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共 292 条记录，以下是71-80 订阅

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parallel SCC Detection Based on Reusing Warps and Coloring Partitions on GPUs 20th

Parallel SCC Detection Based on Reusing Warps and Coloring P...

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

作者： Hou, Junteng Wang, Shupeng Wu, Guangjun Ma, Bingnan Zhang, Lei Chinese Acad Sci Inst Informat Engn Beijing Peoples R China Univ Chinese Acad Sci Sch Cyber Secur Beijing Peoples R China Natl Comp Network Emergency Response Tech Team Coordinat Ctr China Beijing Peoples R China

ISBN: (纸本)9783030602451;9783030602444

Detecting strongly connected components (SCCs) in directed graphs is crucial for analyzing the structure of graphs. It can accelerate the process of large-scale graphs with applying GPUs to graph computation. However, GPU-based SCC detection still faces the challenge from graphs, especially real-world graphs, skewing and strict synchronization requirement. In this paper, we proposed a new paradigm of identifying SCCs with reusing warps and coloring partitions. the scheme of reusing warps assigns multiple vertices to each virtual warp at one time, which greatly reduces the number of warps that are applied but assigned few tasks. Furthermore, we proved that the partitions formed by coloring independently contains all SCCs in them, so we use coloring to identify more partitions and parallel detect SCCs in each partition to get more SCCs. Finally, the colored partitions are deeply combined with the following traversal to optimize the algorithm. We conduct extensive theoretical and experimental analysis to demonstrate the efficiency and accuracy of our approach. the experimental results expose that our approach can achieve 7.23x, 30.55x, 1.75x and 1.26x speedup for SCC detection using NVIDIA K80 compared with algorithms of Tarjan, Barnat, Hong and Slota respectively.

关键词： Strongly connected components detection GPUs Reusing warps Coloring partitions Partitions combined with traversal

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CHEAPS2AGA: Bounding Space Usage in Variance-Reduced Stochastic Gradient Descent over Streaming Data and Its Asynchronous parallel Variants 20th

CHEAPS2AGA: Bounding Space Usage in Variance-Reduced Stochas...

引用

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

作者： Peng, Yaqiong Fei, Haiqiang Li, Lun Ding, Zhenquan Hao, Zhiyu Chinese Acad Sci Inst Informat Engn Beijing 100093 Peoples R China

ISBN: (纸本)9783030602390;9783030602383

Stochastic Gradient Descent (SGD) is widely used to train a machine learning model over large datasets, yet its slow convergence rate can be a bottleneck. As a remarkable family of variance reduction techniques, memory algorithms such as SAG and SAGA have been proposed to accelerate the convergence rate of SGD. However, these algorithms need to store per training data point corrections in memory. the unlimited space usage feature is impractical for modern large-scale applications, especially over data points that arrive over time (referred to as streaming data in this paper). To overcome this weakness, this paper investigates the methods that bound the space usage in the state-of-the-art family of variance-reduced stochastic gradient descent over streaming data, and presents CHEAPS2AGA. At each step of updating the model, the key idea of CHEAPS2AGA is always reserving N random data points as samples, while re-using information about past stochastic gradients across all the observed data points with limited space usage. In addition, training an accurate model over streaming data requires the algorithm to be time-efficient. To accelerate the model training phase, CHEAPS2AGA embraces a lock-free data structure to insert new data points and remove unused data points in parallel, and updates the model parameters without using any locking. We conduct comprehensive experiments to compare CHEAPS2AGA to prior related algorithms suited for streaming data. the experimental results demonstrate the practical competitiveness of CHEAPS2AGA in terms of scalability and accuracy.

关键词： Machine learning Stochastic gradient descent Streaming data parallel algorithm Multicore

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FastDRC: Fast and Scalable Genome Compression Based on Distributed and parallel processing 19th

FastDRC: Fast and Scalable Genome Compression Based on Distr...

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

作者： Ji, Yimu Fang, Houzhi Yao, Haichang He, Jing Chen, Shuai Li, Kui Liu, Shangdong Nanjing Univ Posts & Telecommun Sch Comp Sci Nanjing Peoples R China Nanjing Inst Ind Technol Sch Comp & Software Nanjing 210023 Peoples R China Swinburne Univ Technol Sch Software & Elect Engn Melbourne Vic 3122 Australia Nanjing Univ Posts & Telecommun Inst High Performance Comp & Big Data Nanjing 210003 Peoples R China Nanjing Ctr HPC China Nanjing 210003 Peoples R China Jiangsu HPC & Intelligent Proc Engineer Res Ctr Nanjing 210003 Peoples R China

ISBN: (纸本)9783030389611;9783030389604

With the advent of next-generation sequencing technology, sequencing costs have fallen sharply compared to the previous sequencing technologies. Genomic big data has become the significant big data application. In the face of growing genomic data, its storage and migration face enormous challenges. therefore, researchers have proposed a variety of genome compression algorithms, but these algorithms cannot meet the processing requirements for large amount of biological data and high processing speed. this manuscript proposes a parallel and distributed referential genome compression algorithm-Fast Distributed Referential Compression (FastDRC). this algorithm compresses a large number of genomic sequences in parallel under the Apache Hadoop distributed computing framework. Experiments show that the compression efficiency of the FastDRC is greatly improved when it compresses large quantities of genomic data. Moreover, FastDRC leads to the only distributed computing method known to us in the field of genome compression. the source code for FastDRC can be obtained from this link: https://***/GhostCCCathenry/FastDRC.

关键词： Genome compression Distributed processing Apache Hadoop FASTA

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

16th International Conference on Algorithms and Architecture...

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

ISBN: (纸本)9783319495828

the proceedings contain 52 papers. the special focus in this conference is on parallel, Distributed architectures, Software Systems, Programming, Distributed, Network-Based Computing, Big Data, Its Applications, parallel, Distributed algorithms, Performance Modeling and Evaluation. the topics include: Optimizing execution performance by automatic query relaxation and queue scheduling;hardware-based sequential consistency violation detection made simpler;optimized mapping spiking neural networks onto network-on-chip;a portable lock-free bounded queue;creating distributed execution plans with bobolangng;a new scalable approach for distributed metadata in HPC;enabling android-based devices to high-end GPGPUs;3-additive approximation algorithm for multicast time in 2D torus networks;a novel optical high-radix switch architecture for 3D integration;a platform based on microclouds for neighborhood services;impact of shutdown techniques for energy-efficient cloud data centers;implement and optimization of indoor positioning system based on Wi-Fi signal;road segment information based named data networking for vehicular environments;energy-aware query processing on a parallel database cluster node;current flow betweenness centrality with apache spark;light loss-less data compression, with GPU implementation;a GPU-based backtracking algorithm for permutation combinatorial problems;scaling DBSCAN-like algorithms for event detection systems in twitter;towards parallel CFD computation for the ADAPT framework;leveraging the performance of LBM-HPC for large sizes on GPUs using ghost cells;locality of computation for stencil optimization;an efficient implementation of LZW compression in the FPGA and GPU-based heterogeneous coding architecture for HEVC.

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HDF5-Based I/O Optimization for Extragalactic HI Data Pipeline of FAST 19th

HDF5-Based I/O Optimization for Extragalactic HI Data Pipeli...

引用

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

作者： Ji, Yiming Yu, Ce Xiao, Jian Tang, Shanjiang Wang, Hao Zhang, Bo Tianjin Univ Coll Intelligence & Comp Tianjin Peoples R China Chinese Acad Sci CAS Key Lab FAST NAOC Beijing Peoples R China

ISBN: (纸本)9783030389611;9783030389604

the Five-hundred-meter Aperture Spherical Radio Telescope (FAST), which is the largest single-dish radio telescope in the world, has been producing a very large data volume with high speed. So it requires a high performance data pipeline to covert the huge raw observed data to science data product. However, the existing solutions of pipelines widely used in radio data processing cannot tackle this situation efficiently. the paper proposes a pipeline architecture for FAST based on HDF5 format and several I/O optimization strategies. First, we design the workflow engine driving the various tasks efficiently in the pipeline;second, we design a common radio data storage specification on the top of HDF5 format, and also developed a fast converter to map the original FITS format to the new HDF5 format;third, we apply several concrete strategies to optimize the I/O operations, including chunks storage, parallel reading/writing, on-demand dump, and stream process etc. In the experiment of processing 700 GB of FAST data, the results show that HDF5 based data structure without other optimizations was 1.7 times faster than original FITS format. If chunk storage and parallel I/O optimization are applied, the overall performance can reach 4.5 times as the original one. Moreover, due to the good expansibility and flexibility, our solution of FAST pipeline can be adapted to other radio telescopes.

关键词： FAST FITS HDF5 Pipeline in parallel High performance I/O

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Distributed and parallel Ensemble Classification for Big Data Based on Kullback-Leibler Random Sample Partition 20th

Distributed and Parallel Ensemble Classification for Big Dat...

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

作者： Wei, Chenghao Zhang, Jiyong Valiullin, Timur Cao, Weipeng Wang, Qiang Long, Hao Shenzhen Univ Big Data Inst Coll Comp Sci & Software Engn Shenzhen 518000 Peoples R China Hangzhou Dianzi Univ Sch Automat Hangzhou 311305 Peoples R China Southern Univ Sci & Technol SUSTech Acad Adv Interdisciplinary Studies Shenzhen 518055 Peoples R China

ISBN: (纸本)9783030602451;9783030602444

In this article, we use a Kullback-Leibler random sample partition data model to generate a set of disjoint data blocks, where each block is a good representation of the entire data set. Every random sample partition (RSP) block has a sample distribution function similar to the entire data set. To obtain the statistical measure between them, Kernel Density Estimation (KDE) with a dual-tree recursion data structure is firstly applied to fast estimate the probability density of each block. then, based on the Kullback-Leibler (KL) divergence measure, we can obtain the statistical similarity between a randomly selected RSP data block and other RSP data blocks. We rank the RSP data blocks according to their divergence values in descending order and choose the first ten for an ensemble classification learning. the classification models are established in parallel for the selected RSP data blocks and the final ensemble classification model is obtained by the weighted voting ensemble strategy. the experiments were conducted by building XGboost model based on those ten blocks in parallel, and we incrementally ensemble them according to their KL values. the testing classification results show that our method can increase the generalization capability of the ensemble classification model. It could reduce the model building time in parallel computation environment by using less than 15% of the entire data, which could also solve the memory constraints of big data analysis.

关键词： Big data analysis Approximate computing Random sample partition Ensemble classification parallel distributed computing

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16th international conference on algorithms and architectures for parallel processing - ica3pp 2016 Collocated Workshops: 1st Workshop on Supercomputing Co-Design Technology, SCDT-2016, 1st international Workshop on theoretical Approaches to Performance Evaluation, Modeling and Simulation, TAPEMS 2016, 1st international Workshop on Trust, Security and Privacy for Big Data, BigTrust 2016, 1st edition of the workshop on Ultrascale Computing for Early Researchers, UCER 2016 and 1st international Workshop on Data Locality in Modern Computing Systems, DLMCS 2016

16th International Conference on Algorithms and Architecture...

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16th international conference on algorithms and architectures for parallel processing - ica3pp 2016 Collocated Workshops: 1st Workshop on Supercomputing Co-Design Technology, SCDT-2016, 1st international Workshop on theoretical Approaches to Performance Evaluation, Modeling and Simulation, TAPEMS 2016, 1st international Workshop on Trust, Security and Privacy for Big Data, BigTrust 2016, 1st edition of the workshop on Ultrascale Computing for Early Researchers, UCER 2016 and 1st international Workshop on Data Locality in Modern Computing Systems, DLMCS 2016

ISBN: (纸本)9783319499550

the proceedings contain 30 papers. the special focus in this conference is on TAPEMS 2016 and BigTrust 2016. the topics include: An in-memory event tracing extension to the open trace format 2;network-aware optimization of MPDATA on homogeneous multi-core clusters with heterogeneous network;formalizing data locality in task parallel applications;improving the energy efficiency of evolutionary multi-objective algorithms;a parallel model for heterogeneous cluster;comparative analysis of OpenACC compilers;traffic sign recognition based on parameter-free detector and multi-modal representation;reversible data hiding using non-local means prediction;secure data access in hadoop using elliptic curve cryptography;statistical analysis of CCM.M-K1 international comparison based on monte carlo method;redundancy elimination in the ExaStencils code generator;a dataflow IR for memory efficient RIPL compilation to FPGAs;exploring a distributed iterative reconstructor based on split bregman using PETSc;implementation of the beamformer algorithm for the NVIDIA jetson;efficiency of GPUs for relational database engine processing;geocon;a middleware for location-aware ubiquitous applications and cellular ANTomata as engines for highly parallel pattern processing.

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algorithms and architectures for parallel processing: 15th international conference, ica3pp 2015 zhangjiajie, China, November 18-20, 2015 proceedings, part IV 15th

Algorithms and architectures for parallel processing: 15th i...

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15th international conference on algorithms and architectures for parallel processing, ica3pp 2015

作者： Wang, Guojun Zomaya, Albert Perez, Gregorio Martinez Li, Kenli Central South University Changsha China The University of Sydney SydneyNSW Australia University of Murcia Murcia Spain Hunan University Changsha China

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An Axiomatization for BSP algorithms 18th

An Axiomatization for BSP Algorithms

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

作者： Marquer, Yoann Gava, Frederic Univ Paris East Lab Algorithms Complex & Log LACL Creteil France

ISBN: (纸本)9783030050573;9783030050566

the Gurevich's thesis stipulates that sequential abstract state machines (ASMS) capture the essence of sequential algorithms. On another hand, the bulk-synchronous parallel (BSP) bridging model is a well known model for HPC algorithm design. It provides a conceptual bridge between the physical implementation of the machine and the abstraction available to a programmer of that machine. the assumptions of the BSP model are thus provide portable and scalable performance predictions on most HPC systems. We follow Gurevich's thesis and extend the sequential postulates in order to intuitively and realistically capture BSP algorithms.

关键词： BSP ASM parallel algorithm HPC Postulates Cost model

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Abstract parallel Array Types and Ghost Cell Update Implementation 18th

Abstract Parallel Array Types and Ghost Cell Update Implemen...

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

作者： Zhang, Shuang Wang, Bei Chen, Yifeng Peking Univ EECS Beijing 100871 Peoples R China

ISBN: (纸本)9783030050511;9783030050504

Stencil patterns are widely used in scientific simulations and image processing. To parallelize these problems, we have to divide data into chunks that are processed by different processors. One challenge with this approach is the update of ghost cells, which are the neighbor values that calculated on remote processes. this paper focus on the update communication. We provide an abstract array types to describe distribution patterns, such as ghost cells, from global and intuitive view. Based on this description, a general copyto function is provided to perform communication automatically. Furthermore, our work makes it possible to design a distribution-independent algorithm. this results in better productivity on tuning performance.

关键词： MPI Stencil Ghost cell

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