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检索条件"任意字段=10th International Conference on Algorithms and Architectures for Parallel Processing"

共 2804 条记录，以下是561-570 订阅

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Using Neuromorphic Hardware for the Scalable Execution of Massively parallel, Communication-Intensive algorithms 11

Using Neuromorphic Hardware for the Scalable Execution of Ma...

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11th IEEE/ACM international conference on Utility and Cloud Computing (UCC-Companion) / 5th IEEE/ACM international conference on Big Data Computing, Applications and Technologies (BDCAT)

作者： Blin, Louis Awan, Ahsan Javed Heinis, thomas Imperial Coll London Dept Comp London England

ISBN: (纸本)9781728103594

Neuromorphic hardware like SpiNNaker offers massive parallelism and efficient communication of small payloads to accelerate the simulation of spiking neurons in neural networks. In this paper, we demonstrate that this hardware is also beneficial for other for applications which require massive parallelism and the large-scale exchange of small messages. More specifically, we study the scalability of PageRank on SpiNNaker and compare it to an implementation on traditional hardware. In our experiments, we show that PageRank on SpiNNaker scales better than on traditional multicore architectures.

关键词： Neurons Hardware Neuromorphics Computational modeling Multicore processing Payloads

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De(con) Struction of the Lazy-F Loop: Improving Performance of Smith Waterman Alignment

De(con) Struction of the Lazy-F Loop: Improving Performance ...

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IEEE Symposium on Bioinformatics and Bioengineering (BIBE)

作者： Roman Snytsar Research and AI Microsoft Redmond USA

ISBN: (纸本)9781728146188

Striped variation of the Smith-Waterman algorithm is known as extremely efficient and easily adaptable for the SIMD architectures. However, the potential for improvement has not been exhausted yet. the popular Lazy-F loop heuristic requires additional memory access operations, and the worst-case performance of the loop could be as bad as the nonvectorized version. We demonstrate the progression of the lazy-F loop transformations that improve the loop performance, and ultimately eliminate the loop completely. Our algorithm achieves the best asymptotic performance of all scan-based SW algorithms O(n/p+log(p)), and is very efficient in practice.

关键词： Bioinformatics parallel processing Optimization Arrays Computational complexity Instruction sets

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Investigating parallelization of Cross-language Plagiarism Detection System Using the Winnowing Algorithm in Cloud Based Implementation

Investigating Parallelization of Cross-language Plagiarism D...

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IEEE international conference on Awareness Science and Technology

作者： Anak Agung Putri Ratna F. Astha Ekadiyanto Ihsan Ibrahim Diyanatul Husna Fathimah Rahimullah Department of Electrical Engineering Faculty of Engineering Universitas Indonesia Depok Indonesia

the computational performance of cross-language plagiarism detection system using winnowing algorithm developed at the Electrical Engineering Department, Universitas Indonesia became an issue for real world application. this research is investigating the parallelization of such system implemented on a lab-scale multicore based private cloud platform using OpenStack. parallelization was done on the portion of the program where the paragraphs of reference documents are processed. the result of execution time of the overall parallelized computation was able to reach speed up of 1.07 to 3.52 times compared to the execution time of the original serial computation.

关键词： Cloud computing Plagiarism Multiprocessing systems Python parallel processing

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vPlacer: A Co-scheduler for Optimizing the Performance of parallel Jobs in Xen 18th

vPlacer: A Co-scheduler for Optimizing the Performance of Pa...

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18th international conference on algorithms and architectures for parallel processing (ICA3PP)

作者： Jiang, Peng He, Ligang Ren, Shenyuan Chen, Zhiyan Mao, Rui Univ Warwick Dept Comp Sci Coventry CV4 7ES W Midlands England Shenzhen Univ Coll Comp Sci & Software Engn Shenzhen Peoples R China

ISBN: (纸本)9783030050511;9783030050504

Xen, a popular virtualization platform which enables multiple operating systems sharing one physical host, has been widely used in various fields nowadays. Currently, the existing schedulers of Xen are initially targeting at serial jobs, which achieves a remarkable utilization of computer hardware and impressive overall performance. However, the virtualized systems are expected to accommodate both parallel jobs and serial jobs in practice, and resource contention between virtual machines results in severe performance degradation of the parallel jobs. Moreover, the physical resource is vastly wasted during the communication process due to the ineffective scheduling of parallel jobs. this paper aims to optimize the performance of the parallel jobs in Xen using the co-scheduling mechanism. In this paper, we statistically analyze the process of scheduling parallel jobs in Xen, which points out that the credit scheduler is not capable of properly scheduling a parallel job. Moreover, we propose vPlacer, a conservative co-scheduler to improve the performance of the parallel job in Xen. Our co-scheduler is able to identify the parallel jobs and optimize the scheduling process to satisfy the particularity of the parallel job. the prototype of our vPlacer is implemented, and the experimental results show that the performance of the parallel job is significantly improved and the utilization of the hardware resource is optimized.

关键词： Xen Virtualization parallel job Scheduler

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Efficient Sparse Neural Networks Using Regularized Multi Block Sparsity Pattern on a GPU

Efficient Sparse Neural Networks Using Regularized Multi Blo...

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international conference on High Performance Computing

作者： Dharma Teja Vooturi Kishore Kothapalli Center for Security Theory and Algorithmic research (CSTAR) International Institute of Information Technology Hyderabad India

ISBN: (纸本)9781728145365

A large portion of the computation in sparse neural networks comprises of multiplying a sparse matrix with a dense matrix, denoted SDMM in this paper. the SDMM operation with an unstructured sparsity pattern cannot be efficiently processed on modern architectures such as GPUs due to irregularity in compute and memory accesses. However, efficient parallel algorithms on a GPU can be designed for SDMM when the sparsity pattern is more structured. thus, the run time performance of sparse neural networks on a GPU is dependent on the sparsity pattern present in the underlying matrix. In sparse neural networks obtained using pruning based approaches, the choice of sparsity pattern not only effects the run time, but also effects the accuracy of the task for which the neural network is trained for. Sparsity patterns which have a good run time performance on a GPU may not have a good accuracy and vice-versa. the real challenge then is to given a target architecture, identify a sparsity pattern, a storage format, and an algorithm for SDMM that leads to sparse neural networks which are efficient in both run time and accuracy. In this work, we propose a novel, structured, flexible, and generic sparsity pattern called the RMB (Regularized Multi Block) sparsity pattern, and an efficient storage format (CRMB), and a fast GPU algorithm for processing RMBMM (SDMM with the multiplicand having RMB sparsity pattern). Using the RMB sparsity pattern, we achieve better trade-offs between the accuracy, and the run time performance of sparse neural networks on a GPU when compared to commonly used sparsity patterns like unstructured, and block sparsity patterns.

关键词： Neural networks Sparse matrices Graphics processing units parallel algorithms Computer architecture Task analysis conferences

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Software Effective Evaluating Technology: SWEET

Software Effective Evaluating Technology: SWEET

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IEEE international conference on Software Engineering and Service Sciences (ICSESS)

作者： Yaozong Li Tao Wang Yue Yu Dongyang Hu National Laboratory for Parallel and Distributed Processing National University of Defence Technology Changsha China

ISBN: (数字)9781728109459

ISBN: (纸本)9781728109466

In open source community, there are a large number of software resources existing. Such software resources distribute in different societies or storehouses, which require different software characteristic. this phenomenon results in difficult to evaluate software quality using traditional methods. In this case, a novel open-source software sorting algorithm may be an effective solution. Considering both subjective and objective levels, we propose a new method on software sorting and retrieving. In the subjective level, metrics are selected from the corresponding collaborative development community based on software topic. In the objective level, metrics are obtained from the group emotional evaluation value of the knowledge sharing community. We have proved the effectiveness of the method through comparison experiments. Combining with the Solrcloud tool, this method has been integrated into the OSSEAN platform.

关键词： Open source software Sorting Collaboration Software algorithms Measurement Tools

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parallelizing and optimizing LHCb-Kalman for Intel Xeon Phi KNL processors 26

Parallelizing and optimizing LHCb-Kalman for Intel Xeon Phi ...

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26th Euromicro international conference on parallel, Distributed, and Network-Based processing (PDP)

作者： Fernandez, Placido del Rio Astorga, David Dolz, Manuel F. Fernandez, Javier Awile, Omar Garcia, Daniel CERN EP LBC CH-1211 Geneva 23 Switzerland Univ Carlos III Madrid Dept Comp Sci Madrid Spain CERN IT CF FPP CH-1211 Geneva 23 Switzerland

ISBN: (纸本)9781538649756

Real time data processing is an important component of particle physics experiments with large computing resource requirements. As the Large Hadron Collider (LHC) at CERN is preparing for its next upgrade the LHCb experiment is upgrading its detector for a 30x increase in data throughput. In preparation for this upgrade the experiment is considering a number of architectural improvements encompassing both its software and hardware infrastructure. One of the hardware platforms under consideration is the Intel Xeon-Phi Knights Landing processor. thanks to its on-package high-bandwidth memory and many-core architecture it offers an interesting alternative to more traditional server systems. We present a scalable, multi-threaded and NUMA-aware Kalman filter proto-application for particle track fitting expressed in terms of generic parallel patterns using the GRPPI interface. We show how code maintainability and readability improves, while maintaining comparable levels of performance to the baseline implementation. this is achieved by keeping the parallel algorithms in the underlying framework generic, but topology aware through the use of the Portable Hardware Locality (hwloc) library, which allows us to target different architectures with the same program. We measure the performance of our topology-aware GRPPI Kalman filter implementation on the Intel Xeon-Phi Knights Landing platform and conclude on the feasibility of integrating such high-level parallelization libraries in complex software frameworks such as LHCb's Gaudi framework.

关键词： NUMA affinity LHCb parallel patterns GRPPI Kalman filter knl multi-threading xeon phi

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Collider - parallel Experiments in Silico 19th

Collider - Parallel Experiments in Silico

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19th international GI/ITG conference on Measurement, Modelling and Evaluation of Computing Systems (MMB)

作者： Scheftelowitsch, Dimitri TU Dortmund Informat 4 Dortmund Germany

ISBN: (纸本)9783319749471;9783319749464

Large-scale software experiments are a ubiquitous feature of research. For example, performance evaluation of algorithms implies testing said algorithms on a large number of test cases. We provide a software framework which helps performing experiments on large parameter spaces, benefits from multi-core architectures, and saves generated results in a machine-readable format for future post-processing.

关键词： Performance evaluation Experiments Software tools

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Proceedings of IA3 2018: 8th Workshop on Irregular Applications: architectures and algorithms, Held in conjunction with SC 2018: the international conference for High Performance Computing, Networking, Storage and Analysis

Proceedings of IA3 2018: 8th Workshop on Irregular Applicati...

引用

8th IEEE/ACM Workshop on Irregular Applications: architectures and algorithms, IA3 2018

ISBN: (纸本)9781728101866

the proceedings contain 8 papers. the topics discussed include: a block-oriented, parallel and collective approach to sparse indefinite preconditioning on GPUs;software prefetching for unstructured mesh applications;there are trillions of little forks in the road. choose wisely! - estimating the cost and likelihood of success of constrained walks to optimize a graph pruning pipeline;scale-free graph processing on a NUMA machine;a fast and simple approach to merge and merge sort using wide vector instructions;impact of traditional sparse optimizations on a migratory thread architecture;mix-and-match: a model-driven runtime optimization strategy for BFS on GPUs;and high-performance GPU implementation of PageRank with reduced precision based on mantissa segmentation.

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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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