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检索条件"任意字段=5th International Conference on Algorithms and Architectures for Parallel Processing"

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A Gaussian Recursive Filter parallel Implementation with Overlapping

A Gaussian Recursive Filter Parallel Implementation with Ove...

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international IEEE conference on Signal-Image Technologies and Internet-Based System

作者： Pasquale De Luca Ardelio Galletti Livia Marcellino Department of Computer Science University of Salerno Italy Department of Science and Technology University of Naples Parthenope Naples Italy

ISBN: (数字)9781728156866

ISBN: (纸本)9781728156873

Gaussian convolutions computation is required in several scientific fields and, to this aim, efficient approximation methods, based on Recursive Filters (RFs), have been developed recently. Among them, Gaussian Recursive Filters (RFs) are designed to approximate the Gaussian convolution in a very efficient way. the accuracy of these methods, as is well known, can be improved by means of the use of the so-called K-iterated Gaussian recursive filters, that is in the repeated application of the basic RF. To improve the provided accuracy, K-iterated versions of these methods are also considered. Since it is often necessary to handle large size one-dimensional input signals, a parallel approach becomes mandatory. Recently, we proposed a parallel algorithm for the implementation of the K-iterated first-order Gaussian RF on multicore architectures. Here, using a similar parallelization strategy, based on a domain decomposition with overlapping, we propose a new implementation that would exploit, in terms of both accuracy and performance, the GPU (Graphics processing Unit) capabilities on CUDA environment. Tests and experiments confirm the reliability and the efficiency of the proposed implementation.

关键词： Radio frequency Convolution Graphics processing units parallel algorithms Tools Boundary conditions Instruction sets

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Resource-Reconfigured Flow Shop Scheduling and Lot-Sizing Problems in Semiconductor Test 5

Resource-Reconfigured Flow Shop Scheduling and Lot-Sizing Pr...

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5th international conference on Industrial Engineering and Applications (ICIEA)

作者： Zhang, Yige Lu, Yuangen Zhang, Cangrong Tsinghua Univ Res Ctr Modern Logist Grad Sch Shenzhen Shenzhen Peoples R China

ISBN: (纸本)9781538657485

this paper presents a study of the production planning decisions in the context of semiconductor test industry. In our problem, the items to be tested need to go through a series of stages (stations) which require different configurations of resources (kits) which are shared among stages. We establish two novel mixed integer programming models, with the objective to minimize the total processing cost, subject to the resource reconfiguration constraints on the test resources, machine capacity, and the changing-over time for setting up the station for different stages. Numerical examples are conducted to illustrate the performance of our models, and the comparisons between the two models are given as well.

关键词： resource reconfiguration lot-sizing and scheduling semiconductor test unrelated parallel machine

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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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OpenCL Superpixel Implementation on a General Purpose Multi-core CPU

OpenCL Superpixel Implementation on a General Purpose Multi-...

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IEEE international conference on Imaging Systems and Techniques (IST)

作者： Haseljic, Hana Cogo, Emir Prazina, Irfan Turcinhodzic, Razija Buza, Emir Akagic, Amila Univ Sarajevo Dept Comp Sci & Informat Fac Elect Engn Sarajevo Bosnia & Herceg

ISBN: (纸本)9781538666289

Multi-, many-core, hybrid processors and parallel programming languages are slowly becoming pervasive in mainstream computing. It is expected that they will affect a large spectrum of systems, from embedded and general-purpose, to high-end computing systems. this architectural change has already challenged programmers to efficiently write an application code that can scale over many cores to utilize its computational power. Moreover, many heterogeneous architectures exist today, hence there was an emergent need for a uniform interface to these architectures. Recently, Khronos Group defined the Open Computing Language (OpenCL) for abstracting the underlying hardware, which enables software developers to write a portable code across different shared-memory architectures. In this paper, we introduce a new parallel implementation of one of the fastest image segmentation algorithms known as Simple Linear Iterative Clustering based on OpenCL. We evaluate the effectiveness of this implementation using only multi-core GPCPU. Our implementation is fully compatible with sequential implementation. When the algorithm is executed sequentially it utilizes only 25% of total computational power of a GPCPU for any image resolution, while its modified algorithm is able to utilize close to 100% for high resolution images. the resulting algorithm is up to 5x faster than its sequential counterpart.

关键词： parallel programming multi-core gpcpu opencl superpixel image segmentation image processing

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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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Brain Tumor Segmentation by Texture Feature Extraction with the parallel Implementation of Fuzzy C-Means using CUDA on GPU 5

Brain Tumor Segmentation by Texture Feature Extraction with ...

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5th IEEE international conference on parallel, Distributed and Grid Computing (PDGC)

作者： Saxena, Sanjay Shama, Suraj Int Inst Informat Technol Bhubaneswar IIIT Bhuban Dept Comp Sci & Engn Bhubaneswar India

ISBN: (纸本)9781728106465

Exact segmentation of the brain tumor is one of the imperative tasks in medical image processing and its analysis as it deals with extracting the information of the tumorous region from the brain MRI sequences. Automated segmentation and detection of brain tumors from the brain MRI is an exigent issue caused by the texture, size, shape, and location. In this paper, a significant method of brain tumor segmentation from the FLAIR MRI sequences is by classifying local window followed by parallel fuzzy c means clustering. Fuzzy c- means methods have shown their efficiency in extracting a variety of objects in several medical image processing applications. However, one of the major issues of these algorithms is high computational requirements at the time of dealing with large data set. Nowadays, NVIDIA's GPU plays an extremely essential role in implementing such time-consuming algorithms to reduce the time complexity. Our experiments based on NCI-MICCAI BRATS 2017 FLAIR MRI of HGG (High-Grade Glioma) demonstrate the efficiency of the implemented parallel algorithm. For the segmentation of tumorous region, a mechanism of sliding window is implemented on CPU (host) in which a 45 X 45 sized window is taken to classify whether that particular window is having tumor region or not. For perfect segmentation at the GPU (device) side, fuzzy c means technique is used to get the exact location of the tumor. Approx 17.6 speed up obtained, for the BRATS data sets over the implementation of the algorithm on CPU. Apart from speed up significant dice similarity coefficients are obtained which shows the efficient segmentation in the reasonable time.

关键词： Brain Tumor Segmentation Computed Unified Device Architecture Graphics processing Unit High Performance Computing Texture Feature Extraction Fuzzy C-means

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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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Molecular Rotors in Viscous Fluids: A Numerical Analysis Aid by GPU Computing 10th

Molecular Rotors in Viscous Fluids: A Numerical Analysis Aid...

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10th international conference on Supercomputing, ISUM 2019

作者： Gutiérrez-Garibay, Daniel Híjar, Humberto Engineering School La Salle University Mexico Benjamin Franklin 45 Mexico City06140 Mexico

ISBN: (纸本)9783030380427

Molecular rotors are microscopic structures composed of two coupled parts that can rotate mutually. they have promising applications in viscosity measurement and flow detection in micro-sized and complex geometries. We propose a model for studying molecular rotors as classical systems moving in viscous environments, subjected to internal interactions and stochastic forces. Our model is expressed as a set of non-linear stochastic Langevin equations that are solved numerically using a Brownian Dynamics procedure. Attention is focused on the calculation of the two-time correlation function of the internal angular variable. For small internal forces, this correlation turns out to have a very slow time decay and its correct estimation requires long numerical experiments. We propose a CUDA implementation for a computer cluster with TESLA GPUs that calculates angular correlation functions in parallel. the implementation reduces the used computational time considerably in comparison with the one consumed by a usual serial scheme. It allows for the simulation of massive molecular rotors ensembles from which reliable results can be obtained. It is discussed how the GPU implementation can be improved in modern GPUs architectures. © Springer Nature Switzerland AG 2019.

关键词： Graphics processing unit

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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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