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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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Faster image compression technique based on LZW algorithm using GPU parallel processing 7

Faster image compression technique based on LZW algorithm us...

引用

Joint 7th international conference on Informatics, Electronics and Vision and 2nd international conference on Imaging, Vision and Pattern Recognition, ICIEV-IVPR 2018

作者： Alam, Md. Ashraful Ahsan, Fakhrul Soobhee, Ateeq-Ur-Rahman Subhani, Mahfuze Fahmid Hossain, F.M. Saiful Islam, Md. Ruma, Kamrun Nahar Department of Computer Science and Engineering BRAC University Dhaka Bangladesh

ISBN: (纸本)9781538651612

As web, mobile, desktop and all other applications use image for different purposes, image compression technique has become one of the most important applications in image analysis as well as in computer science. though image compression is an old concept, it is still a very time-consuming process which has opened a new field of research in image compression. In this paper, we showcase our extensive research works performed on LZW (Lempel-Ziv-Welch) algorithm, a lossless image compression algorithm that yields faster computation time when it is processed with parallel computation, based on the outcome of our research. the improved time efficiency obtained from our experimental result can be perceived in the context of millisecond scale rather than in the macro level of measurement along with keeping the decoded image in lossless format. © 2018 IEEE.

关键词： Image compression

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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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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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An Improved Apriori Algorithm Applied to Mining Ancient Chinese Poems

An Improved Apriori Algorithm Applied to Mining Ancient Chin...

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7th international conference on Computer, Information and Telecommunication Systems (CITS)

作者： Li, Biwei Ji, Qing Mi, Zhenqiang Yang, Yang Guo, Yu Univ Sci & Technol Beijing Sch Comp & Commun Engn Beijing Peoples R China Beijing Inst Special Electromech Technol Beijing Peoples R China

ISBN: (纸本)9781538645994

Based on the study of classical Apriori algorithm, this paper makes a comparison between FP-tree algorithm and Eclat algorithm, analyzes the basic principles of the three algorithms and their respective advantages and disadvantages. After the research on the algorithms, the author finds that time and space optimization can not have both. So the author puts forward an improved algorithm with the conception of importance inspired by the rules of Chinese poems, and puts forward the concept of "importance degree" with examples. Compared to the classic Apriori algorithm, the new improved algorithm ignores some association rules that are not concerned by the user, and greatly reduces the generation of two candidate items. the running speed of the program is increased by up to hundreds of times in the testing process. the improved algorithm can be applied to explore the association rules of the characters in Chinese poems, which is of great significance to the study of ancient poetry and grammar.

关键词： Itemsets Data mining Sparks parallel processing Tools"

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Performance of Different Optimizers for Traffic Sign Classification

Performance of Different Optimizers for Traffic Sign Classif...

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international conference on Computing and Networking Technology (ICCNT)

作者： Ishita Joshi Milauni Desai Sannidhi Bookseller Rucha Mohod Chirag N. Paunwala Bhaumik Vaidya Sarvajanik College of Engineering and Technology Surat India

the use of traffic signs is vital especially when travelling in the highways or the hills in adverse environmental conditions. the pace of advancements in the field of Machine learning has opened doors for scopes of improvement in the performance of Convolutional Neural Network architectures dedicated to classification of traffic signs. Speed is as important as accuracy for such problems in the field of Advance Driver Assistance Systems and the use of GPU instead of CPU gives the benefit of parallel processing. Gradient Descent helps navigating towards the minima of the loss function. Purpose of various gradient descent optimizing algorithms is to help in quicker convergence. this proposed algorithm comprises of a compact Convolutional Neural Network architecture that was trained on GPU using RMSProp, Adam and Nadam optimizers on the BelgiumTS dataset. RMSProp and Adam caused either underfitting or over-fitting that was resolved by Nadam used with an appropriate dropout with 97.51 training accuracy and 96.78 testing accuracy. the predictions on test images convey that the architecture trained using Nadam works perfectly for blurry images, positionally challenging images and images with uneven illumination.

关键词： Training Road transportation Graphics processing units Computer architecture Convolutional neural networks Testing Vehicles

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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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TAMM: A New Topology-Aware Mapping Method for parallel Applications on the Tianhe-2A Supercomputer 18th

TAMM: A New Topology-Aware Mapping Method for Parallel Appli...

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

作者： Chen, Xinhai Liu, Jie Li, Shengguo Xie, Peizhen Chi, Lihua Wang, Qinglin Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Changsha 410073 Peoples R China Hunan Inst Traff Engn Inst Adv Sci & Technol Hengyang 421001 Peoples R China

ISBN: (纸本)9783030050511;9783030050504

With the increasing size of high performance computing systems, the expensive communication overhead between processors has become a key factor leading to the performance bottleneck. However, default process-to-processor mapping strategies do not take into account the topology of the interconnection network, and thus the distance spanned by communication messages may be particularly far. In order to enhance the communication locality, we propose a new topology-aware mapping method called TAMM. By generating an accurate description of the communication pattern and network topology, TAMM employs a two-step optimization strategy to obtain an efficient mapping solution for various parallel applications. this strategy first extracts an appropriate subset of all idle computing resources on the underlying system and then constructs an optimized one-to-one mapping with a refined iterative algorithm. Experimental results demonstrate that TAMM can effectively improve the communication performance on the Tianhe-2A supercomputer.

关键词： High performance computing systems Topology-aware mapping Communication pattern Network topology

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Case Study on Design and Evaluation of a Multi-Soft-Core Processor

Case Study on Design and Evaluation of a Multi-Soft-Core Pro...

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international conference and Workshop on Computing and Communication (IEMCON)

作者： Michael Kirchhoff Lothar Wagner Bernd Däne Detlef Streitferdt Wolfgang Fengler Group for Computer Architecture and Embedded Systems Technische Universität Ilmenau Ilmenau Germany Group for Software Systems and Process Informatics Technische Universität Ilmenau Ilmenau Germany

ISBN: (纸本)9781728125312

Modern FPGAs (Field Programmable Gate Arrays) are becoming increasingly important when it comes to embedded system development. Within these FPGAs, soft-core processors are often used to solve a wide range of different tasks. Soft-core processors are a cost-effective and time-efficient way to realize embedded systems. the trend for soft-core processors, as well as mainstream CPUs (central processing units), leads to multi-core architectures. Both the necessary memory architectures and the compilers play an important role in this process. In this paper, a novel method that aims at minimizing the necessary memory resources on the FPGA while maximizing the processing speed of any given algorithm is described. In the first step, an application-specializable multi-soft-core processor architecture is presented that is capable of solving problems while adhering to hard real-time deadlines. Its special architecture and other necessary features are discussed. Furthermore, a method for the generation of optimized machine code for each processor core as well as hard real-time compatible deadlock handling mechanisms are presented. Selected algorithms are implemented to demonstrate the functionality and efficiency of the realized approach for different configurations of the multi-soft-core processor architecture.

关键词： Microprocessors Multicore processing Memory architecture Multiprocessor interconnection Field programmable gate arrays Field programmable gate arrays Memory architecture macroprocessor parallel processor interconnection PROCESSOR soft-core Processor Core embedded system development Processor architectures Mechanical Code central processing units hard real-time

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A parallel Fast Fourier Transform Algorithm for Large-Scale Signal Data Using Apache Spark in Cloud 1

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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: (数字)9783030050573

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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