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gpgpu programming AND CELLULAR AUTOMATA: IMPLEMENTATION OF THE SCIARA LAVA FLOW SIMULATION CODE

GPGPU PROGRAMMING AND CELLULAR AUTOMATA: IMPLEMENTATION OF T...

23rd European Modeling and Simulation Symposium / International Mediterranean and Latin American Modeling Multiconference

作者： Filippone, Giuseppe Spataro, William Spingola, Giuseppe D'Ambrosio, Donato Rongo, Rocco Perna, Giovanni Di Gregorio, Salvatore Univ Calabria Dept Math I-87030 Commenda Di Rende Italy Univ Calabria HPCC I-87030 Commenda Di Rende Italy Univ Calabria Dept Earth Sci I-87030 Commenda Di Rende Italy

ISBN: (纸本)9788890372445

This paper presents an efficient implementation of a well-known computational model for simulating lava flows on Graphical Processing Units (GPU) using the Compute Unified Device Architecture (CUDA) interface developed by NVIDIA. GPUs are specifically designated for efficiently processing graphic datasets. However, recently, they are also being exploited for achieving exceptional computational results even for applications not directly connected with the Computer Graphics field. We here show an implementation of the SCIARA Cellular Automata model for simulating lava flows on graphic processors using CUDA. Carried out experiments show that significant performance improvements are achieved, over a factor of 100, depending on the problem size, adopted device and type of performed memory optimization, confirming how graphics hardware can represent a valid solution for the implementation for Cellular Automata models.

关键词： Cellular Automata Lava flows simulation gpgpu programming CUDA

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Can gpgpu programming Be Liberated from the Data-Parallel Bottleneck?

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COMPUTER 2012年第8期45卷 42-52页

作者： Gaster, Benedict R. Howes, Lee Advanced Micro Devices

Heterogeneous parallel primitives (HPP) addresses two major shortcomings in current gpgpu programming models: it supports full composability by defining abstractions and increases flexibility in execution by introduci... 详细信息

关键词： Graphics Processing Unit programming Performance Evaluation Parellel Processing Indexes Hardware Multithreading Hardware Massively Threaded Computing Systems Heterogeneous Parallel Primitives Braided Parallelism Persistent Threading gpgpu programming Data Parallel Execution Distributed Arrays

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Autogeneration and Autotuning of 3D Stencil Codes on Homogeneous and Heterogeneous GPU Clusters

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IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS 2013年第3期24卷 417-427页

作者： Zhang, Yongpeng Mueller, Frank N Carolina State Univ Dept Comp Sci Raleigh NC 27695 USA

This paper develops and evaluates search and optimization techniques for autotuning 3D stencil (nearest neighbor) computations on GPUs. Observations indicate that parameter tuning is necessary for heterogeneous GPUs to achieve optimal performance with respect to a search space. Our proposed framework takes a most concise specification of stencil behavior from the user as a single formula, autogenerates tunable code from it, systematically searches for the best configuration and generates the code with optimal parameter configurations for different GPUs. This autotuning approach guarantees adaptive performance for different generations of GPUs while greatly enhancing programmer productivity. Experimental results show that the delivered floating point performance is very close to previous handcrafted work and outperforms other autotuned stencil codes by a large margin. Furthermore, heterogeneous GPU clusters are shown to exhibit the highest performance for dissimilar tuning parameters leveraging proportional partitioning relative to single-GPU performance.

关键词： Accelerators gpgpu programming stencil codes GPU clusters

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Parallel brute-force algorithm for deriving reset sequences from deterministic incomplete finite automata

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TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2019年第5期27卷 3544-+页

作者： Turker, Uraz Cengiz Gebze Tech Univ Fac Engn Dept Comp Engn Kocaeli Turkey

A reset sequence (RS) for a deterministic finite automaton A is an input sequence that brings A to a particular state regardless of the initial state of A. Incomplete finite automata (FA) are strong in modeling reactive systems, but despite their importance, there are no works published for deriving RSs from FA. This paper proposes a massively parallel algorithm to derive short RSs from FA. Experimental results reveal that the proposed parallel algorithm can construct RSs from FA with 16,000,000 states. When multiple GPUs are added to the system the approach can handle larger FA.

关键词： Finite automata incomplete finite automata brute-force approach reset sequences gpgpu programming

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Accelerating subset sum and lattice based public-key cryptosystems with multi-core CPUs and GPUs

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JOURNAL OF PARALLEL AND DISTRIBUTED COMPUTING 2018年 119卷 179-190页

作者： Al Badawi, Ahmad Veeravalli, Bharadwaj Aung, Khin Mi Mi Hamadicharef, Brahim Natl Univ Singapore Dept Elect & Comp Engn 4 Engn Dr 3 Singapore 117576 Singapore ASTAR DSI Connexis North Lobby 1 Fusionopolis Way Singapore 138632 Singapore

Post-quantum cryptosystems based on subset sum and lattice problems have gained much attention from researchers due to their simple construction, their resistance to quantum attacks, the new potential applications they provide, and above all, the mathematical security proofs that rigorously relate them to computational hard problems. However, the computational complexity of these cryptosystems is still high compared to classic number-theoretical ones, which may impede their adoption on a large scale. We studied the performance of three public-key cryptosystems based on subset sum, learning with errors and ring learning with errors problems. We provide a systematic study for choosing their parameters to guarantee sufficient security levels and detail an asymptotic comparison between them in terms of storage and running time complexities. We accelerate the running time of these cryptosystems by exploiting the inherent parallelism in computations through a gpgpu-based parallel implementation. The cryptosystems are implemented using C++ on Intel(R) Xeon(R) multi-core 64-bit processors machine with CUDA-enabled Tesla K80 GPUs. The parallel implementation is based on OpenCL framework and can run on arbitrary hardware platform accelerators with minor changes. Several optimizations and efficient algorithms were used to compute the core operations in each cryptosystem to achieve optimum performance. The ring learning with errors based cryptosystem showed the best performance while the Subset Sum cryptosystem showed the highest speedup gain for the encryption primitive. (C) 2018 Elsevier Inc. All rights reserved.

关键词： Lattice cryptography Post-quantum cryptography Public-key cryptosystem Subset sum Learning with errors gpgpu programming Parallel polynomial multiplier

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CaKernel - A parallel application programming framework for heterogenous computing architectures

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SCIENTIFIC programming 2011年第4期19卷 185-197页

作者： Blazewicz, Marek Brandt, Steven R. Kierzynka, Michal Kurowski, Krzysztof Ludwiczak, Bogdan Tao, Jian Weglarz, Jan Poznan Supercomp & Networking Ctr Poznan Poland Louisiana State Univ Ctr Computat & Technol Baton Rouge LA 70803 USA Louisiana State Univ Dept Comp Sci Baton Rouge LA 70803 USA Poznan Univ Tech Inst Comp Sci PL-60965 Poznan Poland

With the recent advent of new heterogeneous computing architectures there is still a lack of parallel problem solving environments that can help scientists to use easily and efficiently hybrid supercomputers. Many scientific simulations that use structured grids to solve partial differential equations in fact rely on stencil computations. Stencil computations have become crucial in solving many challenging problems in various domains, e. g., engineering or physics. Although many parallel stencil computing approaches have been proposed, in most cases they solve only particular problems. As a result, scientists are struggling when it comes to the subject of implementing a new stencil-based simulation, especially on high performance hybrid supercomputers. In response to the presented need we extend our previous work on a parallel programming framework for CUDA - CaCUDA that now supports OpenCL. We present CaKernel - a tool that simplifies the development of parallel scientific applications on hybrid systems. CaKernel is built on the highly scalable and portable Cactus framework. In the CaKernel framework, Cactus manages the inter-process communication via MPI while CaKernel manages the code running on Graphics Processing Units (GPUs) and interactions between them. As a non-trivial test case we have developed a 3D CFD code to demonstrate the performance and scalability of the automatically generated code.

关键词： gpgpu programming computational framework HPC finite difference stencil computation CUDA OpenCL

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GPU algorithms for density matrix methods on MOPAC: linear scaling electronic structure calculations for large molecular systems

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JOURNAL OF MOLECULAR MODELING 2020年第11期26卷 313-313页

作者： Maia, Julio Daniel Carvalho dos Anjos Formiga Cabral, Lucidio Rocha, Gerd Bruno Univ Fed Paraiba Ctr Informat BR-58055000 Joao Pessoa PB Brazil Univ Illinois Beckman Inst Theoret & Computat Biophys Grp Urbana IL 61801 USA Univ Fed Paraiba Dept Quim CCEN Caixa Postal 5093 BR-58051970 Joao Pessoa PB Brazil

Purification of the density matrix methods should be employed when dealing with complex chemical systems containing many atoms. The running times for these methods scale linearly with the number of atoms if we consider the sparsity from the density matrix. Since the efficiency expected from those methods is closely tied to the underlying parallel implementations of the linear algebra operations (e.g., P-2 = P x P), we proposed a central processing unit (CPU) and graphics processing unit (GPU) parallel matrix-matrix multiplication in SVBR (symmetrical variable block row) format for energy calculations through the SP2 algorithm. This algorithm was inserted in MOPAC's MOZYME method, using the original LMO Fock matrix assembly, and the atomic integral calculation implemented on it. Correctness and performance tests show that the implemented SP2 is accurate and fast, as the GPU is able to achieve speedups up to 40 times for a water cluster system with 42,312 orbitals running in one NVIDIA K40 GPU card compared to the single-threaded version. The GPU-accelerated SP2 algorithm using the MOZYME LMO framework enables the calculations of semiempirical wavefunction with stricter SCF criteria for localized charged molecular systems, as well as the single-point energies of molecules with more than 100.000 LMO orbitals in less than 1 h.

关键词： Linear scaling algorithms Density matrix methods gpgpu programming Sparse matrices Semiempirical methods

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Human Gesture Detection based on 3D Blobs and Skeleton Model

Human Gesture Detection based on 3D Blobs and Skeleton Model

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4th International Conference on Information Science and Applications (ICISA)

作者： Kang, Sungil Oh, Juhee Hong, Hyunki Chung Ang Univ Dept Imaging Sci & Arts GSAIM Seoul 156756 South Korea

ISBN: (纸本)9781479906048;9781479906024

This paper presents a gesture tracking method using 3D blobs and a skeleton model for interactive application. A disparity map is obtained from stereo matching based on gpgpu and we generate 3D foreground blobs using depth information. The distribution of 3D blobs is applied to determine the human position combined with the face and torso detection. The skeleton model for an upper body is fitted successively to a median axis in the area with more 3D blobs from the shoulder parts to the hands. We examine the position and color of the 3D blobs for robust tracking of the gesture parts. The reconstructed trajectory is classified into one of the gesture set.

关键词： Human gesture human computer interface real-time stereo system skeleton model gpgpu programming

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OpenMP Target Offload Utilizing GPU Shared Memory 19th

OpenMP Target Offload Utilizing GPU Shared Memory

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19th International Workshop on OpenMP (IWOMP)

作者： Gammelmark, Mathias Rydahl, Anton Karlsson, Sven Tech Univ Denmark Anker Engelunds Vej 1 DK-2800 Lyngby Denmark

ISBN: (纸本)9783031407437;9783031407444

Memory resources are an important aspect to consider when designing high performing programs. This is especially true for programs running on graphical processing units, GPUs, yet this is not something trivially done using current OpenMP target offloading. In this paper, we examine methods for implementing parallel programs running on GPUs, which rely on locally shared memory resources and intricate synchronization. Employing the methods, we show you can achieve between 1.5 to 9 relative speedup over a range of compilers. We evaluate portability by running experiments on two systems, utilizing different GPU technologies and vendors. We further investigate scheduling, synchronization and execution time of our experiments, to better understand the overhead associated with using OpenMP, compared to architecture specific languages. Lastly, we argue that improved GPU scheduling could yield a potential speedup of 3.

关键词： gpgpu programming OpenMP Target Offloading Shared Memory Fine-Grained Parallelism

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Data-Parallel Flattening by Expansion 6

Data-Parallel Flattening by Expansion

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6th ACM SIGPLAN International Workshop on Libraries, Languages and Compilers for Array programming (ARRAY)

作者： Elsman, Martin Henriksen, Troels Serup, Niels Gustav Westphal Univ Copenhagen Copenhagen Denmark

ISBN: (纸本)9781450367172

We present a higher-order programmer-level technique for compiling particular kinds of irregular data-parallel problems to parallel hardware. The technique, which we have named "flattening-by-expansion" builds on a number of segmented data-parallel operations but is itself implemented as a higher-order generic function, which makes it useful for many irregular problems. Concretely, the implementation is given in Futhark and we demonstrate the usefulness of the functionality for a number of irregular problems and show that, in practice, the irregular problems are compiled to efficient parallel code that can be executed on GPUs. The technique is useful in any data-parallel language that provides a key set of primitives.

关键词： gpgpu programming irregular nested parallelism flattening functional programming

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