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检索条件"任意字段=Proceedings of the 26th ACM SIGPLAN Symposium on Principles and Practice of Parallel Programming"

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proceedings of the acm sigplan symposium on principles and practice of parallel programming, PPOPP: Chairs' Welcome

Proceedings of the ACM SIGPLAN Symposium on Principles and P...

引用

proceedings of the acm sigplan symposium on principles and practice of parallel programming, PPOPP 2011年 iii页

作者： Cascaval, Calin Yew, Pen-Chung Qualcomm Research United States Academia Sinica Taiwan University of Minnesota United States

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Two Examples of parallel programming without Concurrency Constructs (PP-CC) 11

Two Examples of Parallel Programming without Concurrency Con...

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16th acm symposium on principles and practice of parallel programming

作者： Ding, Chen Univ Rochester Rochester NY 14627 USA

No abstract available.

ISBN: (纸本)9781450301190

No abstract available.

关键词： Languages Performance

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Active Pebbles: A programming Model For Highly parallel Fine-Grained Data-Driven Computations 11

Active Pebbles: A Programming Model For Highly Parallel Fine...

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16th acm symposium on principles and practice of parallel programming

作者： Willcock, Jeremiah Hoefler, Torsten Edmonds, Nicholas Lumsdaine, Andrew Indiana Univ Bloomington IN 47405 USA Univ Illinois Urbana IL 61801 USA

ISBN: (纸本)9781450301190

A variety of programming models exist to support large-scale, distributed memory, parallel computation. these programming models have historically targeted coarse-grained applications with natural locality such as those found in a variety of scientific simulations of the physical world. Fine-grained, irregular, and unstructured applications such as those found in biology, social network analysis, and graph theory are less well supported. We propose Active Pebbles, a programming model which allows these applications to be expressed naturally;an accompanying execution model ensures performance and scalability.

关键词： Performance Design Irregular applications programming models active messages

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A Wait-Free NCAS Library for parallel Applications with Timing Constraints 11

A Wait-Free NCAS Library for Parallel Applications with Timi...

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16th acm symposium on principles and practice of parallel programming

作者： Stellwag, Philippe Scheler, Fabian Krainz, Jakob Schroeder-Preikschat, Wolfgang Univ Erlangen Nurnberg Erlangen Germany

ISBN: (纸本)9781450301190

We introduce our major ideas of a wait-free, linearizable, and disjoint-access parallel NCAS library, called RTNCAS. It focuses the construction of wait-free data structure operations (DSO) in real-time circumstances. RTNCAS is able to conditionally swap multiple independent words (NCAS) in an atomic manner. It allows us, furthermore, to implement arbitrary DSO by means of their sequential specification.

关键词： Algorithms Design

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programming the Memory Hierarchy Revisited: Supporting Irregular parallelism in Sequoia 11

Programming the Memory Hierarchy Revisited: Supporting Irreg...

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16th acm symposium on principles and practice of parallel programming

作者： Bauer, Michael Clark, John Schkufza, Eric Aiken, Alex Stanford Univ Dept Comp Sci Stanford CA 94305 USA

ISBN: (纸本)9781450301190

We describe two novel constructs for programming parallel machines with multi-level memory hierarchies: call-up, which allows a child task to invoke computation on its parent, and spawn, which spawns a dynamically determined number of parallel children until some termination condition in the parent is met. Together we show that these constructs allow applications with irregular parallelism to be programmed in a straightforward manner, and furthermore these constructs complement and can be combined with constructs for expressing regular parallelism. We have implemented spawn and call-up in Sequoia and we present an experimental evaluation on a number of irregular applications.

关键词： sequoia hierarchical memory parallelism locality

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Evaluating Graph Coloring on GPUs 11

Evaluating Graph Coloring on GPUs

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16th acm symposium on principles and practice of parallel programming

作者： Grosset, A. V. Pascal Zhu, Peihong Liu, Shusen Venkatasubramanian, Suresh Hall, Mary Univ Utah Sch Comp Salt Lake City UT 84112 USA

ISBN: (纸本)9781450301190

this paper evaluates features of graph coloring algorithms implemented on graphics processing units (GPUs), comparing coloring heuristics and thread decompositions. As compared to prior work on graph coloring for other parallel architectures, we find that the large number of cores and relatively high global memory bandwidth of a GPU lead to different strategies for the parallel implementation. Specifically, we find that a simple uniform block partitioning is very effective on GPUs and our parallel coloring heuristics lead to the same or fewer colors than prior approaches for distributed-memory cluster architecture. Our algorithm resolves many coloring conflicts across partitioned blocks on the GPU by iterating through the coloring process, before returning to the CPU to resolve remaining conflicts. With this approach we get as few color (if not fewer) than the best sequential graph coloring algorithm and performance is close to the fastest sequential graph coloring algorithms which have poor color quality.

关键词： Algorithms Performance Graph coloring parallel algorithm GPU CUDA

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Automatic Formal Verification of MPI-Based parallel Programs 11

Automatic Formal Verification of MPI-Based Parallel Programs

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16th acm symposium on principles and practice of parallel programming

作者： Siegel, Stephen F. Zirkel, Timothy K. Univ Delaware Verified Software Lab Dept Comp & Informat Sci Newark DE 19716 USA

ISBN: (纸本)9781450301190

the Toolkit for Accurate Scientific Software (TASS) is a suite of tools for the formal verification of MPI-based parallel programs used in computational science. TASS can verify various safety properties as well as compare two programs for functional equivalence. the TASS front end takes an integer n >= 1 and a C/MPI program, and constructs an abstract model of the program with n processes. Procedures, structs, (multi-dimensional) arrays, heap-allocated data, pointers, and pointer arithmetic are all representable in a TASS model. the model is then explored using symbolic execution and explicit state space enumeration. A number of techniques are used to reduce the time and memory consumed. A variety of realistic MPI programs have been verified with TASS, including Jacobi iteration and manager-worker type programs, and some subtle defects have been discovered. TASS is written in Java and is available from http://***/tass under the Gnu Public License.

关键词： Verification Symbolic execution MPI message-passing debugging verification

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Copperhead: Compiling an Embedded Data parallel Language 11

Copperhead: Compiling an Embedded Data Parallel Language

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16th acm symposium on principles and practice of parallel programming

作者： Catanzaro, Bryan Garland, Michael Keutzer, Kurt Univ Calif Berkeley Berkeley CA 94720 USA

ISBN: (纸本)9781450301190

Modern parallel microprocessors deliver high performance on applications that expose substantial fine-grained data parallelism. Although data parallelism is widely available in many computations, implementing data parallel algorithms in low-level languages is often an unnecessarily difficult task. the characteristics of parallel microprocessors and the limitations of current programming methodologies motivate our design of Copperhead, a high-level data parallel language embedded in Python. the Copperhead programmer describes parallel computations via composition of familiar data parallel primitives supporting both flat and nested data parallel computation on arrays of data. Copperhead programs are expressed in a subset of the widely used Python programming language and interoperate with standard Python modules, including libraries for numeric computation, data visualization, and analysis. In this paper, we discuss the language, compiler, and runtime features that enable Copperhead to efficiently execute data parallel code. We define the restricted subset of Python which Copperhead supports and introduce the program analysis techniques necessary for compiling Copperhead code into efficient low-level implementations. We also outline the runtime support by which Copperhead programs interoperate with standard Python modules. We demonstrate the effectiveness of our techniques with several examples targeting the CUDA platform for parallel programming on GPUs. Copperhead code is concise, on average requiring 3.6 times fewer lines of code than CUDA, and the compiler generates efficient code, yielding 45-100% of the performance of hand-crafted, well optimized CUDA code.

关键词： Python Data parallelism GPU Algorithms Design Performance

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Accelerating CUDA Graph Algorithms at Maximum Warp 11

Accelerating CUDA Graph Algorithms at Maximum Warp

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16th acm symposium on principles and practice of parallel programming

作者： Hong, Sungpack Kim, Sang Kyun Oguntebi, Tayo Olukotun, Kunle Stanford Univ Comp Syst Lab Stanford CA 94305 USA

ISBN: (纸本)9781450301190

Graphs are powerful data representations favored in many computational domains. Modern GPUs have recently shown promising results in accelerating computationally challenging graph problems but their performance suffers heavily when the graph structure is highly irregular, as most real-world graphs tend to be. In this study, we first observe that the poor performance is caused by work imbalance and is an artifact of a discrepancy between the GPU programming model and the underlying GPU architecture. We then propose a novel virtual warp-centric programming method that exposes the traits of underlying GPU architectures to users. Our method significantly improves the performance of applications with heavily imbalanced workloads, and enables trade-offs between workload imbalance and ALU underutilization for fine-tuning the performance. Our evaluation reveals that our method exhibits up to 9x speedup over previous GPU algorithms and 12x over single thread CPU execution on irregular graphs. When properly configured, it also yields up to 30% improvement over previous GPU algorithms on regular graphs. In addition to performance gains on graph algorithms, our programming method achieves 1.3x to 15.1x speedup on a set of GPU benchmark applications. Our study also confirms that the performance gap between GPUs and other multi-threaded CPU graph implementations is primarily due to the large difference in memory bandwidth.

关键词： Algorithms Performance parallel graph algorithms CUDA GPGPU

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Achieving a Single Compute Device Image in OpenCL for Multiple GPUs 11

Achieving a Single Compute Device Image in OpenCL for Multip...

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16th acm symposium on principles and practice of parallel programming

作者： Kim, Jungwon Kim, Honggyu Lee, Joo Hwan Lee, Jaejin Seoul Natl Univ Ctr Manycore Programming Sch Comp Sci & Engn Seoul 151744 South Korea

ISBN: (纸本)9781450301190

In this paper, we propose an OpenCL framework that treats multiple GPUs as a single compute device. Providing the single GPU image makes an OpenCL application written for a single GPU portable to the GPGPU systems with multiple GPUs. It also makes the application exploit the full computing power of the multiple GPUs and the entire amount of GPU memories available in the system. Our OpenCL framework automatically distributes at run time an OpenCL kernel written for a single GPU into multiple CUDA kernels that execute on the multiple GPUs. It applies a run-time memory access range analysis to the kernel by performing a sampling run and identifies an optimal workload distribution for the kernel. To achieve a single compute device image, the runtime maintains a virtual device memory that is allocated in the main memory of the GPGPU system. the OpenCL runtime treats the memory as if it were the memory of a single GPO device and keeps it consistent to the memories of the multiple GPUs. Our OpenCL-C-to-C translator generates the sampling code from the OpenCL kernel code and our OpenCL-C-to-CUDA-C translator generates the CUDA kernel code for the distributed OpenCL kernel. We show the effectiveness of our OpenCL framework by implementing the OpenCL runtime and the two source-to-source translators. We evaluate its performance with a GPGU system that contains eight GPUs using eleven OpenCL benchmark applications.

关键词： Algorithm Design Experimentation Languages Measurement Performance OpenCL Compilers Runtime Access range analysis Workload distribution Virtual device memory

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