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检索条件"任意字段=6th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP"

共 292 条记录，以下是121-130 订阅

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Cellular ANTomata as Engines for Highly parallel Pattern processing 16th

Cellular ANTomata as Engines for Highly Parallel Pattern Pro...

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16th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Rosenberg, Arnold L. Northeastern Univ Comp & Informat Sci Boston MA 02115 USA

ISBN: (纸本)9783319499567;9783319499550

One important approach to high-performance computing has a (relatively) simple physical computer architecture emulate virtual algorithmic architectures (VAAs) that are highly optimized for important application domains. We expose the Cellular ANTomaton (CAnt) computing model-cellular automata enhanced with mobile FSMs (Ants)-as a highly efficient VAA for a variety of pattern-processing problems that are inspired by biocomputing applications. We illustrate the CAnt model via a scalable design for an n x n CAnt that solves the following bio-inspired problem in linear time. the Pattern-Assembly Problem. Inputs: a length-n master pattern II and r test patterns pi(0), . . . , pi(r-1), of respective lengths m(0) >= . . . >= m(r-1). the problem: Find every sequence of pi(k)'s, possibly with repetitions, that "assemble" (i.e., concatenate) to produce II;i.e., pi(j0) . . . pi(js-1) = II. Timing: m(1) + . . . + m(r) + O(n) steps, with a quite-small big-O constant.

关键词： HPC via emulation Cellular ANTomata Pattern assembly

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Light Loss-Less Data Compression, with GPU Implementation 16th

Light Loss-Less Data Compression, with GPU Implementation

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16th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Funasaka, Shunji Nakano, Koji Ito, Yasuaki Hiroshima Univ Dept Informat Engn Kagamiyama 1-4-1 Higashihiroshima 7398527 Japan

ISBN: (纸本)9783319495835;9783319495828

there is no doubt that data compression is very important in computer engineering. However, most lossless data compression and decompression algorithms are very hard to parallelize, because they use dictionaries updated sequentially. the main contribution of this paper is to present a new lossless data compression method that we call Light Loss-Less (LLL) compression. It is designed so that decompression can be highly parallelized and run very efficiently on the GPU. this makes sense for many applications in which compressed data is read and decompressed many times and decompression performed more frequently than compression. We show optimal sequential and parallel algorithms for LLL decompression and implement them to run on Core i7-4790 CPU and GeForce GTX 1080 GPU, respectively. To show the potentiality of LLL compression method, we have evaluated the running time using five images and compared with well-known compression methods LZW and LZSS. Our GPU implementation of LLL decompression runs 91.1-176 times faster than the CPU implementation. Also, the running time on the GPU of our experiments show that LLL decompression is 2.49-9.13 times faster than LZW decompression and 4.30-14.1 times faster that LZSS decompression, although their compression ratios are comparable.

关键词： Data compression parallel algorithms GPGPU

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Automatic Optimization of Python Skeletal parallel Programs 19th

Automatic Optimization of Python Skeletal Parallel Programs

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19th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Loulergue, Frederic Philippe, Jolan No Arizona Univ Sch Informat Comp & Cyber Syst Flagstaff AZ 86011 USA IMT Atlantique LS2N INRIA UBL Nantes France

ISBN: (纸本)9783030389918;9783030389901

Skeletal parallelism is a model of parallelism where parallel constructs are provided to the programmer as usual patterns of parallel algorithms. High-level skeleton libraries often offer a global view of programs instead of the common Single Program Multiple Data view in parallel programming. A program is written as a sequential program but operates on parallel data structures. Most of the time, skeletons on a parallel data structure have counterparts on a sequential data structure. For example, the map function that applies a given function to all the elements of a sequential collection (e.g., a list) has a map skeleton counterpart that applies a sequential function to all the elements of a distributed collection. Two of the challenges a programmer faces when using a skeleton library that provides a wide variety of skeletons are: which are the skeletons to use, and how to compose them? these design decisions may have a large impact on the performance of the parallel programs. However, skeletons, especially when they do not mutate the data structure they operate on, but are rather implemented as pure functions, possess algebraic properties that allow to transform compositions of skeletons into more efficient compositions of skeletons. In this paper, we present such an automatic transformation framework for the Python skeleton library PySke and evaluate it on several example applications.

关键词： Algorithmic skeletons Program transformation Python

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A parallel Branch and Bound Algorithm for the Probabilistic TSP 18th

A Parallel Branch and Bound Algorithm for the Probabilistic ...

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18th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Amar, Mohamed Abdellahi Khaznaji, Walid Bellalouna, Monia Univ Manouba Natl Sch Comp Sci CRISTAL Lab POLE GRIFT Tunis Tunisia Tunisia SESAME Univ Ariana Tunisia

ISBN: (纸本)9783030050511;9783030050504

the paper presents parallelization of exact algorithm of resolution for the Probabilistic Traveling Salesman Problem (PTSP). this algorithm allows us, first, to verify the stability of well-solvable special cases and also to optimally solve useful instances of PTSP. It again allows to perform our version of Karp partitioning algorithm, where real problems are very large-sized. the implementation of the algorithm of Karp consists in subdividing the square plan, into sub-plans. So we transform the resolution of a large size problem to the resolution of many small size sub-problems which can be exactly solved. this application can be gridified and these different sub-problems would be processed in parallel by different nodes since they are totally independent. In each sub-plan the Branch and Bound algorithm is used. In this paper we propose two parallelizations of the Branch and Bound algorithm for the resolution of the PTSP. On the one hand, the parallelization of the branches used in the exploration of the tree, on the other hand the parallelization of the algorithm associated with the notion of partitioning introduced by Karp. We perform an experimental study conducted in a multi-core environment to evaluate the performance of the proposed approach.

关键词： PTSP parallel algorithm Open MP Simulations

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High-Performance Simulations on GPUs Using Adaptive Time Steps 20th

High-Performance Simulations on GPUs Using Adaptive Time Ste...

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20th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Koester, Marcel Gross, Julian Krueger, Antonio Saarland Informat Campus Campus D3-2 D-66123 Saarbrucken Germany

ISBN: (纸本)9783030602451;9783030602444

Graphics processing Units (GPUs) are widely spread nowadays due to their parallel processing capabilities. Leveraging these hardware features is particularly important for computationally expensive tasks and workloads. Prominent use cases are optimization problems and simulations that can be parallelized and tuned for these architectures. In the general domain of simulations (numerical and discrete), the overall logic is split into several components that are executed one after another. these components need step-size information which determines the number of steps (e.g. the elapsed time) they have to perform. Small step sizes are often required to ensure a valid simulation result with respect to precision and constraint correctness. Unfortunately, they are often the main bottleneck of the simulation. In this paper, we introduce a new and generic way of realizing high-performance simulations with multiple components using adaptive time steps on GPUs. Our method relies on a code-analysis phase that resolves data dependencies between different components. this knowledge is used to generate specially-tuned execution kernels that encapsulate the underlying component logic. An evaluation on our simulation benchmarks shows that we are able to considerably improve runtime performance compared to prior work.

关键词： Simulations parallel computing Adaptive time steps Graphics processing units GPUs

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Power-Performance Evaluation of parallel Multi-objective EEG Feature Selection on CPU-GPU Platforms 17th

Power-Performance Evaluation of Parallel Multi-objective EEG...

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17th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Jose Escobar, Juan Ortega, Julio Francisco Diaz, Antonio Gonzalez, Jesus Damas, Miguel Univ Granada Dept Comp Architecture & Technol CITIC Granada Spain

ISBN: (纸本)9783319654829;9783319654812

Heterogeneous CPU-GPU platforms include resources to benefit from different kinds of parallelism present in many data mining applications based on evolutionary algorithms that evolve solutions with time-demanding fitness evaluation. this paper describes an evolutionary parallel multi-objective feature selection procedure with subpopulations using two scheduling alternatives for evaluation of individuals according to the number of subpopulations. Evolving subpopulations usually provides good diversity properties and avoids premature convergence in evolutionary algorithms. the proposed procedure has been implemented in OpenMP to distribute dynamically either subpopulations or individuals among devices and OpenCL to evaluate the individuals taking into account the devices characteristics, providing two parallelism levels in CPU and up to three levels in GPUs. Different configurations of the proposed procedure have been evaluated and compared with a master-worker approach considering not only the runtime and achieved speedups but also the energy consumption between both scheduling models.

关键词： Heterogeneous subpopulations scheduling Energy-aware computing EEG classification Multi-objective feature selection GPU

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Massively parallel Sequence Alignment with BLAST through Work Distribution Implemented Using PCJ Library 17th

Massively Parallel Sequence Alignment with BLAST Through Wor...

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17th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Nowicki, Marek Bzhalava, Davit Bala, Piotr Nicolaus Copernicus Univ Fac Math & Comp Sci Chopina 12-18 PL-87100 Torun Poland Karolinska Inst Dept Lab Med F46 S-14186 Stockholm Sweden Univ Warsaw Interdisciplinary Ctr Math & Computat Modelling Pawinskiego 5a PL-02106 Warsaw Poland

ISBN: (纸本)9783319654829;9783319654812

this article presents massively parallel execution of the BLAST algorithm on supercomputers and HPC clusters using thousands of processors. Our work is based on the optimal splitting up the set of queries running with the non-modified NCBI-BLAST package for sequence alignment. the work distribution and search management have been implemented in Java using a PCJ (parallel Computing in Java) library. the PCJ-BLAST package is responsible for reading sequence for comparison, splitting it up and start multiple NCBI-BLAST executables. We also investigated a problem of parallel I/O and thanks to PCJ library we deliver high throughput execution of BLAST. the presented results show that using Java and PCJ library we achieved very good performance and efficiency. In result, we have significantly reduced time required for sequence analysis. We have also proved that PCJ library can be used as an efficient tool for fast development of the scalable applications.

关键词： Sequence alignment NGS Next Generation Sequencing parallel programming Java BLAST NCBI-BLAST PCJ

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Configuring Concurrent Computation of Phylogenetic Partial Likelihoods: Accelerating Analyses Using the BEAGLE Library 17th

Configuring Concurrent Computation of Phylogenetic Partial L...

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17th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Ayres, Daniel L. Cummings, Michael P. Univ Maryland Ctr Bioinformat & Computat Biol College Pk MD 20742 USA

ISBN: (纸本)9783319654829;9783319654812

We describe our approach in augmenting the BEAGLE library for high-performance statistical phylogenetic inference to support concurrent computation of independent partial likelihoods arrays. Our solution involves identifying independent likelihood estimates in analyses of partitioned datasets and in proposed tree topologies, and configuring concurrent computation of these likelihoods via CUDA and opencL frameworks. We evaluate the effect of each increase in concurrency on throughput performance for our partial likelihoods kernel for a four-state nucleotide substitution model on a variety of parallel computing hardware, such as NVIDIA and AMD GPU5, and Intel multicore cPus, observing up to 16-fold speedups over our previous implementation. Finally, we evaluate the effect of these gains on an domain application program, MrBayes. For a partitioned nucleotide-model analysis we observe an average speedup for the overall run time of 2.1-fold over our previous parallel implementation, and 10-fold over the native MrBayes with SSE.

关键词： Bayes methods Biology computing Evolution (biology) Phylogeny Maximum likelihood estimation Multicore processing parallel programming High performance computing

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FANG: Fast and Efficient Successor-State Generation for Heuristic Optimization on GPUs 19th

FANG: Fast and Efficient Successor-State Generation for Heur...

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19th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Koester, Marcel Gross, Julian Krueger, Antonio Saarland Informat Campus Campus D3-2 D-66123 Saarbrucken Germany

ISBN: (纸本)9783030389918;9783030389901

Many optimization problems (especially nonsmooth ones) are typically solved by genetic, evolutionary, or metaheuristic-based algorithms. However, these genetic approaches and other related papers typically assume the existence of a neighborhood or successor-state function N(x), where x is a candidate state. the implementation of such a function can become arbitrarily complex in the field of combinatorial optimization. Many N(x) functions for a huge variety of different domainspecific problems have been developed in the past to solve this general problem. However, it has always been a great challenge to port or realize these functions on a massively-parallel architecture like a Graphics processing Unit (GPU). We present a GPU-based method called FANG that implements a generic and reusable N(x) for arbitrary domains in the field of combinatorial optimization. It can be customized to satisfy domainspecific requirements and leverages the underlying hardware in a fast and efficient way by construction. Moreover, our method has a high scalability with respect to the number of input states and the complexity of a single state. Measurements show significant performance improvements compared to traditional exploration approaches leveraging the CPU on our evaluation scenarios.

关键词： Heuristic search Combinatorial optimization Successor-state generation Neighborhood exploration Massively-parallel processing Graphics processing units GPUs

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Comparative Analysis of OpenACC Compilers 16th

Comparative Analysis of OpenACC Compilers

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16th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Barba, Daniel Gonzalez-Escribano, Arturo Llanos, Diego R. Univ Valladolid Dept Informat Valladolid Spain

ISBN: (纸本)9783319499567;9783319499550

OpenACC has been on development for a few years now. the OpenACC 2.5 specification was recently made public and there are some initiatives for developing full implementations of the standard to make use of accelerator capabilities. there is much to be done yet, but currently, OpenACC for GPUs is reaching a good maturity level in various implementations of the standard, using CUDA and OpenCL as backends. Nvidia is investing in this project and they have released an OpenACC Toolkit, including the PGI Compiler. there are, however, more developments out there. In this work, we analyze different available OpenACC compilers that have been developed by companies or universities during the last years. We check their performance and maturity, keeping in mind that OpenACC is designed to be used without extensive knowledge about parallel programming. Our results show that the compilers are on their way to a reasonable maturity, presenting different strengths and weaknesses.

关键词： parallel programming

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