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检索条件"任意字段=7th International Conference on Algorithms and Architectures for Parallel Processing"

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Memory Bandwidth Contention: Communication vs Computation Tradeoffs in Supercomputers with Multicore architectures 24

Memory Bandwidth Contention: Communication vs Computation Tr...

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24th IEEE international conference on parallel and Distributed Systems (ICPADS)

作者： Langguth, Johannes Cai, Xing Sourouri, Mohammed Simula Res Lab Dept High Performance Comp Oslo Norway Acando Norway Oslo Norway

ISBN: (纸本)9781538673089

We study the problem of contention for memory bandwidth between computation and communication in super-computers that feature multicore CPUs. the problem arises when communication and computation are overlapped, and both operations compete for the same memory bandwidth. this contention is most visible at the limits of scalability, when communication and computation take similar amounts of time, and thus must be taken into account in order to reach maximum scalability in memory bandwidth bound applications. Typical examples of codes affected by the memory bandwidth contention problem are sparse matrix-vector computations, graph algorithms, and many machine learning problems, as they typically exhibit a high demand for both memory bandwidth and inter-node communication, while performing a relatively low number of arithmetic operations. the problem is even more relevant in truly heterogeneous computations where CPUs and accelerators are used in concert. In that case it can lead to mispredictions of expected performance and consequently to suboptimal load balancing between CPU and accelerator, which in turn can lead to idling of powerful accelerators and thus to a large decrease in performance. We propose a simple benchmark in order to quantify the loss of performance due to memory bandwidth contention. Based on that, we derive a theoretical model to determine the impact of the phenomenon on parallel memory-bound applications. We test the model on scientific computations, discuss the practical relevance of the problem and suggest possible techniques to remedy it.

关键词： Bandwidth Multicore processing Benchmark testing Message systems Computational modeling Sockets Instruction sets

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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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SPMP: A JavaScript Support for Shared Persistent Memory on *** 18th

SPMP: A JavaScript Support for Shared Persistent Memory on *...

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

作者： Zhang, Qipeng Li, Tianyou Deng, Pan Chen, Yuting Huang, Linpeng Rudoff, Andy Shanghai Jiao Tong Univ Shanghai Peoples R China Intel Asia Pacific R&D Co LTD Shanghai Peoples R China Intel Corp Santa Clara CA USA

ISBN: (纸本)9783030050542;9783030050535

JavaScript is widely used for scripting on client side. *** is a JavaScript runtime environment, allowing Javascript to be used for building scalable network applications on server side. However, *** does not support parallel programming, making it difficult to enhance applications' performance. Meanwhile, persistent memory (PM) shows optimistic prospects of being used in server-side applications, while few researches do exist in allowing script languages to support PM-based parallel programming. In this paper, we introduce SPMP, a JavaScript support for shared persistent memory on ***. With SPMP, each process needs to hold PersistentArrayBuffer, an object that is responsible for allocating, managing, and accessing persistent memory. Multiple processes can then share persistent memory and communicate each other by their PersistentArrayBuffer objects. Furthermore, SPMP supports dynamic load-balancing strategies and ensures data coherency, and also supports data persistence in a secondary storage. We have evaluated SPMP against Extended Memory Semantics (EMS, a state-of-the-art model for parallel programming on ***) on two data-intensive tasks. the results show that SPMP is 100 similar to 300x faster than EMS on five basic operations, and 2x faster on complicated parallel computing tasks such as counting words, due to its particular way on memory allocation and mapping.

关键词： Node.js parallel programming Shared persistent memory

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Model-Based System Design and Evaluation of Image processing architectures with SimTAny Framework 19th

Model-Based System Design and Evaluation of Image Processing...

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

作者： Deitsch, Anna Schneider, Vitali Friedrich Alexander Univ Erlangen Nuremberg Dept Comp Sci 7 Martensstr 3 D-91058 Erlangen Germany

ISBN: (纸本)9783319749471;9783319749464

Becoming a ubiquitous part of a huge number of various applications, image processing algorithms and underling architectures have to meet many different requirements. Some have real-time performance constraints combined with demands on efficient implementation for limited or various hardware resources. this poses particular challenges for design, implementation, and evaluation of efficient image processing systems. In this paper, we present a model-based approach to address these issues using our framework SimTAny. Founded on the standard modeling language UML, we propose the UML Image Proccessing Language (UIPL) to facilitate expressing image processing application algorithms directly in UML, which is especially beneficial for rapid modeling. With the help of SimTAny, such design models can be simulated in order to investigate the performance of a modeled system, to determine optimal design solutions, and to validate the required properties. We extend SimTAny to enable the generation of efficient implementation code of image processing algorithms for different target architectures. the code generated is then directly integrated in the simulation environment to increase the accuracy of our performance evaluations.

关键词： Model driven engineering UML SysML MARTE Image processing applications High-level synthesis

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three levels of fail-safe mode in MPI I/O NVRAM distributed cache 7

Three levels of fail-safe mode in MPI I/O NVRAM distributed ...

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7th Annual international Young Scientists conference on Computational Science (YSC)

作者： Malinowski, Artur Czarnul, Pawel Gdansk Univ Technol Fac Elect Telecommun & Informat Dept Comp Architecture Gdansk Poland

the paper presents architecture and design of three versions for fail-safe data storage in a distributed cache using NVRAM in cluster nodes. In the first one, cache consistency is assured through additional buffering write requests. the second one is based on additional write log managers running on different nodes. the third one benefits from synchronization with a parallel File System (PFS) for saving data into a new file which allows to keep file history at the cost of space. We have shown that the three level fail-safe mode incorporating these versions does introduce minimal overhead for a random walk microbenchmark application for a 1GB file and checkpoints created every 2000 iterations, computing powers of a graph with 10000 vertices and up to 20% overhead for parallel processing of images up to 1000 megapixels compared to the basic NVRAM cache without fail-safe modes. We also presented times for checkpoint creation and restoring for sizes up to 10GBs. (C) 2018 the Authors. Published by Elsevier B.V.

关键词： NVRAM parallel MPI I/O extension fail safe distributed cache

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parallel Model Checking on Pushdown Systems 16

Parallel Model Checking on Pushdown Systems

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16th IEEE ISPA / 17th IEEE IUCC / 8th IEEE BDCloud / 11th IEEE SocialCom / 8th IEEE SustainCom

作者： Wei, Hansheng Chen, Xinyu Ye, Xin Fu, Neng Huang, Yanhong Shi, Jianqi East China Normal Univ Natl Trusted Embedded Software Engn Technol Res C Shanghai Peoples R China Hardware Software Codesign Technol & Applicat Eng Shanghai Peoples R China Shanghai Key Lab Trustworthy Comp Shanghai Peoples R China LIPN Paris France Paris Univ 7 Paris France

ISBN: (纸本)9781728111414

Pushdown systems (PDSs) have been widely used in program verification, such as recursive programs, malware detection, and other model-checking problems. As programs become more complicated, the model-checking process of PDSs represents a real challenge, which is the well-known state explosion problem. this problem may be cost expensive both in terms of memory and time. parallel computing has been introduced to overcome this limitation, especially GPU computing. In this paper, we propose novel parallel algorithms to accelerate model checking on PDSs according to the characteristics of automata-theoretic. To represent the model-checking process on parallel architectures, we propose two new models: multi-threaded P-automaton and multi-threaded Buchi pushdown systems. Moreover, we design a highly efficient data structure for PDSs and dynamic task management to accommodate the GPU. Compared with Moped, a popular model checker for PDSs, our approach achieves promising performance.

关键词： CUDA GPU Model Checking parallel computing Pushdown Systems

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PLZMA: A parallel Data Compression Method for Cloud Computing 1

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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: (数字)9783030050573

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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Design and Implementation of ConvNet for Handwritten Digits Classification on Graphical processing Unit 7

Design and Implementation of ConvNet for Handwritten Digits ...

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7th IEEE international conference on Communication and Signal processing (IEEE ICCSP)

作者： Shaziya, Humera Shyamala, K. Zaheer, Raniah Osmania Univ UCE Dept CSE Hyderabad Andhra Pradesh India Najran Univ Dept CS Najran Saudi Arabia

ISBN: (纸本)9781538635216

Convolutional Neural Network (CNN) or ConvNet is the leading-edge deep learning model that has achieved phenomenal successes in the tasks of image classification, object recognition, speech recognition and natural language processing. ConvNets are inherently complex architectures and training ConvNets requires significant amount of computation. there is a need to determine whether GPU or CPU provide the effective method for implementation of ConvNets. Very few studies have come up with the comparison of the implementation of ConvNets on both CPU and GPU. the present work examines the impact of GPU on the implementation of ConvNets. ConvNet is trained on MNIST dataset to perform classification of handwritten digits. the experiments have been performed on both CPU and GPU and observed that there is a performance improvement of 5 times in terms of training time speedup on GPU. the proposed work also investigates the effect of regularization and the results show that regularization indeed reduces the problem of overfitting.

关键词： Convolutional Neural Networks (ConvNet) Deep Learning Graphical processing Unit (GPU) Handwritten Digits Classification

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Adaptive Asynchronous parallelization of Graph algorithms 18

Adaptive Asynchronous Parallelization of Graph Algorithms

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44th ACM SIGMOD international conference on Management of Data

作者： Fan, Wenfei Lu, Ping Luo, Xiaojian Xu, Jingbo Yin, Qiang Yu, Wenyuan Xu, Ruiqi Univ Edinburgh Edinburgh Midlothian Scotland Beihang Univ BDBC Beijing Peoples R China 7 Bridges Ltd Cambridge MA 02129 USA

ISBN: (纸本)9781450317436

this paper proposes an Adaptive Asynchronous parallel (AAP) model for graph computations. As opposed to Bulk Synchronous parallel (BSP) and Asynchronous parallel (AP) models, AAP reduces both stragglers and stale computations by dynamically adjusting relative progress of workers. We show that BSP, AP and Stale Synchronous parallel model (SSP) are special cases of AAP. Better yet, AAP optimizes parallel processing by adaptively switching among these models at different stages of a single execution. Moreover, employing the programming model of GRAPE, AAP aims to parallelize existing sequential algorithms based on fixpoint computation with partial and incremental evaluation. Under a monotone condition, AAP guarantees to converge at correct answers if the sequential algorithms are correct. Furthermore, we show that AAP can optimally simulate MapReduce, PRAM, BSP, AP and SSP. Using real-life and synthetic graphs, we experimentally verify that AAP outperforms BSP, AP and SSP for a variety of graph computations.

关键词： parallel model parallelization graph computations Church-Rosser

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Network-Aware Grouping in Distributed Stream processing Systems 18th

Network-Aware Grouping in Distributed Stream Processing Syst...

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

作者： Chen, Fei Wu, Song Jin, Hai Huazhong Univ Sci & Technol Sch Comp Sci & Technol Serv Comp Technol & Syst Lab Cluster & Grid Comp Lab Wuhan 430074 Peoples R China

ISBN: (纸本)9783030050511;9783030050504

Distributed Stream processing (DSP) systems have recently attracted much attention because of their ability to process huge volumes of real-time stream data with very low latency on clusters of commodity hardware. Existing workload grouping strategies in a DSP system can be classified into four categories (i.e. raw and blind, data skewness, cluster heterogeneity, and dynamic load-aware). However, these traditional stream grouping strategies do not consider network distance between two communicating operators. In fact, the traffic from different network channels makes a significant impact on performance. How to grouping tuples according to network distances to improve performance has been a critical problem. In this paper, we propose a network-aware grouping framework called Squirrel to improve the performance under different network distances. Identifying the network location of two communicating operators, Squirrel sets a weight and priority for each network channel. It introduces Weight Grouping to assign different numbers of tuples to each network channel according to channel's weight and priority. In order to adapt to changes in network conditions, input load, resources and other factors, Squirrel uses Dynamic Weight Control to adjust network channel's weight and priority online by analyzing runtime information. Experimental results prove Squirrel's effectiveness and show that Squirrel can achieve 1.67x improvement in terms of throughput and reduce the latency by 47%.

关键词： Stream processing Load balancing Grouping Network distance

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