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parallel Multi-objective Optimization for High-Order Epistasis Detection 17th

Parallel Multi-objective Optimization for High-Order Epistas...

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

作者： Gallego-Sanchez, Daniel Granado-Criado, Jose M. Santander-Jimenez, Sergio Rubio-Largo, Alvaro Vega-Rodriguez, Miguel A. Univ Extremadura Caceres Spain Univ Lisbon Lisbon Portugal Univ NOVA Lisboa Lisbon Portugal

ISBN: (纸本)9783319654829;9783319654812

Many studies have shown that there is a direct relationship between Single Nucleotide Polymorphisms (SNPs) and the appearance of complex diseases, such as Alzheimer's or Parkinson's. However, recent advances in the Study of the Complete Genome Association indicate that the relationship between SNPs and these diseases goes beyond a simple one-to-one relationship, that is, the appearance of multiple SNPs (epistasis) influences the appearance of these diseases. In this sense, this work proposes the application of the NSGA-II multi-objective algorithm for the detection of epistasis of multiple loci in a database with 31,341 SNPs. Moreover, a parallel study has been performed to reduce the execution time of this problem. Our implementation not only achieves a reasonable good parallel performance and scalability, but also its biological significance overcomes other approaches published in the literature.

关键词： parallelism NSGA-II Epistasis SNP

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A parallel Fast Fourier Transform Algorithm for Large-Scale Signal Data Using Apache Spark in Cloud 18th

A Parallel Fast Fourier Transform Algorithm for Large-Scale ...

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

作者： Yang, Cheng Bao, Weidong Zhu, Xiaomin Wang, Ji Xiao, Wenhua Natl Univ Def Technol Changsha Peoples R China State Key Lab High Performance Comp Changsha Peoples R China Acad Mil Med Sci Beijing Peoples R China

ISBN: (纸本)9783030050573;9783030050566

In the field of signal process, Fast Fourier Transform (FFT) is a widely used algorithm to transform signal data from time to frequency. Unfortunately, with the exponential growth of data, traditional methods cannot meet the demand of large-scale computation on these big data because of three main challenges of large-scale FFT, i.e., big data size, real-time data processing and high utilization of compute resources. To satisfy these requirements, an optimized FFT algorithm in Cloud is deadly needed. In this paper, we introduce a new method to conduct FFT in Cloud with the following contributions: first, we design a parallel FFT algorithm for large-scaled signal data in Cloud;second, we propose a MapReduce-based mechanism to distribute data to compute nodes using big data processing framework;third, an optimal method of distributing compute resources is implemented to accelerate the algorithm by avoiding redundant data exchange between compute nodes. the algorithm is designed in MapReduce computation framework which contains three steps: data preprocessing, local data transform and parallel data transform to integrate processing results. the parallel FFT is implemented in a 16-node Cloud to process real signal data the experimental results reveal an obvious improvement in the algorithm speed. Our parallel FFT is approximately five times faster than FFT in Matlab in when the data size reaches 10 GB.

关键词： Fast fourier transform Cloud computing Apache spark parallel algorithm

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Pipelining Computation and Optimization Strategies for Scaling GROMACS on the Sunway Many-Core Processor 17th

Pipelining Computation and Optimization Strategies for Scali...

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

作者： Yu, Yang An, Hong Chen, Junshi Liang, Weihao Xu, Qingqing Chen, Yong Univ Sci & Technol China Hefei Anhui Peoples R China Texas Tech Univ Dept Comp Sci Lubbock TX 79409 USA

ISBN: (纸本)9783319654829;9783319654812

the increasing gap between plentiful computing elements and limited memory bandwidth makes it increasingly difficult and sometimes even infeasible for HPC community to port more applications onto many-core processor architectures. the Sunway many-core processor SW26010 used to build the Sunway TaihuLight System contains a total of 260 heterogeneous cores. All these cores can be divided into 4 core groups (CGs). Each CG includes a Management processing Element (MPE) core and 64 Computing processing Elements (CPEs) cores. In this paper, we refactor an important molecular dynamics (MD) application GROMACS on the Sunway Taihulight system. By rewriting the compute-intensive kernel of GROMACS, we exploit a suitable parallelism for CPE cluster and implement pipelining computation between MPE and CPE cluster. Optimization strategies including the efficient use of scratchpad, the software-emulated cache and a hybrid parallel algorithm are adopted to solve the challenging memory bandwidth limitation. When comparing the refactored version using MPE and 64 CPEs with the original ported version using only MPE, we achieve a 16x speedup for the compute-intensive kernel. For simulating a molecule with 3 million atoms, we currently have managed to scale to 798,720 cores. Moreover, we analyze the adaptability of our mapping and optimization strategies for solving the memory bandwidth limitation when refactoring a real-world application on the Sunway heterogeneous many-core processor system.

关键词： Sunway TaihuLight system GROMACS parallel model Performance optimization Bandwidth competition Adaptability

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More Effective Distributed Deep Learning Using Staleness Based Parameter Updating 18th

More Effective Distributed Deep Learning Using Staleness Bas...

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

作者： Ye, Yan Chen, Mengqiang Yan, Zijie Wu, Weigang Xiao, Nong Sun Yet Sen Univ Sch Data & Comp Sci Guangzhou Peoples R China Guangdong Prov Key Lab Big Data Anal & Proc Guangzhou Peoples R China MoE Key Lab Machine Intelligence & Adv Comp Guangzhou Peoples R China

ISBN: (纸本)9783030050542;9783030050535

Deep learning technology has been widely applied for various purposes, especially big data analysis. However, computation required for deep learning is getting more complex and larger. In order to accelerate the training of large-scale deep networks, various distributed parallel training protocols have been proposed. In this paper, we design a novel asynchronous training protocol, Weighted Asynchronous parallel (WASP), to update neural network parameters in a more effective way. the core of WASP is "gradient staleness", a parameter version number based metric to weight gradients and reduce the influence of the stale parameters. Moreover, by periodic forced synchronization of parameters, WASP combines the advantages of synchronous and asynchronous training models and can speed up training with a rapid convergence rate. We conduct experiments using two classical convolutional neural networks, LeNet-5 and ResNet-101, at the Tianhe-2 supercomputing system, and the results show that, WASP can achieve much higher acceleration than existing asynchronous parallel training protocols.

关键词： Distributed deep learning parallel computing Parameter server Asynchronous parallel Supercomputing system

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PLZMA: A parallel Data Compression Method for Cloud Computing 18th

PLZMA: A Parallel Data Compression Method for Cloud Computin...

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

作者： Wang, Xin Gan, Lin Xu, Jingheng Yang, Jinzhe Xia, Maocai Fu, Haohuan Huang, Xiaomeng Yang, Guangwen Tsinghua Univ Dept Comp Sci & Technol Beijing Peoples R China Tsinghua Univ Key Lab Earth Syst Modeling Minist Educ Beijing Peoples R China Tsinghua Univ Dept Earth Syst Sci Beijing Peoples R China Natl Supercomp Ctr Wuxi Jiangsu Peoples R China Imperial Coll London Dept Comp London England Qingdao Natl Lab Marine Sci & Technol Lab Reg Oceanog & Numer Modeling Qingdao Peoples R China

ISBN: (纸本)9783030050573;9783030050566

Recent decades have seen the rapid development of cloud computing, resulting in a huge breakthrough for people to handle the data produced every second and everywhere. Meanwhile, data compression is becoming increasingly important, due to its great potential in benefiting both the network transportation and the storage. Based on the urgent demand in high-efficient compression method with balanced performance in both merits of compression time and ratio, this paper presents PLZMA, a parallel design of LZMA. Process-level and thread-level parallelisms are implemented according to the algorithm of LZMA, which have gained great improvement in compression time, while ensuring a fair compression ratio. Experimental results on real-world application showed that PLZMA is able to achieve more balanced performance over other famous methods. the parallel design is able to achieve a performance speedup of 8x over the serial baseline, using 12 threads.

关键词： Data compression parallel computing LZMA

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A C++ generic parallel pattern interface for stream processing 16th

A C++ generic parallel pattern interface for stream processi...

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16th international conference on algorithms and architectures for parallel processing, ica3pp 2016

作者： Astorga, David del Rio Dolz, Manuel F. Sanchez, Luis Miguel Blas, Javier García Daniel García, J. Department of Computer Science University Carlos III of Madrid Leganés28911 Spain

ISBN: (纸本)9783319495828

Current parallel programming frameworks aid to a great extent developers to implement applications in order to exploit parallel hardware resources. Nevertheless, developers require additional expertise to properly use and tune them to operate on specific parallel platforms. On the other hand, porting applications between different parallel programming models and platforms is not straightforward and requires, in most of the cases, considerable efforts. Apart from that, the lack of highlevel parallel pattern abstractions in these frameworks increases even more the complexity for developing parallel applications. To pave the way in this direction, this paper proposes GrppI, a generic and reusable high-level parallel pattern interface for stream-based C++ applications. thanks to its high-level C++ API, this interface allows users to easily expose parallelism in sequential applications using already existing parallel frameworks, such as C++ threads, OpenMP and Intel TBB. We evaluate this approach using an image processing use case to demonstrate its benefits from the usability, flexibility, and performance points of view. © Springer international Publishing AG 2016.

关键词： parallel programming

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A note on developing optimal and scalable parallel two-list algorithms

A note on developing optimal and scalable parallel two-list ...

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12th international conference on algorithms and architectures for parallel processing, ica3pp 2012

作者： Chedid, Fouad B. College of Arts and Applied Sciences Dhofar University Oman Department of Computer Science Notre Dame University - Louaize Lebanon

ISBN: (纸本)9783642330643

We show that developing an optimal parallelization of the two-list algorithm is much easier than we once thought. All it takes is to observe that the steps of the search phase of the two-list algorithm are closely related to the steps of a merge procedure for merging two sorted lists, and we already know how to parallelize merge efficiently. Armed with this observation, we present an optimal and scalable parallel two-list algorithm that is easy to understand and analyze, while it achieves the best known range of processor-time tradeoffs for this problem. In particular, our algorithm based on a CREW PRAM model takes time O(2n/2 - α) using 2α processors, for 0 ≤ α ≤ n/2 - 2logn + 2. © 2012 Springer-Verlag.

关键词： parallel processing systems

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A verified library of algorithmic skeletons on evenly distributed arrays

A verified library of algorithmic skeletons on evenly distri...

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12th international conference on algorithms and architectures for parallel processing, ica3pp 2012

作者： Bousdira, Wadoud Loulergue, Frédéric Tesson, Julien LIFO University of Orléans France Kochi University of Technology Japan

ISBN: (纸本)9783642330773

To make parallel programming as widespread as parallel architectures, more structured parallel programming paradigms are necessary. One of the possible approaches are algorithmic skeletons. they can be seen as higher order functions implemented in parallel. Algorithmic skeletons offer a simple interface to the programmer without all the details of parallel implementations as they abstract the communications and the synchronisations of parallel activities. To write a parallel program, users have to combine and compose the skeletons. Orléans Skeleton Library (OSL) is an efficient meta-programmed C++ library of algorithmic skeletons that manipulate distributed arrays. A prototype implementation of OSL exists as a library written with the function parallel language Bulk Synchronous parallel ML (BSML). In this paper we are interested in verifying the correctness of a subset of this prototype implementation. To do so, we give a functional specification of a subset of OSL and we prove the correctness of the BSML implementation with respect to this functional specification, using the Coq proof assistant. To illustrate how the user could use these skeletons, we prove the correctness of two applications implemented with them. © 2012 Springer-Verlag.

关键词： parallel programming

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An implementation of parallel 2-D FFT using intel AVX instructions on multi-core processors

An implementation of parallel 2-D FFT using intel AVX instru...

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12th international conference on algorithms and architectures for parallel processing, ica3pp 2012

作者： Takahashi, Daisuke Faculty of Engineering Information and Systems University of Tsukuba 1-1-1 Tennodai Tsukuba Ibaraki 305-8573 Japan

ISBN: (纸本)9783642330643

In this paper, we propose an implementation of a parallel two-dimensional fast Fourier transform (FFT) using Intel Advanced Vector Extensions (AVX) instructions on multi-core processors. the combination of vectorization and a block two-dimensional FFT algorithm is shown to effectively improve performance. We vectorized FFT kernels using the AVX instructions. Performance results of two-dimensional FFTs on multi-core processors are reported. We successfully achieved a performance of over 61 GFlops on an Intel Xeon E5-2670 (2.6 GHz, two CPUs, 16 cores) and over 24 GFlops on an Intel Core i7-3930K (3.2 GHz, one CPU, six cores) for a 212 x 212-point FFT. © 2012 Springer-Verlag.

关键词： Fast Fourier transforms

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A massively parallel hardware for modular exponentiations using the m-ary method

A massively parallel hardware for modular exponentiations us...

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10th international conference algorithms and architectures for parallel processing, ica3pp 2010

作者： Farias, Marcos Santana De Souza Raposo, S. Nedjah, Nadia De Macedo Mourelle, L. Department of Electronics Engineering and Telecommunications Engineering Faculty State University of Rio de Janeiro Brazil Department of System Engineering and Computation Engineering Faculty State University of Rio de Janeiro Brazil

ISBN: (纸本)3642131352

Most of cryptographic systems are based on modular exponentiation. It is performed using successive modular multiplications. One way of improving the throughput of a cryptographic system implementation is reducing the number of the required modular multiplications. Existing methods attempt to reduce this number by partitioning the exponent in constant or variable size windows. In this paper, in the purpose of further accelerating the computation of modular exponentiation, a concurrent novel approach is proposed along with hardware implementation of the concurrent m-ary method. We compare the proposed method to the sequential implementation. © Springer-Verlag Berlin Heidelberg 2010.

关键词： Cryptography

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