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检索条件"任意字段=Proceedings of the 24th Symposium on Principles and Practice of Parallel Programming"

共 364 条记录，以下是121-130 订阅

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Improving parallel shear-warp volume rendering on shared address space multiprocessors 97

Improving parallel shear-warp volume rendering on shared add...

引用

proceedings of the 1997 6th ACM SIGPLAN symposium on principles and practice of parallel programming

作者： Jiang, Dongming Singh, Jaswinder Pal Princeton Univ Princeton NJ United States

ISBN: (纸本)9780897919067

this paper presents a new parallel volume rendering algorithm and implementation, based on shear warp factorization, for shared address space multiprocessors. Starting from an existing parallel shear-warp renderer, we use increasingly detailed performance measurements on real machines and simulators to understand performance bottlenecks. this leads us to a new parallel implementation that substantially outperforms and out-scales the old one on a range of shared address space platforms, from bus-based centralized memory machine to hardware-coherent distributed memory machines to networks of computers connected by page-based shared virtual memory. the results demonstrate that real time volume rendering is promising on general purpose multiprocessors, and illustrate the utility of tool hierarchies in conjunction with algorithmic and application knowledge to understand memory system interactions and improve parallel algorithms.

关键词： parallel algorithms

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Simplifying low-level GPU programming with GAS 21

Simplifying low-level GPU programming with GAS

引用

26th ACM SIGPLAN symposium on principles and practice of parallel programming, PPoPP 2021

作者： Yan, Da Wang, Wei Chu, Xiaowen Hkust Hong Kong Hong Kong Baptist University Hong Kong

ISBN: (纸本)9781450382946

Many low-level optimizations for NVIDIA GPU can only be implemented in native hardware assembly (SASS). However, programming in SASS is unproductive and not portable. To simplify low-level GPU programming, we present GAS (Gpu ASsembly), a PTX-like language that provides a stable instruction set across hardware architectures while giving programmers a low-level control of code execution. We demonstrate that GAS can be used with ease for low-level benchmarking and performance tuning in the context of Tensor Core HGEMM. © 2021 Owner/Author.

关键词： Graphics processing unit

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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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Space and time efficient execution of parallel irregular computations

Space and time efficient execution of parallel irregular com...

引用

6th ACM SIGPLAN symposium on principles and practice of parallel programming

作者： Fu, C Yang, T Univ of California Santa Barbara United States

Solving problems of large sizes is an important goal for parallel machines with multiple CPU and memory resources. In this paper, issues of efficient execution of overhead-sensitive parallel irregular computation under memory constraints are addressed. the irregular parallelism is modeled by task dependence graphs with mixed granularities. the trade-off in achieving both time and space efficiency is investigated. the main difficulty of designing efficient run-time system support is caused by the use of fast communication primitives available on modern parallel architectures. A run-time active memory management scheme and new scheduling techniques are proposed to improve memory utilization while retaining good time efficiency, and a theoretical analysis on correctness and performance is provided. this work is implemented in the context of RAPID system [5] which provides run-time support for parallelizing irregular code on distributed memory machines and the effectiveness of the proposed techniques is verified on sparse Cholesky and LU factorization with partial pivoting. the experimental results on Cray-T3D show that solvable problem sizes can be increased substantially under limited memory capacities and the loss of execution efficiency caused by the extra memory managing overhead is reasonable.

关键词： parallel processing systems

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parallelizing algorithms for symbolic computation using ||MAPLE|| 93

Parallelizing algorithms for symbolic computation using ||MA...

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4th ACM SIGPLAN symposium on principles and practice of parallel programming, PPOPP 1993

作者： Siegl, Kurt Johannes Kepler University LinzA-4040 Austria

ISBN: (纸本)0897915895

||MAPLE|| (speak: parallel Maple) is a portable system for parallel symbolic computation. the system is built as an interface between the parallel declarative programming language Strand and the sequential computer algebra system Maple, thus providing the elegance of Strand and the power of the existing sequential algorithms in Maple. the implementation of difFerent parallel programming paradigms shows that it is fairly easy to parallelize even complex algebraic algorithms using this system. Sample applications (among them algorithms solving multivariate nonlinear equation systems) are implemented on various parallel architectures. For example a straightforward parallelization of the complex and important problem of real root isolation has been parallelized using a generic Strand program of fewer than 20 Unes of code and a slight modification of 5 lines in the original sequential Maple source. Even with such a simple modification we gained a speed-up of 5 times, that is better than those reported by others in the literature.

关键词： Logic programming

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Data and computation transformations for multiprocessors

Data and computation transformations for multiprocessors

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proceedings of the 5th ACM SIGPLAN symposium on principles and practice of parallel programming

作者： Anderson, Jennifer M. Amarasinghe, Saman P. Lam, Monica S. Stanford Univ Stanford United States

Effective memory hierarchy utilization is critical to the performance of modem multiprocessor architectures. We have developed the first compiler system that fully automatically parallelizes sequential programs and changes the original array layouts to improve memory system performance. Our optimization algorithm consists of two steps. the first step chooses the parallelization and computation assignment such that synchronization and data sharing are minimized. the second step then restructures the layout of the data in the shared address space with an algorithm that is based on a new data transformation framework. We ran our compiler on a set of application programs and measured their performance on the Stanford DASH multiprocessor. Our results show that the compiler can effectively optimize parallelism in conjunction with memory subsystem performance.

关键词： parallel processing systems

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parallel Block-Delayed Sequences 22

Parallel Block-Delayed Sequences

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27th ACM SIGPLAN symposium on principles and practice of parallel programming (PPoPP)

作者： Westrick, Sam Rainey, Mike Anderson, Daniel Blelloch, Guy E. Carnegie Mellon Univ Pittsburgh PA 15213 USA

ISBN: (纸本)9781450392044

programming languages using functions on collections of values, such as map, reduce, scan and filter, have been used for over fifty years. Such collections have proven to be particularly useful in the context of parallelism because such functions are naturally parallel. However, if implemented naively they lead to the generation of temporary intermediate collections that can significantly increase memory usage and runtime. To avoid this pitfall, many approaches use "fusion" to combine operations and avoid temporary results. However, most of these approaches involve significant changes to a compiler and are limited to a small set of functions, such as maps and reduces. In this paper we present a library-based approach that fuses widely used operations such as scans, filters, and flattens. In conjunction with existing techniques, this covers most of the common operations on collections. Our approach is based on a novel technique which parallelizes over blocks, with streams within each block. We demonstrate the approach by implementing libraries targeting multicore parallelism in two languages: parallel ML and C++, which have very different semantics and compilers. To help users understand when to use the approach, we define a cost semantics that indicates when fusion occurs and how it reduces memory allocations. We present experimental results for a dozen benchmarks that demonstrate significant reductions in both time and space. In most cases the approach generates code that is near optimal for the machines it is running on.

关键词： parallel programming fusion collections functional programming

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A parallel Sparse Tensor Benchmark Suite on CPUs and GPUs 20

A Parallel Sparse Tensor Benchmark Suite on CPUs and GPUs

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25th ACM SIGPLAN symposium on principles and practice of parallel programming (PPoPP)

作者： Li, Jiajia Lakshminarasimhan, Mahesh Wu, Xiaolong Li, Ang Olschanowsky, Catherine Barker, Kevin Pacific Northwest Natl Lab Richland WA 99352 USA Univ Utah Salt Lake City UT USA Purdue Univ W Lafayette IN 47907 USA Boise State Univ Boise ID 83725 USA

ISBN: (纸本)9781450368186

Tensor computations present significant performance challenges that impact a wide spectrum of applications. Efforts on improving the performance of tensor computations include exploring data layout, execution scheduling, and parallelism in common tensor kernels. this work presents a benchmark suite for arbitrary-order sparse tensor kernels using state-of-the-art tensor formats: coordinate (COO) and hierarchical coordinate (HiCOO). It demonstrates a set of reference tensor kernel implementations and some observations on Intel CPUs and NVIDIA GPUs. the full paper can be referred to at http://***/abs/2001.00660.

关键词： sparse tensors benchmark GPU roofline model

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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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Using GPU's to Accelerate Stencil-based Computation Kernels for the Development of Large Scale Scientific Applications on Heterogeneous Systems 12

Using GPU's to Accelerate Stencil-based Computation Kernels ...

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17th ACM SIGPLAN symposium on principles and practice of parallel programming

作者： Tao, Jian Blazewicz, Marek Brandt, Steven R. Louisiana State Univ Ctr Computat & Technol Baton Rouge LA 70803 USA Poznan Supercomp & Networking Ctr Applicat Dept Poznan Poland

ISBN: (纸本)9781450311601

We present CaCUDA - a GPGPU kernel abstraction and a parallel programming framework for developing highly efficient large scale scientific applications using stencil computations on hybrid CPU/GPU architectures. CaCUDA is built upon the Cactus computational toolkit, an open source problem solving environment designed for scientists and engineers. Due to the flexibility and extensibility of the Cactus toolkit, the addition of a GPGPU programming framework required no changes to the Cactus infrastructure, guaranteeing that existing features and modules will continue to work without modification. CaCUDA was tested and benchmarked using a 3D CFD code based on a finite difference discretization of Navier-Stokes equations.

关键词： Algorithms Design Languages GPGPU programming Computational Framework HPC Stencil Computation

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