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共 31 条记录，以下是11-20 订阅

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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...

引用

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

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

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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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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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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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