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What is ahead for parallel computing

JOURNAL OF parallel AND DISTRIBUTED COMPUTING 2014年第7期74卷 2574-2581页

作者： Hwu, Wen-mei Univ Illinois Urbana IL 61801 USA

With the industry-wide switch to multicore and manycore architectures, parallel computing has become the only venue in sight for continued growth in application performance. In order for the performance of an application to grow with future generations of hardware, a significant portion of its computation must be done with scalable parallel algorithms. It is therefore important to develop and deploy as many scalable parallel algorithms as possible. This paper takes a critical look at the major challenges involved in the development of scalable parallel algorithms and points to needs for compiler tool innovations to help address these challenges. (C) 2014 Elsevier Inc. All rights reserved.

关键词： parallel algorithms parallel data structures Locality Memoryicore bandwidth GPU Multicore Algorithm optimization Algorithm library data layout

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RECURSIVE STAR-TREE parallel data STRUCTURE

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SIAM JOURNAL ON COMPUTING 1993年第2期22卷 221-242页

作者： BERKMAN, O VISHKIN, U UNIV MARYLAND INST ADV COMP STUDIESCOLL PKMD 20742 UNIV MARYLAND DEPT COMP SCICOLL PKMD 20742 TEL AVIV UNIV DEPT COMP SCIIL-69978 TEL AVIVISRAEL

This paper introduces a novel parallel data structure called the recursive star-tree (denoted ''*-tree''). For its definition a generalization of the * functional is used (where for a function f * f (n) = min{i\f(i)(n) less-than-or-equal-to 1} and f(i) is the ith iterate of f). Recursive *-trees are derived by using recursion in the spirit of the inverse Ackermann function. The recursive *-tree data structure leads to a new design paradigm for parallel algorithms. This paradigm allows for extremely fast parallel computations, specifically, O(alpha(n)) time (where alpha(n) is the inverse of the Ackermann function), using an optimal number of processors on the (weakest) concurrent-read, concurrent-write parallel random-access machine (CRCW PRAM). These computations need only constant time, and use an optimal number of processors if the following nonstandard assumption about the model of parallel computation is added to the CRCW PRAM: an extremely small number of processors each can write simultaneously into different bits of the same word. Applications include finding lowest common ancestors in trees by a new algorithm that is considerably simpler than the known algorithms for the problem, restricted domain merging, parentheses matching, and a new parallel reducibility.

关键词： parallel ALGORITHMS parallel data structures LOWEST COMMON ANCESTORS

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A parallel SPH Implementation on Multi-Core CPUs

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COMPUTER GRAPHICS FORUM 2011年第1期30卷 99-112页

作者： Ihmsen, Markus Akinci, Nadir Becker, Markus Teschner, Matthias Univ Freiburg D-7800 Freiburg Germany

This paper presents a parallel framework for simulating fluids with the Smoothed Particle Hydrodynamics (SPH) method. For low computational costs per simulation step, efficient parallel neighbourhood queries are proposed and compared. To further minimize the computing time for entire simulation sequences, strategies for maximizing the time step and the respective consequences for parallel implementations are investigated. The presented experiments illustrate that the parallel framework can efficiently compute large numbers of time steps for large scenarios. In the context of neighbourhood queries, the paper presents optimizations for two efficient instances of uniform grids, that is, spatial hashing and index sort. For implementations on parallel architectures with shared memory, the paper discusses techniques with improved cache-hit rate and reduced memory transfer. The performance of the parallel implementations of both optimized data structures is compared. The proposed solutions focus on systems with multiple CPUs. Benefits and challenges of potential GPU implementations are only briefly discussed.

关键词： fluid animation smoothed particle hydrodynamics neighborhood search parallel data structures

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Fundamental data structures for matrix-free finite elements on hybrid tetrahedral grids

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INTERNATIONAL JOURNAL OF parallel EMERGENT AND DISTRIBUTED SYSTEMS 2023年第1期39卷 51-74页

作者： Kohl, Nils Bauer, Daniel Boehm, Fabian Ruede, Ulrich Friedrich Alexander Univ Erlangen Nurnberg FAU Erlangen Germany Erlangen Natl High Performance Comp Ctr NHR FAU Erlangen Germany Ctr Europeen Rech & Format Avancee Calcul Sci CERF Toulouse France

This paper presents efficient data structures for the implementation of matrix-free finite element methods on block-structured, hybrid tetrahedral grids. It provides a complete categorization of all geometric sub-objects that emerge from the regular refinement of the unstructured, tetrahedral coarse grid and describes efficient iteration patterns and analytical linearization functions for the mapping of coefficients to memory addresses. This foundation enables the implementation of fast, extreme-scalable, matrix-free, iterative solvers, and in particular geometric multigrid methods by design. Their application to the variable-coefficient Stokes system subject to an enriched Galerkin discretization and to the curl-curl problem discretized with Nedelec edge elements showcases the flexibility of the implementation. Finally, the solution of a curl-curl problem with 1.6 center dot 10(11) (more than one hundred billion) unknowns on more than 32,000 processes with a matrix-free full multigrid solver demonstrates its extreme-scalability.

关键词： Matrix-free finite elements hybrid tetrahedral grids block-structured grids parallel data structures

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THE PROGRAMMING LANGUAGE ACTUS

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SOFTWARE-PRACTICE & EXPERIENCE 1983年第4期13卷 305-322页

作者： PERROTT, RH CROOKES, D MILLIGAN, P Department of Computer Science The Queen's University of Belfast Belfast BT7 INN U.K.

AbstractThis paper describes, in an informal manner, the programming language ACTUS which was designed to facilitate programming array processing and vector processing ‘supercomputers’.ACTUS extends the program structuring and data structuring facilities of Pascal to the synchronous parallel environment as represented by array and vector processor architectures. A knowledge of Pascal is assumed and only the parallel features of ACTUS are descri

关键词： Extent of parallelism parallel data structures parallel control structures Array and vector processors Language design

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Real-Time parallel Hashing on the GPU

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ACM TRANSACTIONS ON GRAPHICS 2009年第5期28卷 154:1-154:9页

作者： Alcantara, Dan A. Sharf, Andrei Abbasinejad, Fatemeh Sengupta, Shubhabrata Mitzenmacher, Michael Owens, John D. Amenta, Nina Univ Calif Davis Davis CA 95616 USA Harvard Univ Cambridge MA 02138 USA

We demonstrate an efficient data-parallel algorithm for building large hash tables of millions of elements in real-time. We consider two parallel algorithms for the construction: a classical sparse perfect hashing approach, and cuckoo hashing, which packs elements densely by allowing an element to be stored in one of multiple possible locations. Our construction is a hybrid approach that uses both algorithms. We measure the construction time, access time, and memory usage of our implementations and demonstrate real-time performance on large datasets: for 5 million key-value pairs, we construct a hash table in 35.7 ms using 1.42 times as much memory as the input data itself, and we can access all the elements in that hash table in 15.3 ms. For comparison, sorting the same data requires 36.6 ms, but accessing all the elements via binary search requires 79.5 ms. Furthermore, we show how our hashing methods can be applied to two graphics applications: 3D surface intersection for moving data and geometric hashing for image matching.

关键词： GPU computing hash tables cuckoo hashing parallel hash tables parallel data structures

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Scalable Distributed Fast Multipole Methods

Scalable Distributed Fast Multipole Methods

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14th IEEE International Conference on High Performance Computing and Communications (HPCC) / IEEE 9th International Conference on Embedded Software and Systems (ICESS)

作者： Hu, Qi Gumerov, Nail A. Duraiswami, Ramani Univ Maryland Inst Adv Comp Studies UMIACS College Pk MD 20742 USA

ISBN: (纸本)9780769547497

The Fast Multipole Method (FMM) allows O(N) evaluation to any arbitrary precision of N-body interactions that arises in many scientific contexts. These methods have been parallelized, with a recent set of papers attempting to parallelize them on heterogeneous CPU/GPU architectures [1]. While impressive performance was reported, the algorithms did not demonstrate complete weak or strong scalability. Further, the algorithms were not demonstrated on nonuniform distributions of particles that arise in practice. In this paper, we develop an efficient scalable version of the FMM that can be scaled well on many heterogeneous nodes for nonuniform data. Key contributions of our work are data structures that allow uniform work distribution over multiple computing nodes, and that minimize the communication cost. These new data structures are computed using a parallel algorithm, and only require a small additional computation overhead. Numerical simulations on a heterogeneous cluster empirically demonstrate the performance of our algorithm.

关键词： fast multipole methods GPGPU N-body simulations heterogeneous algorithms scalable algorithms parallel data structures

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Goal-Oriented Self-Adaptive hp Finite Element Simulation of 3D DC Borehole Resistivity Simulations

Goal-Oriented Self-Adaptive <i>hp</i> Finite Element Simulat...

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International Conference on Computational Science (ICCS) on the Ascent of Computational Excellence

作者： Calo, Victor M. Pardo, David Paszynski, Maciej R. King Abdullah Univ Sci & Technol Dept Appl Math Dept Computat Sci Earth Engn Thuwal 239556900 Saudi Arabia Univ Basque Country & Ikerbasque Dept Appl Math Stat & Operat Res Bilbao Spain AGH Univ Sci & Technol Dept Comp Sci PL-30059 Krakow Poland

In this paper we present a goal-oriented self-adaptive hp Finite Element Method (hp-FEM) with shared data structures and a parallel multi-frontal direct solver. The algorithm automatically generates (without any user interaction) a sequence of meshes delivering exponential convergence of a prescribed quantity of interest with respect to the number of degrees of freedom. The sequence of meshes is generated from a given initial mesh, by performing h (breaking elements into smaller elements), p (adjusting polynomial orders of approximation) or hp (both) refinements on the finite elements. The new parallel implementation utilizes a computational mesh shared between multiple processors. All computational algorithms, including automatic hp goal-oriented adaptivity and the solver work fully in parallel. We describe the parallel self-adaptive hp-FEM algorithm with shared computational domain, as well as its efficiency measurements. We apply the methodology described to the three-dimensional simulation of the borehole resistivity measurement of direct current through casing in the presence of invasion.

关键词： hp-FEM goal-oriented adaptivity parallel data structures

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Fast parallel Equivalence Relations in a datalog Compiler 28

Fast Parallel Equivalence Relations in a Datalog Compiler

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28th International Conference on parallel Architectures and Compilation Techniques (PACT)

作者： Nappa, Patrick Zhao, David Subotic, Pavle Scholz, Bernhard Univ Sydney Sydney NSW Australia Amazon London England

ISBN: (纸本)9781728136134

Modern parallelizing datalog compilers are employed in industrial applications such as networking and static program analysis. These applications regularly reason about equivalences, e.g., computing bitcoin user groups, fast points-to analyses, and optimal network routes. State-of-the-art datalog engines represent equivalence relations verbatim by enumerating all possible pairs in an equivalence class. This approach inhibits scalability for large datasets. In this paper, we introduce EQREL, a specialized parallel union-find data structure for scalable equivalence relations, and its integration into a datalog compiler. Our data structure provides a quadratic worst-case speed-up and space improvement. We demonstrate the efficacy of our data structure in SOUFFLE, which is a datalog compiler that synthesizes parallel C++ code. We use real-world benchmarks and show that the new data structure scales on shared-memory multi-core architectures storing up to a half-billion pairs for a static program analysis scenario.

关键词： parallel data structures Equivalence Relation datalog Compiler Semi-naive Evaluation

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Real-Time parallel Hashing on the GPU 09

Real-Time Parallel Hashing on the GPU

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ACM SIGGRAPH Asia Conference 2009

ISBN: (纸本)9781605588582

关键词： GPU computing hash tables cuckoo hashing parallel hash tables parallel data structures

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