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Asynchronous parallel Dijkstra's Algorithm on Intel Xeon Phi Processor How to Accelerate Irregular Memory Access Algorithm 18th

Asynchronous Parallel Dijkstra's Algorithm on Intel Xeon Phi...

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

作者： Zhang, Weidong Zhang, Lei Chen, Yifeng Peking Univ EECS Beijing 100871 Peoples R China HuaWei Inc Shenzhen Peoples R China

ISBN: (纸本)9783030050511;9783030050504

As the instruction-level parallelism (ILP) on CPU develops to a rather advanced level, the exploration that whether many-core architecture is applicable for graph algorithms is generating more interests in researchers. However, due to the irregular memory access and the low ratio of computation to memory access, the performance of graph algorithms on many-core architectures has never worked good enough. To obtain outstanding speedup on many-core architecture, first of all, we need to figure out three questions: (i) how to optimize the memory access, (ii) how to minimize the overhead of synchronization, (iii) how to exploit the parallelism in algorithm. Prior works hardly reach the goal if such questions are treated in separated way. throughout this paper, we aim to settle these questions systematically, and try to provide a set of methods of optimizing graph algorithms on many-core architecture. this paper mainly discusses how to accelerate the Single Source Shortest Path (SSSP) problem on Intel Many Integrated Core (MIC) architecture, on which we propose an asynchronous parallel Dijkstra's algorithm. It aims at maximizing parallelism and minimizing overhead of synchronization. Experimental result shows that the MIC architecture could efficiently solve the SSSP problem, and its performance could be sped up by 9.2x compared to the benchmark of DIMACS.

关键词： Single source shortest path parallel algorithm Intel MIC Road network

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parallel Implementation and Optimizations of Visibility Computing of 3D Scene on Tianhe-2 Supercomputer 18th

Parallel Implementation and Optimizations of Visibility Comp...

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

作者： Xu, Zhengwei Wang, Xiaodong Zhang, Congpin Wu, Changmao Henan Normal Univ Sch Comp & Informat Engn Xinxiang Henan Peoples R China Chinese Acad Sci Inst Software Lab Parallel Software & Computat Sci Beijing Peoples R China

ISBN: (纸本)9783030050511;9783030050504

Visibility computing is a basic problem in computer graphics, and is often the bottleneck in realistic rendering algorithms. Some of the most common include the determination of the objects visible from a viewpoint, virtual reality, real-time simulation and 3D interactive design. As one technique to accelerate the rendering speed, the research on visibility computing has gained great attention in recent years. Traditional visibility computing on single processor machine has been unable to meet more and more large-scale and complex scenes due to lack parallelism. However, it will face many challenges to design parallel algorithms on a cluster due to imbalance workload among compute nodes, the complicated mathematical model and different domain knowledge. In this paper, we propose an efficient and highly scalable framework for visibility computing on Tianhe-2 supercomputer. Firstly, a new technique called hemispheric visibility computing is designed, which can overcome the visibility missing of traditional perspective algorithm. Secondly, a distributed parallel algorithm for visibility computing is implemented, which is based on the master-worker architecture. Finally, we discuss the issue of granularity of visibility computing and some optimization strategies for improving overall performance. Experiments on Tianhe-2 supercomputer show that our distributed parallel visibility computing framework almost reaches linear speedup by using up to 7680 CPU cores.

关键词： Visibility computing Performance optimization parallel implementation

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Optimizing B⁺-Tree Searches on Coupled CPU-GPU architectures 20th

Optimizing B<SUP>+</SUP>-Tree Searches on Coupled CPU-GPU Ar...

引用

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

作者： Huang, Han Luan, Hua Beijing Normal Univ Beijing Peoples R China

ISBN: (纸本)9783030602451;9783030602444

the B+-tree is an important index in the fields of data warehousing and database management systems. With the development of new hardware technologies, the B+-tree needs to be revisited to fully take advantage of hardware resources. In this paper, we focus on optimization techniques to increase the searching performance of B+-trees on the coupled CPU-GPU architecture. First, we propose a hierarchical searching approach on the single coupled GPU to efficiently deal with leaf nodes of B+-trees. It adopts a flexible strategy to determine the number of work items in a work group to search one key in order to reduce irregular memory accesses and divergent branches in the work group. Second, we present a co-processing pipeline method on the coupled architecture. the CPU and the integrated GPU process the sorting and searching tasks simultaneously to hide sorting and partial searching latencies. A distribution model is designed to support the workload balance strategy based on real-time performance. Our performance study shows that the hierarchical searching scheme provides an improvement up to 36% on the GPU compared to the baseline algorithm with fixed number of work items and the co-processing pipeline method further increases the throughput by a factor of 1.8. To the best of our knowledge, this paper is the first study to consider both the CPU and the coupled GPU to optimize B+-trees searches.

关键词： B+-trees the coupled architecture Integrated GPU Co-processing

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A Distributed Framework for Online Stream Data Clustering 20th

A Distributed Framework for Online Stream Data Clustering

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

作者： Ding, Jiafeng Fang, Junhua Chao, Pingfu Xu, Jiajie Zhao, PengPeng Zhao, Lei Soochow Univ Dept Comp Sci & Technol Suzhou Peoples R China Univ Queensland Brisbane Qld Australia

ISBN: (纸本)9783030602451;9783030602444

the recent prevalence of positioning sensors and mobile devices generates a massive amount of spatial-temporal data from moving objects in real-time. As one of the fundamental processes in data analysis, the clustering on spatial-temporal data creates various applications, like event detection and travel pattern extraction. However, most of the existing works only focus on the offline scenario, which is not applicable to online time-sensitive applications due to their low efficiency and ignorance of temporal features. In this paper, we propose a distributed streaming framework for spatial-temporal data clustering, which accepts various clustering algorithms while ensuring low resource consumption and result correctness. the framework includes a dynamic partitioning strategy for continuous load-balancing and a cluster-merging algorithm based on convex hulls [10], which guarantees the result correctness. Extensive experiments on real dataset prove the effectiveness of our proposed framework and its advantage over existing solutions.

关键词： Real-time cluster analysis Distributed stream processing Spatial-temporal data mining Top-k query parallel computing

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A Communication-Avoiding Algorithm for Molecular Dynamics Simulation 19th

A Communication-Avoiding Algorithm for Molecular Dynamics Si...

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

作者： Wang, Bei Chen, Yifeng Hou, Chaofeng Peking Univ EECS Beijing 100871 Peoples R China Chinese Acad Sci Inst Proc Engn Beijing Peoples R China

ISBN: (纸本)9783030389918;9783030389901

Molecular dynamics and many similar time-dependent computing tasks are defined as simple state updates over multiple time steps. In recent years, modern supercomputing clusters have enjoyed fast-growing compute capability and moderate-growing memory bandwidth, but their improvement of network bandwidth/latency is limited. In this paper, we propose a new communication-avoiding algorithmic model based on asynchronous communications which, unlike BSP, records and handles multiple iterative states together. the basic idea is to let computation run in small regular time steps while communications over longer dynamic time steps. Computation keeps checking inaccuracies so that the intervals between communications are small in volatile scenarios but longer when dynamics is smooth. this helps reduce the number of data exchanges via network communication and hence improve the overall performance when communication is the bottleneck. We test MD simulation of condensed covalent materials on the Sunway TaihuLight. For best time-to-solution, the general-purpose supercomputer Sunway TaihuLight performs 11.8K steps/s for a system with 2.1 million silicon atoms and 5.1 K steps/s for 50.4 million silicon atoms. this time-to-solution performance is close to those of state-of-art hardware solution. A software solution using general-purpose supercomputers makes the technology more accessible to the general scientific users.

关键词： BSP parallel-in-time Molecular dynamics

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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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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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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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Distributed parallel Simulation of Primary Sample Space Metropolis Light Transport 18th

Distributed Parallel Simulation of Primary Sample Space Metr...

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

作者： Wu, Changmao Zhang, Changyou Sun, Qiao Chinese Acad Sci Inst Software Lab Parallel Software & Computat Sci Beijing Peoples R China

ISBN: (纸本)9783030050511;9783030050504

Monte-Carlo rendering algorithms are known for producing highly realistic images, but at a significant computational cost, because they rely on tracing up to trillions of light paths through a scene to simulate physically based light transport. For this reason, a large body of research exists on various techniques for accelerating these costly algorithms. As one of the Monte-Carlo rendering algorithms, PSSMLT (Primary Sample Space Metropolis Light Transport) is widely used nowadays for photorealistic rendering. Unfortunately, the computational cost of PSSMLT is still very high since the space of light paths in high-dimension and up to trillions of paths are typically required in such path space. Recent research on PSSMLT has proposed a variety of optimized methods for single node rendering, however, multi-node rendering for PSSMLT is rarely mentioned due in large part to the complicated mathematical model, complicated physical processes and the irregular memory access patterns, and the imbalanced workload of light-carrying paths. In this paper, we present a highly scalable distributed parallel simulation framework for PSSMLT. Firstly, based on light transport equation, we propose the notion of sub-image with certain property for multi-node rendering and theoretically prove that the whole set of sub-images can be combined to produce the final image;then we further propose a sub-image based assignment partitioning algorithm for multi-node rendering since the traditional demand-driven assignment partitioning algorithm doesn't work well. Secondly, we propose a physically based parallel simulation for the PSSMLT algorithm, which is revealed on a parallel computer system in master-worker paradigm. Finally, we discuss the issue of granularity of the assignment partitioning and some optimization strategies for improving overall performance, and then a static/dynamic hybrid scheduling strategy is described. Experiments show that framework has a nearly linear speedup along wi

关键词： Primary sample space metropolis light transport Physically based ray tracing Assignment partitioning Hybrid scheduling Distributed computing Performance optimization

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algorithms and architectures for parallel processing 1

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丛书名： Lecture Notes in Computer Science

1000年

作者： Yang Xiang Wanlei Zhou Alfredo Cuzzocrea Michael Hobbs

ISBN: (数字)9783642246500

ISBN: (纸本)9783642246494

this two volume set LNCS 7016 and LNCS 7017 constitutes the refereed proceedings of the 11th international conference on algorithms and architectures for parallel processing, ica3pp 2011, held in Melbourne, Australia, in October 2011. the first volume presents 24 revised regular papers and 17 revised short papers together with the abstract of the keynote lecture - all carefully reviewed and selected from 85 initial submissions. the papers cover the many dimensions of parallel algorithms and architectures, encompassing fundamental theoretical approaches, practical experimental results, and commercial components and systems and focus on two broad areas of parallel and distributed computing, i.e., architectures, algorithms and networks, and systems and applications.

关键词： Algorithm Analysis and Problem Complexity Artificial Intelligence Software Engineering Information Systems Applications (incl. Internet) Computer Communication Networks Management of Computing and Information Systems

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