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检索条件"任意字段=13th IEEE International Symposium on Parallel and Distributed Processing with Applications"

共 2573 条记录，以下是141-150 订阅

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parallel Protein Identification Using an FPGA-Based Solution 30

Parallel Protein Identification Using an FPGA-Based Solution

引用

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

作者： Casasopra, Fabiola Bianchi, Gea Durelli, Gianluca C. Santambrogio, Marco D. Politecn Milan Dipartimento Elett Informaz & Bioingn Milan Italy

ISBN: (纸本)9781509036820

the ability to rapidly identify a given protein from small subsamples (i.e. peptides) is at the basis of fundamental applications in the medical field. At the basis of protein identification we have a string matching problem which is computational intensive if we consider that the complexity of the algorithm scales with the length of the string and the number of sweeps of the database that are needed. In this paper we present an improvement for the FPGA-based string matching solution available in the literature improving the amount of parallelism exploited by the solution achieving a 1.63x reduction of the energy needed for the task over the literature and a 5.75x reduction when compared with high-end workstation.

关键词： Field programmable gate arrays (FPGA)

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Rethinking Energy-Efficiency of Heterogeneous Computing for CNN-Based Mobile applications 15

Rethinking Energy-Efficiency of Heterogeneous Computing for ...

引用

15th ieee international symposium on parallel and distributed processing with applications (ISPA) / 16th ieee international Conference on Ubiquitous Computing and Communications (IUCC)

作者： Wang, Zhen Li, Xi Wang, Chao Cheng, Zhinan Song, Jiachen Zhou, Xuehai Univ Sci & Technol China Sch Comp Sci & Technol Hefei 230027 Anhui Peoples R China

ISBN: (纸本)9781538637906

Convolutional Neural Networks (CNNs) have become more and more powerful in the computer vision domain, as they achieve the state-of-the-art accuracy. Despite this, it is generally difficult to apply CNNs on mobile platforms. Client server paradigm is a straightforward way to deploy CNNs on mobile phones, but studies have shown that it suffers serious problems, such as privacy leaks. Recently, researchers focus on using heterogeneous local processors (e.g., GPUs, CPUs) to accelerate the inference of CNNs. Utilizing all local processors available can achieve the highest performance, but it might incur energy-inefficiency. Different from previous works, this paper concerns more about energy-efficiency of CNN based mobile applications. We present an adaptive strategy, which is able to compute the energy-efficiency of all local processors, and further to obtain the energy-efficient device processor combination to perform CNN inference in parallel. the strategy is implemented on ODROID platform, where the evaluation results show that our proposed approach provides 3.67 x higher energy-efficiency with only 9.7% performance degradation on average compared with the greedy strategy which tries to use all local processors available.

关键词： distributed processing Conferences Ubiquitous computing

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HDSVM: A High Efficiency distributed SVM Framework over Data Stream 15

HDSVM: A High Efficiency Distributed SVM Framework over Data...

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15th ieee international symposium on parallel and distributed processing with applications (ISPA) / 16th ieee international Conference on Ubiquitous Computing and Communications (IUCC)

作者： Hou, Yan Wang, Yijie Ma, Xingkong Cheng, Li Natl Univ Def Technol Coll Comp Natl Lab Parallel & Distributed Proc Changsha 410073 Hunan Peoples R China

ISBN: (纸本)9781538637906

the application of Support Vector Machine (SVM) over data stream is growing with the increasing real-time processing requirements in classification field, like anomaly detection and real-time image processing. However, the dynamic live data with high volume and fast arrival rate in data streams make it challenging to apply SVM in data stream processing. Existing SVM implementations are mostly designed for batch processing and hardly satisfy the efficiency requirement of stream processing for its inherent complexity. To address the challenges, we propose a high efficiency distributed SVM framework over data stream (HDSVM), which consists of two main algorithms, incremental learning algorithm and distributed algorithm. Firstly, we propose a partial support vectors reserving incremental learning algorithm (PSVIL). By selecting a subset of support vectors based on their distances to classification hyperplane instead of the universal set to update SVM, the algorithm achieves lower time overhead while ensuring accuracy. Secondly, we propose a distribution remaining partition and fast aggregation distributed algorithm (DRPFA) for SVM. the real-time data is partitioned based on the original distribution with clustering instead of random partition, and historical support vectors are partitioned based on their distances to the classification hyperplane. the global hyperplane can be obtained by averaging the parameters of local hyperplanes due to the above partition strategy. Extensive experiments on Apache Storm show that the proposed HDSVM achieve lower time overhead and similar accuracy compared with the state-of-art. Speed-up ratio is increased by 2-8 times within 1% accuracy deviation.

关键词： distributed SVM Incremental learning Data stream Support vector selection Distance distribution

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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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Using Intrinsic Performance Counters to Assess Efficiency in Task-based parallel applications 30

Using Intrinsic Performance Counters to Assess Efficiency in...

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

作者： Grubel, Patricia Kaiser, Hartmut Huck, Kevin Cook, Jeanine New Mexico State Univ Las Cruces NM 88003 USA Louisiana State Univ Ctr Computat & Technol Baton Rouge LA 70803 USA Univ Oregon Eugene OR 97403 USA Sandia Natl Labs POB 5800 Albuquerque NM 87185 USA

ISBN: (纸本)9781509036820

the ability to measure performance characteristics of an application at runtime is essential for monitoring the behavior of the application and the runtime system on the underlying architecture. Traditional performance measurement tools do not adequately provide measurements of asynchronous task-based parallel applications, either in real-time or for postmortem analysis. We propose that this capability is best performed directly by the runtime system for ease in use and to minimize conflicts and overheads potentially caused by traditional measurement tools. In this paper, we describe and illustrate the use of the performance monitoring capabilities in the HPX [13] runtime system. We describe and detail existing performance counters made available through HPX's performance counter framework and demonstrate how they are useful to understanding application efficiency and resource usage at runtime. this extensive framework provides the ability to asynchronously query software and hardware counters and could potentially be used as the basis for runtime adaptive resource decisions. We demonstrate the ease of porting the Inncabs benchmark suite to the HPX runtime system, the improved performance of benchmarks that employ fine-grained task parallelism when ported to HPX, and the capabilities and advantages of using the in-situ performance monitoring system in HPX to give detailed insight to the performance and behavior of the benchmarks and the runtime system.

关键词： runtime instrumentation performance counters execution monitoring HPX task-based parallelism many asynchronous tasks

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A Reliable Soil Moisture Sensing Methodology for Agricultural Irrigation 15

A Reliable Soil Moisture Sensing Methodology for Agricultura...

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15th ieee international symposium on parallel and distributed processing with applications (ISPA) / 16th ieee international Conference on Ubiquitous Computing and Communications (IUCC)

作者： Yang, Yanxiang Sun, Lijia Hu, Jiang Porter, Dana Marek, thomas Hillyer, Charles Texas A&M Univ Dept Elect & Comp Engn College Stn TX 77843 USA Texas A&M Univ Dept Biol & Agr Engn College Stn TX 77843 USA Texas A&M AgriLife Res 6500 Amarillo Blvd W Amarillo TX 79106 USA

ISBN: (纸本)9781538637906

Agricultural irrigation accounts for two-thirds of global fresh water use and plays a pivotal role in addressing the challenge of water shortage. Soil moisture sensors can improve the water use efficiency of irrigation and this work is a study on sensor deployment and fault detection in the context of agricultural irrigation. Previous works are mostly oriented toward general applications and focused on optimizing complicated sensor networks. In agricultural irrigation, the sensor network is relatively simple, while the expectation to system reliability is quite high. In this work, a reliability-driven soil moisture sensing methodology is developed and discussed. It includes a genetic algorithm based optimization technique and a fault detection technique. Simulations are performed to compare the proposed methodology with previous works. the results indicate that the proposed methodology considerably improves system reliability in term of mean time to failure.

关键词： Irrigation Soil moisture Reliability Fault detection Wireless sensor networks Sensor systems

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Network resource control for data intensive applications in heterogeneous infrastructures

Network resource control for data intensive applications in ...

引用

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

作者： Zhao, Zhiming Dumitru, Cosmin Grosso, Paola de Laat, Cees Univ Amsterdam Inst Informat Syst & Network Engn Res Grp NL-1098 XH Amsterdam Netherlands

ISBN: (纸本)9780769546766

Many scientific workflow applications are data intensive. Large quantities of simulation generated data or data collected from sensors or instruments drive the process flow of the applications, and the processing of the data is commonly done at a different location from where the data is stored. Advanced networks such as hybrid networks make it feasible for high level applications to request network paths and service provisioning. However, current applications tune the execution quality rarely considering network resources, and by selecting only optimal software services and computing resources. Including network services in the resource scheduling adds an extra dimension workflow applications to optimize the runtime performance but also introduces new challenges, in particular in heterogeneous infrastructures. In this paper we continue our previous work on a system called NEtWork QoS Planner (NEWQoSPlanner) which is able to select network resources in the context of workflow composition and scheduling. We will discuss how NEWQoSPlanner invokes network services to achieve dynamic resource optimization for workflows, and how to apply such planner in heterogeneous infrastructures.

关键词： Advanced network scientific workflow QoS resource planning

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Scaling Data-Intensive applications on Heterogeneous Platforms with Accelerators

Scaling Data-Intensive Applications on Heterogeneous Platfor...

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

作者： Balevic, Ana Kienhuis, Bart Leiden Inst Adv Comp Sci NL-2333 CA Leiden Netherlands

ISBN: (纸本)9780769546766

Heterogeneous parallel systems including accelerators such as Graphics processing Units (GPUs), are expected to play a major role in architecting the largest systems in the world, as well as the most powerful embedded devices. Impressive computational speedups have been reported for numerous algorithms in fields of medical image processing, digital signal processing, astrophysics, modeling and simulations. However, it is frequently assumed that the working data set of the application fits in the memory of the accelerator. In this paper, first we elevate this constraint by presenting a simple and scalable compile-time approach for processing large data sets based on I/O tiling. Second, we combine tiling with streaming in our asynchronous execution model, which enables efficient data-driven processing of large data sets on heterogeneous platforms with accelerators. Finally, we present results for several microbenchmarks and three data parallel kernels.

关键词： Accelerators Tile Dataset big data Platform

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Dynamic Acceleration of parallel applications in Cloud Platforms by Adaptive Time-Slice Control 30

Dynamic Acceleration of Parallel Applications in Cloud Platf...

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

作者： Wu, Song Xie, Zhenjiang Chen, Haibao Di, Sheng Zhao, Xinyu Jin, Hai Huazhong Univ Sci & Technol Sch Comp Sci & Technol Cluster & Grid Comp Lab Serv Comp Technol & Syst Lab Wuhan 430074 Peoples R China Argonne Natl Lab Argonne IL 60439 USA

ISBN: (纸本)9781509021406

Tightly-coupled parallel applications in cloud systems may suffer from significant performance degradation because of the resource over-commitment issue. In this paper, we propose a dynamic approach based on the adaptive control over time-slice for virtual clusters, in order to mitigate the performance degradation for parallel applications in cloud and avoid the negative impact effectively on other non-parallel applications meanwhile. the key idea is to reduce the synchronization overhead inside and across virtual machines (VMs) in cloud systems, by dynamically adjusting the time-slices of VMs in terms of the spinlock latency at runtime. Such a design is motivated by our experimental finding that VM's time slice is a key factor determining the synchronization overhead as well as the parallel execution performance. We perform the evaluation on a real cluster environment deployed with XEN, using five well-known benchmarks with 10+ applications. Experiments show that our approach obtains 1.5-10x performance gain for running parallel applications, than other state-of-the-art solutions (including Credit Scheduling of Xen and the well-known methods like Co-Scheduling and Balance Scheduling), with nearly unaffected impact on the performance of non-parallel applications.

关键词： Benchmarking

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A High-throughput and Low-Latency parallelization of Window-based Stream Joins on Multicores 12

A High-Throughput and Low-Latency Parallelization of Window-...

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

作者： Buono, Daniele De Matteis, Tiziano Mencagli, Gabriele Univ Pisa Dept Comp Sci I-56127 Pisa Italy

ISBN: (纸本)9781479942930

Data Stream processing (DaSP) is a paradigm characterized by on-line (often real-time) applications working on unlimited data streams whose elements must be processed efficiently "on the fly". DaSP computations are characterized by data-flow graphs of operators connected via streams and working on the received elements according to high throughput and low latency requirements. To achieve these constraints, high-performance DaSP operators requires advanced parallelism models, as well related design and implementation techniques targeting multi-core architectures. In this paper we focus on the parallelization of the window-based stream join, an important operator that raises challenging issues in terms of parallel windows management. We review the state-of-the-art solutions about the stream join parallelization and we propose our novel parallel strategy and its implementation on multicores. As demonstrated by experimental results, our parallel solution introduces two important advantages with respect to the existing solutions: (i) it features an high-degree of configurability in order to address the symmetricity/ asymmetricity of input streams (in terms of their arrival rate and window length);(ii) our parallelization provides a high throughput and it is definitely better than the compared solutions in terms of latency, providing an efficient way to perform stream joins on latency-sensible applications.

关键词： Data Stream processing Window-based Stream Joins Continuous Queries throughput Latency Multicores

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