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检索条件"任意字段=4th International Symposium on Parallel and Distributed Processing and Applications"

共 3818 条记录，以下是131-140 订阅

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PCERE: Fine-grained parallel Benchmark Decomposition for Scalability Prediction 29

PCERE: Fine-grained Parallel Benchmark Decomposition for Sca...

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29th IEEE international parallel and distributed processing symposium (IPDPS)

作者： Popov, Mihail Akel, Chadi Conti, Florent Jalby, William Castro, Pablo de Oliveira Univ Versailles St Quentin En Yveline Exascale Comp Res Versailles France

ISBN: (纸本)9781479986484

Evaluating the strong scalability of OpenMP applications is a costly and time-consuming process. It traditionally requires executing the whole application multiple times with different number of threads. We propose the parallel Codelet Extractor and REplayer (PCERE), a tool to reduce the cost of scalability evaluation. PCERE decomposes applications into small pieces called codelets: each codelet maps to an OpenMP parallel region and can be replayed as a standalone program. To accelerate scalability prediction, PCERE replays codelets while varying the number of threads. Prediction speedup comes from two key ideas. First, the number of invocations during replay can be significantly reduced. Invocations that have the same performance are grouped together and a single representative is replayed. Second, sequential parts of the programs do not need to be replayed for each different thread configuration. PCERE codelets can be captured once and replayed accurately on multiple architectures, enabling cross-architecture parallel performance prediction. We evaluate PCERE on a C version of the NAS 3.0 parallel Benchmarks (NPB). We achieve an average speed-up of 25 x on evaluating OpenMP applications scalability with an average error of 4.9% (median error of 1.7%).

关键词： OpenMP applications program replay checkpoint restart parallel code isolation scalability prediction cross-architecture performance prediction

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Parameter Servers Placement for distributed Deep Learning in Edge computing 19

Parameter Servers Placement for Distributed Deep Learning in...

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19th IEEE international symposium on parallel and distributed processing with applications (IEEE ISPA)

作者： Yan, Jiaquan Wu, Yalan Wu, Jigang Chen, Long Guangdong Univ Technol Sch Comp Sci & Technol Guangzhou Peoples R China

ISBN: (纸本)9781665435741

the architecture based on global-local parameter servers (PSs) is an appealing framework to accelerate the training of the global model in distributed deep learning. However, the accuracy and training time are influenced by the placement strategy of PSs in distributed deep learning, due to the adopted gradient descent methods and the heterogeneous computation and communication resources. therefore, this paper formulates a novel problem for placement strategy of PSs in the dynamic available storage capacity, with the objective of minimizing the training time of the distributed deep learning under the constraints of the storage capacity and the number of local PSs. then, we prove the NP-hardness of the proposed problem. To solve the problem, a parameter servers placement algorithm, followed by an adjustment algorithm, is proposed in this paper, by continuously making decisions for placement strategy of PSs to decrease the training time of the global model. Simulation results show that, the proposed combined algorithm performs better than existing works for all cases, in terms of the training time of global model. Moreover, the performance of the proposed algorithm is nearly close to that of brute-force approach, in terms of the training time of the global model.

关键词： Edge Computing distributed Deep Learning Parameter Server Placement

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Minimizing rental cost for multiple recipe applications in the Cloud 30

Minimizing rental cost for multiple recipe applications in t...

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30th IEEE international parallel and distributed processing symposium (IPDPS)

作者： Hanna, Fouad Marchal, Loris Nicod, Jean-Marc Philippe, Laurent Rehn-Sonigo, Veronika Sabbah, Hala UFC UBFC CNRS ENSMM UTBM FEMTO ST Inst Besancon France ENS Lyon CNRS INRIA LIP Besancon France

ISBN: (纸本)9781509036820

Clouds are more and more becoming a credible alternative to parallel dedicated resources. the pay-per-use pricing policy however highlights the real cost of computing applications. this new criterion, the cost, must then be assessed when scheduling an application in addition to more traditional ones as the completion time or the execution flow. In this paper, we tackle the problem of optimizing the cost of renting computing instances to execute an application on the cloud while maintaining a desired performance (throughput). the target application is a stream application based on a DAG pattern, i.e., composed of several tasks with dependencies, and instances of the same execution task graph are continuously executed on the instances. We provide some theoretical results on the problem of optimizing the renting cost for a given throughput then propose some heuristics to solve the more complex parts of the problem, and we compare them to optimal solutions found by linear programming.

关键词： Clouds scheduling optimization DAG applications

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Towards High Performance processing of Streaming Data in Large Data Centers 30

Towards High Performance Processing of Streaming Data in Lar...

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30th IEEE international parallel and distributed processing symposium (IPDPS)

作者： Kamburugamuve, Supun Ekanayake, Saliya Pathirage, Milinda Fox, Geoffrey Indiana Univ Sch Informat & Comp Bloomington IN 47405 USA

ISBN: (纸本)9781509036820

Smart devices, mobile robots, ubiquitous sensors, and other connected devices in the Internet of things (IoT) increasingly require real-time computations beyond their hardware limits to process the events they capture. Leveraging cloud infrastructures for these computational demands is a pattern adopted in the IoT community as one solution, which has led to a class of Dynamic Data Driven applications (DDDA). these applications offload computations to the cloud through distributed Stream processing Frameworks (DSPF) such as Apache Storm. While DSPFs are efficient in computations, current implementations barely meet the strict low latency requirements of large scale DDDAs due to inefficient inter-process communication. this research implements efficient highly scalable communication algorithms and presents a comprehensive study of performance, taking into account the nature of these applications and characteristics of the cloud runtime environments. It further reduces communication costs within a node using an efficient shared memory approach. these algorithms are applicable in general to existing DSPFs and the results show significant improvements in latency over the default implementation in Apache Storm.

关键词： Storms

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A parallel Resampling Algorithm for Particle Filtering on Shared-Memory Architectures

A Parallel Resampling Algorithm for Particle Filtering on Sh...

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26th IEEE international parallel and distributed processing symposium (IPDPS) / Workshop on High Performance Data Intensive Computing

作者： Gong, Peng Basciftci, Yuksel Ozan Ozguner, Fusun Ohio State Univ Dept Elect & Comp Engn Columbus OH 43210 USA

ISBN: (纸本)9780769546766

Many real-world applications such as positioning, navigation, and target tracking for autonomous vehicles require the estimation of some time-varying states based on noisy measurements made on the system. Particle filters can be used when the system model and the measurement model are not Gaussian or linear. However, the computational complexity of particle filters prevents them from being widely adopted. parallel implementation will make particle filters more feasible for real-time applications. Effective resampling algorithms like the systematic resampling algorithm are serial. In this paper, we propose the shared-memory systematic resampling (SMSR) algorithm that is easily parallelizable on existing architectures. We verify the performance of SMSR on graphics processing units. Experimental results show that the proposed SMSR algorithm can achieve a significant speedup over the serial particle filter.

关键词： particle filters resampling algorithm shared-memory architecture parallel computing

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Dynamic Frequency Scaling for Energy Consumption Reduction in Synchronous distributed applications 12

Dynamic Frequency Scaling for Energy Consumption Reduction i...

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12th IEEE international symposium on parallel and distributed processing with applications (ISPA)

作者： Charr, Jean-Claude Couturier, Raphael Fanfakh, Ahmed Giersch, Arnaud Univ Franche Comte FEMTO ST Inst IUT Belfort Montbeliard F-90016 Belfort France

ISBN: (纸本)9781479942930

Dynamic Voltage Frequency Scaling (DVFS) can be applied to modern CPUs. this technique is usually used to reduce the energy consumed by a CPU while computing. thus, decreasing the frequency reduces the power consumed by the CPU. However, it can also significantly affect the performance of the executed program if it is compute bound and if a low CPU frequency is selected. therefore, the chosen scaling factor must give the best possible trade-off between energy reduction and performance. In this paper we present an algorithm that predicts the energy consumed with each frequency gear and selects the one that gives the best ratio between energy consumption reduction and performance. this algorithm works online without training or profiling and has a very small overhead. It also takes into account synchronous communications between the nodes that are executing the distributed algorithm. the algorithm has been evaluated over the SimGrid simulator while being applied to the NAS parallel benchmark programs. the results of the experiments show that it outperforms other existing scaling factor selection algorithms.

关键词： Benchmarktesting Degradation Energyconsumption Predictionalgorithms Programprocessors Time-frequencyanalysis Dynamicvoltagefrequencyscaling homogeneouscluster messagepassingprograms central processing units energy consumption Energy sources dynamic frequency deterioration Scale factor Energy conservation

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An Efficient Scheduling Algorithm for distributed Heterogeneous Systems with Task Duplication Allowed 19

An Efficient Scheduling Algorithm for Distributed Heterogene...

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19th IEEE international symposium on parallel and distributed processing with applications (IEEE ISPA)

作者： Shi, Hao Chen, Yixiang Xu, Jinyi East China Normal Univ Dept Embedded Software & Syst Software Engn Inst Shanghai Peoples R China MOE Engn Res Ctr Software Hardware Codesign Techn Shanghai Peoples R China

ISBN: (纸本)9781665435741

Due to the restriction of data transmission speed and bandwidth between processing units, communication delays are significantly impacting the complete time of applications in distributed heterogeneous systems. Most of the existing methods considering communication delays are approximate. the performance of solutions given by these heuristic methods are depends on the input instance. Another common choice is optimization methods, such as genetic algorithms and branch-and-bound search. However, these algorithms solve general situations with low efficiency. this paper proposes an efficient scheduling algorithm with task duplication allowed, called DHSA. Task duplication allows tasks executing multiple times to trade extra computation for communication delays, which is widely used in parallel system scheduling approximately. To address the complexity explosion caused by allowing all-task duplication, we decompose the scheduling into allocation and ordering stages and optimize them separately with solving optimization model and slack-interval aware ordering approach. Extensive experiments show that DHSA gives more optimal solutions within feasible time comparing to two state-of-the-art methods, particularly for applications with medium to high communication load.

关键词： Duplication-based Scheduling distributed Heterogeneous Systems Optimization Model Slack-interval Aware Ordering

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Weaver: Efficient Coflow Scheduling in Heterogeneous parallel Networks 34

Weaver: Efficient Coflow Scheduling in Heterogeneous Paralle...

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34th IEEE international parallel and distributed processing symposium (IPDPS)

作者： Huang, Xin Sunny Xia, Yiting Ng, T. S. Eugene Rice Univ Houston TX 77251 USA Facebook Inc Cambridge MA USA

ISBN: (纸本)9781728168760

Leveraging application-level requirements expressed in Coflows has been shown to improve application-level communication efficiency. However, most existing works assume all application traffic is serviced by one monolithic network. this over-simplified assumption is no longer sufficient in a modern, evolving data center which operates on multiple generations of network fabrics, an architecture that we define as Heterogeneous parallel Networks (HPNs). In this paper, we present the first scheduler, called Weaver, that addresses the Coflow management problem in HPNs. To exploit HPNs fully, achieving high communication efficiency for applications is crucial, yet it is also challenging because of the complex traffic patterns in applications and the heterogeneous bandwidth distribution in HPNs. Weaver addresses these challenges at two levels. At the microscopic level, for each application, Weaver leverages an efficient algorithm to exploit the distributed bandwidth in HPNs, which we proved to be within a constant factor of the optimal. At the macroscopic level involving multiple applications, Weaver can adopt a range of application traffic scheduling policies as desired by the system operator. Under realistic traffic, Weaver enables HPNs to service Coflows as efficiently as a monolithic network with equivalent aggregated capacity.

关键词： Efficiency

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DAG Scheduling Considering parallel Execution for High-Load processing on Clustered Many-core Processors 26

DAG Scheduling Considering Parallel Execution for High-Load ...

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26th IEEE/ACM international symposium on distributed Simulation and Real Time applications (DS-RT)

作者： Okamura, Ryo Azumi, Takuya Saitama Univ Grad Sch Sci & Engn Saitama Japan

ISBN: (数字)9781665497992

ISBN: (纸本)9781665497992

In recent years, high computational power has been required for computer platforms to support complex systems such as self-driving systems. Clustered many-core processors and directed acyclic graphs (DAGs), which can represent dependencies and parallelism of task processing, have attracted much attention as solutions to this problem. Previous studies on scheduling DAGs on multi-core processors have attempted to reduce the makespan (i.e., time it takes for a task to complete) by increasing the number of processes that can be executed in parallel. However, in self-driving systems, such as those utilizing clustered many-core processors, it is impossible to sufficiently increase the utilization of processor cores due to high-load processing. In this paper, a scheduling method is proposed to improve the utilization of processor cores by parallel executing high-load processes in parallel across multiple cores. the proposed method can reduce the makespan of DAGs performing high-load processing on clustered many-core processors.

关键词： clustered many-core processors DAG work-conserving schedule list scheduling

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parallel Subgraph Counting for Multicore Architectures 12

Parallel Subgraph Counting for Multicore Architectures

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12th IEEE international symposium on parallel and distributed processing with applications (ISPA)

作者： Aparicio, David Ribeiro, Pedro Silva, Fernando Univ Porto CRACS P-4169007 Oporto Portugal Univ Porto INESC TEC LA Fac Ciencias P-4169007 Oporto Portugal

ISBN: (纸本)9781479942930

Computing the frequency of small subgraphs on a large network is a computationally hard task. this is, however, an important graph mining primitive, with several applications, and here we present a novel multicore parallel algorithm for this task. At the core of our methodology lies a state-of-the-art data structure, the g-trie, which represents a collection of subgraphs and allows for a very efficient sequential search. Our implementation was done using Pthreads and can run on any multicore personal computer. We employ a diagonal work sharing strategy to dynamically and effectively divide work among threads during the execution. We assess the performance of our Pthreads implementation on a set of representative networks from various domains and with diverse topological features. For most networks, we obtain a speedup of over 50 for 64 cores and an almost linear speedup up to 32 cores, showcasing the flexibility and scalability of our algorithm. this paves the way for the usage of such counting algorithms on larger subgraph and network sizes without the obligatory access to a cluster.

关键词： parallel Algorithms Adaptive Load Balancing Complex Networks Graph Mining G-Tries

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