model. We find that majority of vertices (about 80%) are colored with only a few colors, such that they can be read and updated in a very high degree of parallelism without violating the sequential consistency. Accordingly, our solution will separate the processing of the vertices based on the distribution of colors.

关键词： Graphics processing unit

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PPoPP'12 - Proceedings of the 2012 acm sigplan symposium on principles and practice of parallel Programming

PPoPP'12 - Proceedings of the 2012 ACM SIGPLAN Symposium on ...

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17th acm sigplan symposium on principles and practice of parallel Programming, PPoPP'12

ISBN: (纸本)9781450311601

the proceedings contain 57 papers. the topics discussed include: scalable framework for mapping streaming applications onto multi-GPU systems;efficient performance evaluation of memory hierarchy for highly multithreaded graphics processors;extending a C-like language for portable SIMD programming;DOJ: dynamically parallelizing object-oriented programs;GPU-based NFA implementation for memory efficient high speed regular expression matching;concurrent tries with efficient non-blocking snapshots;deterministic parallel random-number generation for dynamic-multithreading platforms;algorithm-based fault tolerance for dense matrix factorizations;revisiting the combining synchronization technique;FlexBFS: a parallelism-aware implementation of breadth-first search on GPU;optimizing remote accesses for offloaded kernels: application to high-level synthesis for FPGA;and the boat hull model: adapting the roofline model to enable performance prediction for parallel computing.

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parallel execution of multi-set constraint rewrite rules 08

Parallel execution of multi-set constraint rewrite rules

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PPDP 2008: 10th International acm sigplan symposium on principles and practice of Declarative Programming

作者： Sulzmann, Martin Lam, Edmund S. L. Programming Logics and Semantics Group IT University of Copenhagen Rued Langgaards Vej 7 2300 Copenhagen S Denmark School of Computing National University of Singapore S16 Level 5 3 Science Drive 2 Singapore 117543 Singapore

ISBN: (纸本)9781605581170

Multi-set constraint rewriting allows for a highly parallel computational model and has been used in a multitude of application domains such as constraint solving, agent specification etc. Rewriting steps can be applied simultaneously as long as they do not interfere with each other. We wish that the underlying constraint rewrite implementation executes rewrite steps in parallel on increasingly popular becoming multi-core architectures. We design and implement efficient algorithms which allow for the parallel execution of multi-set constraint rewrite rules. Our experiments show that we obtain some significant speed-ups on multi-core architectures. Copyright © 2008 acm.

关键词： Computer architecture

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An Adaptive Performance Modeling Tool for GPU Architectures

An Adaptive Performance Modeling Tool for GPU Architectures

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15th acm sigplan symposium on principles and practice of parallel Programming

作者： Baghsorkhi, Sara S. Delahaye, Matthieu Patel, Sanjay J. Gropp, William D. Hwu, Wen-mei W. Univ Illinois Urbana IL 61801 USA

ISBN: (纸本)9781605587080

this paper presents an analytical model to predict the performance of general-purpose applications on a GPU architecture. the model is designed to provide performance information to an auto-tuning compiler and assist it in narrowing down the search to the more promising implementations. It can also be incorporated into a tool to help programmers better assess the performance bottlenecks in their code. We analyze each GPU kernel and identify how the kernel exercises major GPU microarchitecture features. To identify the performance bottlenecks accurately, we introduce an abstract interpretation of a GPU kernel, work flow graph, based on which we estimate the execution time of a GPU kernel. We validated our performance model on the NVIDIA GPUs using CUDA (Compute Unified Device Architecture). For this purpose, we used data parallel benchmarks that stress different GPU microarchitecture events such as uncoalesced memory accesses, scratch-pad memory bank conflicts, and control flow divergence, which must be accurately modeled but represent challenges to the analytical performance models. the proposed model captures full system complexity and shows high accuracy in predicting the performance trends of different optimized kernel implementations. We also describe our approach to extracting the performance model automatically from a kernel code.

关键词： Design Measurement Performance Analytical model GPU parallel programming Performance estimation

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PPoPP'08 - Proceedings of the 2008 acm sigplan symposium on principles and practice of parallel Programming

PPoPP'08 - Proceedings of the 2008 ACM SIGPLAN Symposium on ...

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13th acm sigplan symposium on principles and practice of parallel Programming, PPoPP'08

ISBN: (纸本)9781595939609

the proceedings contain 42 papers. the topics discussed include: automatic data movement and computation mapping for multi-level parallel architectures with explicitly managed memories;type inference for locality analysis of distributed data structures;quasi-static scheduling for safe futures;scalable packet classification using interpreting: a cross-platform multi-core solution;FastForward for efficient pipeline parallelism: a cache-optimized concurrent lock-free queue;matrix product on heterogeneous master-worker platforms;high performance dense linear algebra on a spatially distributed processor;optimization principles and application performance evaluation of a multithreaded GPU using CUDA;a case study in SIMD text processing with parallel bit streams: UTF-8 to UTF-16 transcoding;programming with tiles;design and implementation of a high-performance MPI for C# and the common language infrastructure;and a portable runtime interface for multi-level memory hierarchies.

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CuLDACGS: Solving large-scale LDA problems on GPUs 19

CuLDACGS: Solving large-scale LDA problems on GPUs

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

作者： Xie, Xiaolong Liang, Yun Li, Xiuhong Tan, Wei Center for Energy-Efficient Computing and Applications EECS Peking University Beijing China Alibaba Group Hangzhou China Citadel LLC ChicagoIL United States

ISBN: (纸本)9781450362252

GPUs have benefited many ML algorithms. However, we observe that the performance of existing Latent Dirichlet Allocation(LDA) solutions on GPUs are not satisfying. We present CuLDA-CGS, an eficient approach to accelerate large-scale LDA problems. We delicately design workload partition and synchronization mechanism to exploit multiple GPUs. We also optimize the algorithm from the sampling algorithm, parallelization, and data compression perspectives. Experiment evaluations show that compared with the state-of-the-art LDA solutions, CuLDA-CGS outperforms them by a large margin (up to 7.3X) on a single GPU. © 2019 Copyright held by the owner/author(s).

关键词： Graphics processing unit

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Evaluating the locality benefits of active messages

Evaluating the locality benefits of active messages

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

作者： Spertus, Ellen Dally, William J. Massachusetts Inst of Technology Cambridge United States

A major challenge in fine-grained computing is achieving locality without excessive scheduling overhead. We built two J-Machine implementations of a fine-grained programming model, the Berkeley threaded Abstract Machine. One implementation takes an Active Messages approach, maintaining a scheduling hierarchy in software in order to improve data cache performance. Another approach relies on the J-Machine's message queues and fast task switch, lowering the control costs at the expense of data locality. Our analysis measures the costs and benefits of each approach, for a variety of programs and cache configurations. the Active Messages implementation is strongest when miss penalties are high and for the finest-grained programs. the hardware-buffered implementation is strongest in direct-mapped caches, where it achieves substantially better instruction cache performance.

关键词： parallel processing systems

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New model for integrated nested task and data parallel programming

New model for integrated nested task and data parallel progr...

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

作者： Subhlok, Jaspal Yang, Bwolen Carnegie Mellon Univ Pittsburgh United States

High Performance Fortran (HPF) has emerged as a standard language for data parallel computing. However, a wide variety of scientific applications are best programmed by a combination of task and data parallelism. therefore, a good model of task parallelism is important for continued success of HPF for parallel programming. this paper presents a task parallelism model that is simple, elegant, and relatively easy to implement in an HPF environment. Task parallelism is exploited by mechanisms for dividing processors into sub-groups and mapping computations and data onto processor subgroups. this model of task parallelism has been implemented in the Fx compiler at Carnegie Mellon University. the paper addresses the main issues in compiling integrated task and data parallel programs and reports on the use of this model for programming various flat and nested task structures. Performance results are presented for a set of programs spanning signal processing, image processing, computer vision and environment modeling. A variant of this task model is a new approved extension of HPF and this paper offers insight into the power of expression and ease of implementation of this extension.

关键词： parallel processing systems

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Why is graphics hardware so fast? 05

Why is graphics hardware so fast?

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2005 acm sigplan symposium on principles and Practise of parallel Programming, PROPP 05

作者： Hanrahan, Pat Stanford University Gates Computer Science Building Stanford CA 94305-4070 Department of Computer Science and Electrical Engineering Stanford University

ISBN: (纸本)9781595930804

NVIDIA has claimed that their graphics processors (or GPUs) are improving at a rate three times faster than Moore's Law for processors. A $25 GPU is rated from 50-100 gigaflops and approximately 1 teraop (8-bit ops). Alongside this increase in performance is new functionality. the most recent innovation is user-programmable vertex and fragment stages that allow GPUs to compute a wide range of new visual effects enabling movie-quality games. Announced chips have as many as 200 programmable floating point units operating in parallel. the result is that the latest generation of commodity graphics and game chips are powerful data-parallel computers. Why are these graphics processors so fast? Will the future performance of GPUs continue to increase faster than CPUs? Can these GPUs be used for scientific computing? And, if so, how might they be programmed?

关键词： parallel processing systems

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Triolet: A programming system that unifies algorithmic skeleton interfaces for high-performance cluster computing 14

Triolet: A programming system that unifies algorithmic skele...

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2014 19th acm sigplan symposium on principles and practice of parallel Programming, PPoPP 2014

作者： Rodrigues, Christopher Jablin, thomas Dakkak, Abdul Hwu, Wen-Mei University of Illinois at Urbana-Champaign United States

ISBN: (纸本)9781450326568

Functional algorithmic skeletons promise a high-level programming interface for distributed-memory clusters that free developers from concerns of task decomposition, scheduling, and communication. Unfortunately, prior distributed functional skeleton frameworks do not deliver performance comparable to that achievable in a low-level distributed programming model such as C with MPI and OpenMP, even when used in concert with high-performance array libraries. there are several causes: they do not take advantage of shared memory on each cluster node;they impose a fixed partitioning strategy on input data;and they have limited ability to fuse loops involving skeletons that produce a variable number of outputs per input. We address these shortcomings in the Triolet programming language through a modular library design that separates concerns of parallelism, loop nesting, and data partitioning. We show how Triolet substantially improves the parallel performance of algorithms involving array traversals and nested, variable-size loops over what is achievable in Eden, a distributed variant of Haskell. We further demonstrate how Triolet can substantially simplify parallel programming relative to C with MPI and OpenMP while achieving 23.100% of its performance on a 128-core cluster. Copyright © 2014 acm.

关键词： parallel programming

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