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检索条件"任意字段=9th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2009"

共 41 条记录，以下是11-20 订阅

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Accelerating processing of Scale-Free Graphs on Massively-parallel architectures 17th

Accelerating Processing of Scale-Free Graphs on Massively-Pa...

引用

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

作者： Chernoskutov, Mikhail Ural Fed Univ Krasovskii Inst Math & Mech Ekaterinburg Russia

ISBN: (纸本)9783319654829;9783319654812

processing of big scale-free graphs on parallel architectures with high parallelization opportunities connected with a lot of overheads. Due to skewed degree distribution each thread receives different amount of computational workload. In this paper we present a method devoted to address this challenge by modificating CSR data structure and redistributing work across threads. the method was implemented in breadth-first search and single source shortest path algorithms for GPU architecture.

关键词： parallel processing Graph algorithms Workload balancing

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High-Performance Graphics in Racket with DirectX 17th

High-Performance Graphics in Racket with DirectX

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

作者： Bossard, Antoine Kanagawa Univ Grad Sch Sci 2946 Tsuchiya Hiratsuka Kanagawa 2591293 Japan

ISBN: (纸本)9783319654829;9783319654812

Nowadays, modern computer systems rely heavily on parallel processing, and not only because of the multicore CPUs bundled with any machine, even mobile devices, but more and more thanks to the parallel processing capacities of graphics processing units (GPU), general-purpose computing on graphics processing units (GPGPU) being one example. In this paper, relying on the DirectX 12 framework, we propose an innovative approach to enable parallel processing for graphical rendering on both the CPU and GPU for the popular Racket functional programming language (formerly PLT Scheme), and importantly without compromising Racket's usability and programmer-friendliness. Our performance evaluations show significant improvements with respect to execution time (x3 speed-up in some cases), CPU utilisation time (reduced by as much as 80% in some scenarios) and the frame rate when using moving graphics.

关键词： GPU parallel processing Functional Programming Scheme

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Efficiency of GPUs for Relational Database Engine processing 16th

Efficiency of GPUs for Relational Database Engine Processing

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

作者： Cremer, Samuel Bagein, Michel Mahmoudi, Said Manneback, Pierre Univ Mons Dept Comp Sci Rue Houdain 9 B-7000 Mons Belgium

ISBN: (纸本)9783319499567;9783319499550

Relational database management systems (RDBMS) are still widely required by numerous business applications. Boosting performances without compromising functionalities represents a big challenge. To achieve this goal, we propose to boost an existing RDBMS by making it able to use hardware architectures with high memory bandwidth like GPUs. In this paper we present a solution named CuDB. We compare the performances and energy efficiency of our approach with different GPU ranges. We focus on technical specificities of GPUs which are most relevant for designing high energy efficient solutions for database processing.

关键词： In-Memory DB RDBMS GPU SoC

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the Open Community Runtime on the Intel Knights Landing Architecture 17th

The Open Community Runtime on the Intel Knights Landing Arch...

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

作者： Dokulil, Jiri Benkner, Siegfried Yaghob, Jakub Univ Vienna Fac Comp Sci Vienna Austria Charles Univ Prague Dept Software Engn Prague Czech Republic

ISBN: (纸本)9783319654829;9783319654812

the Intel Xeon Phi Knights Landing manycore processor comes with new interesting features: on-chip high-bandwidth memory and several user-selectable NUMA configurations. In this paper, we look into how these affect applications that target the Open Community Runtime (OCR), an asynchronous tasked-based runtime system for future parallel architectures. We have extended our OCR runtime to make it NUMA aware and to allow it to use the high-bandwidth memory. We have conducted a range of experiments, comparing OpenMP, TBB, our OCR implementation, and the reference OCR implementation on different machine configurations using a memory intensive seismic simulation.

关键词： Open Community Runtime Knights Landing Intel Xeon Phi High-bandwidth memory parallel runtime systems NUMA

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Accelerating Exhaustive Pairwise Metagenomic Comparisons 17th

Accelerating Exhaustive Pairwise Metagenomic Comparisons

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

作者： Perez-Wohlfeil, Esteban Torreno, Oscar Trelles, Oswaldo Univ Malaga Dept Comp Architecture Blvd Louis Pasteur 35 Malaga Spain

ISBN: (纸本)9783319654829;9783319654812

In this manuscript, we present an optimized and parallel version of our previous work IMSAME, an exhaustive gapped aligner for the pairwise and accurate comparison of metagenomes. parallelization strategies are applied to take advantage of modern multiprocessor architectures. In addition, sequential optimizations in CPU time and memory consumption are provided. these algorithmic and computational enhancements enable IMSAME to calculate near optimal alignments which are used to directly assess similarity between metagenomes without requiring reference databases. We show that the overall efficiency of the parallel implementation is superior to 80% while retaining scalability as the number of parallel cores used increases. Moreover, we also show that sequential optimizations yield up to 8x speedup for scenarios with larger data.

关键词： High Performance Computing Pairwise comparison parallel computing Next Generation Sequencing Metagenome comparison

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GPU-Accelerated Verification of the Collatz Conjecture

GPU-Accelerated Verification of the Collatz Conjecture

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14th international conference on algorithms and architectures for parallel processing (ica3pp)

作者： Honda, Takumi Ito, Yasuaki Nakano, Koji Hiroshima Univ Dept Informat Engn Higashihiroshima 7398527 Japan

ISBN: (纸本)9783319111971;9783319111964

the main contribution of this paper is to present an implementation that performs the exhaustive search to verify the Collatz conjecture using a GPU. Consider the following operation on an arbitrary positive number: if the number is even, divide it by two, and if the number is odd, triple it and add one. the Collatz conjecture asserts that, starting from any positive number m, repeated iteration of the operations eventually produces the value 1. We have implemented it on NVIDIA GeForce GTX TITAN and evaluated the performance. the experimental results show that, our GPU implementation can verify 5.01x10(11) 64-bit numbers per second, while the CPU implementation on Intel Xeon X7460 can verify 1.80 x 10(9) 64-bit numbers per second. thus, our implementation on the GPU attains a speed-up factor of 278 over the single CPU implementation.

关键词： Collatz conjecture GPGPU parallel processing Exhaustive verification

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A Framework for Designing Autonomous parallel Data Warehouses 19th

A Framework for Designing Autonomous Parallel Data Warehouse...

引用

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

作者： Benkrid, Soumia Bellatreche, Ladjel Ecole Natl Super Informat ESI Algiers Algeria LIAS ISAE ENSMA Poitiers France

ISBN: (纸本)9783030389611;9783030389604

parallel data platforms are recognized as a key solution for processing analytical queries running on extremely large data warehouses (DWs). Deploying a DW on such platforms requires efficient data partitioning and allocation techniques. Most of these techniques assume a priori knowledge of workload. To deal with their evolution, reactive strategies are mainly used. the BI 2.0 requirements have put large batch and ad-hoc user queries at the center. Consequently, reactive-based solutions for deploying a DW in parallel platforms are not sufficient. Autonomous computing has emerged as a paradigm that allows digital objects managing themselves in accordance with high-level guidance by the means of proactive approaches. Being inspired by this paradigm, we propose in this paper, a proactive approach based on a query clustering model to deploying a DW over a parallel platform. the query clustering triggers partitioning and allocation processes by considering only evolved query groups. Intensive experiments were conducted to show the efficiency of our proposal.

关键词： Partitioning Allocation Common sub-expressions Utility maximization Autonomous system Workload clustering

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A Hybrid parallel Search Algorithm for Solving Combinatorial Optimization Problems on Multicore Clusters 17th

A Hybrid Parallel Search Algorithm for Solving Combinatorial...

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

作者： Sanz, Victoria De Giusti, Armando Naiouf, Marcelo Natl Univ La Plata III LIDI Sch Comp Sci La Plata Argentina CIC Buenos Aires DF Argentina Consejo Nacl Invest Cient & Tecn Buenos Aires DF Argentina

ISBN: (纸本)9783319654829;9783319654812

Multicore clusters are widely used to solve combinatorial optimization problems, which require high computing power and a large amount of memory. In this sense, Hash Distributed A* (HDA*) parallelizes A*, a combinatorial optimization algorithm, using the MPI library. HDA* scales well on multicore clusters and on multicore machines. Additionally, there exist several versions of HDA* that were adapted for multicore machines, using the Pthreads library. In this paper, we present Hybrid HDA* (HHDA*), a hybrid parallel search algorithm based on HDA* that combines message-passing (MPI) with shared-memory programming (Pthreads) to better exploit the computing power and memory of multicore clusters. We evaluate the performance and memory consumption of HHDA* on a multicore cluster, using the 15-puzzle as a case study. the results reveal that HHDA* achieves a slightly higher average performance and uses considerably less memory than HDA*. these improvements allowed HHDA* to solve one of the hardest 15-Puzzle instances.

关键词： parallel search algorithms Hybrid programming Multicore cluster Combinatorial optimization problems Hash Distributed A*

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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...

引用

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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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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