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

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New challenges in parallel optimization

New challenges in parallel optimization

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4th international symposium on parallel and distributed Computing (ISPDC 2005)

作者： Alba, E Univ Malaga E-29071 Malaga Spain

ISBN: (纸本)0769524346

parallelism and Optimization are two disciplines that are used together in numerous applications. Solving complex problems in optimization often means to face complex search landscapes, what needs time-consuming operations. Exact and heuristic techniques are being used nowadays to get solutions to problems in mathematics, logistics, bioinformatics, telecommunications, and many other relevant fields. For these tasks it is mandatory to deal with cluster computing in many cases, multiprocessors, and even with computational grids. In this talk I will address the basic challenges of using parallel tools, software, and hardware for extending existing optimization procedures to work in a parallel environment. I will present some basic optimization algorithms, especially heuristic ones, and discuss the application of parallelism to them. Also, I will show how new techniques become possible due to parallelism, giving birth to a whole new class of algorithms and new research lines. © 2005 IEEE.

关键词： Optimization

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mpisee: MPI Profiling for Communication and Communicator Structure 36

mpisee: MPI Profiling for Communication and Communicator Str...

引用

36th IEEE international parallel and distributed processing symposium (IEEE IPDPS)

作者： Vardas, Ioannis Hunold, Sascha Ajanohoun, Jordy, I Traeff, Jesper Larsson TU Wien Res Grp Parallel Comp Fac Informat Vienna Austria

ISBN: (纸本)9781665497473

Cumulative performance profiling is a fast and lightweight method for gaining summary information about where and how communication time in parallel MPI applications is spent. MPI provides mechanisms for implementing such profilers that can be transparently used with applications. Existing profilers typically profile on a process basis and record the frequency, total time, and volume of MPI operations per process. this can lead to grossly misleading cumulative information for applications that make use of MPI features for partitioning the processes into different communicators. We present a novel MPI profiler, mpisee, for communicator-centric profiling that separates and records collective and pointto-point communication information per communicator in the application. We discuss the implementation of mpisee which makes significant use of the MPI attribute mechanism. We evaluate our tool by measuring its overhead and profiling a number of standard applications. Our measurements with thirteen MPI applications show that the overhead of mpisee is less than 3 %. Moreover, using mpisee, we investigate in detail two particular MPI applications, SPLATT and GROMACS, to obtain information on the various MPI operations for the different communicators of these applications. Such information is not available by other, state-of-the-art profilers. We use the communicator-centric information to improve the performance of SPLATT resulting in a significant runtime decrease when run with 1024 processes.

关键词： Time-frequency analysis distributed processing Runtime Scalability Conferences Benchmark testing Topology

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An Immune-based Optimization Algorithm of Multi-tenant Resource Allocation for Geo-distributed Data Centers 15

An Immune-based Optimization Algorithm of Multi-tenant Resou...

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： Song, Yazhen Peng, Jun Liu, Weirong Zhang, Xiaoyong Gu, Xin Yu, Wentao Cent S Univ Sch Informat Sci & Engn Changsha Hunan Peoples R China Cent South Univ Forestry & Technol Coll Comp & Informat Engn Changsha Hunan Peoples R China Hunan Engn Lab Rail Vehicles Braking Technol Changsha Hunan Peoples R China

ISBN: (纸本)9781538637906

In recent years, the global data center industry has entered a large-scale planning and construction stage, bringing huge power consumption and high operating costs. In this paper, a multi-tenant resource allocation optimization model for geo-distributed data centers is proposed to reduce the energy consumption and operating costs of cloud providers. Firstly, the resource allocation model is constructed by considering the heterogeneous electricity prices of geo-distributed data centers. then an immune-based algorithm is proposed to solve the allocation optimization problem with variable users' demand. the biological immune system is a highly parallel, distributed, self-adaptive and self-organizing system with a strong ability to learn, recognize, and memory. It searches for the optimal resource allocation scheme with low computational complexity. Extensive simulations driven by large-scale parallel Workloads Archive demonstrate the feasibility and performance of our immune-based optimization algorithm.

关键词： Multi-tenant geo-distributed data centers electricity price heterogeneity immune algorithm

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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...

引用

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 Memory Saving Mechanism Based on Data Transferring for Pipeline parallelism 19

A Memory Saving Mechanism Based on Data Transferring for Pip...

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

作者： Jiang, Wei Xu, Rui Ma, Sheng Wang, Qiong Hou, Xiang Lu, Hongyi Natl Univ Def Technol Sci & Technol Parallel & Distributed Proc Lab Coll Comp Changsha Peoples R China

ISBN: (纸本)9781665435741

the correctness and robustness of the neural network model are usually proportional to its depth and width. Currently, the neural network models become deeper and wider to cope with complex applications, which leads to high memory capacity requirement and computer capacity requirements of the training process. the multi-accelerator parallelism is a promising choice for the two challenges, which deploys multiple accelerators in parallel for training neural networks. Among them, the pipeline parallel scheme has a great advantage in training speed, but its memory capacity requirements are relatively higher than other parallel schemes. Aiming at solving this challenge of pipeline parallel scheme, we propose a data transfer mechanism, which effectively reduces the peak memory usage of the training process by real-time data transferring. In the experiment, we implement our design and apply it to Pipedream, a mature pipeline parallel scheme. the memory requirement of training process is reduced by up to 48.5%, and the speed loss is kept within a reasonable range.

关键词： multi-accelerator parallelism pipeline parallel scheme data transferring memory usage

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Jobcast - parallel and distributed processing framework Data processing on a cloud style KVS database

Jobcast - Parallel and distributed processing framework Data...

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12th IEEE/IPSJ international symposium on applications and the Internet (SAINT) / IEEE Annual Signature Conference on Computer Software and applications (COMPSAC)

作者： Nakagawa, Ikuo Nagami, Kenichi Intec Inc Adv Technol Res & Dev Inst Tokyo Japan

ISBN: (纸本)9780769547374;9781467320016

In this paper, we propose a new architecture for parallel and distributed processing framework, "Jobcast", which enables data processing on a cloud style KVS database. Nowadays, lots of KVS (as known as Key Value Store) systems exist which achieve high scalability for data spaces among a huge number of computers. Some of KVS implementations use "consistent hash" algorithm to identify the backend data node to store a pair of key and value. Jobcast also uses consistent hash algorithm for a distribution strategy and has a capability to store key and value pairs into huge number of computers as a KVS system. Furthermore, Jobcast also distributes "jobs" into data nodes for parallel and distributed processing. In this paper, we introduce a basic architecture of Jobcast and evaluate a data processing performance for a typical example.

关键词： component parallel and distributed processing cloud database NoSQL KVS

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MPIXternal: A Library for a Portable Adjustment of parallel MPI applications to Heterogeneous Environments

MPIXternal: A Library for a Portable Adjustment of Parallel ...

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23rd IEEE international parallel and distributed processing symposium

作者： Clauss, Carsten Lankes, Stefan Bemmerl, thomas Rhein Westfal TH Aachen Chair Operating Syst D-52056 Aachen Germany

ISBN: (纸本)9781424437511

Nowadays, common systems in the area of high performance computing exhibit highly hierarchical architectures. As a result, achieving satisfactory;application performance demands an adaptation of the respective parallel algorithm to such systems. this, in turn, requires knowledge about the actual hardware structure even at the application level. However, the prevalent Message Passing Interface (MPI) standard (at least in its current version 2.1) intentionally hides heterogeneity from the application programmer in order to assure portability In this paper, we introduce the MPIXternal library which tries to Circumvent this obvious semantic gap within the current MPI standard. For this pur pose, the library offers the programmer additional features that should help to adapt applications to today's hierarchical systems in a convenient and portable way.

关键词： Hierarchical systems

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Using COTS software in high assurance control applications 4

Using COTS software in high assurance control applications

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4th IEEE international symposium on High-Assurance Systems Engineering, HASE 1999

作者： Sha, Lui UIUC United States

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A Study of Graph Analytics for Massive Datasets on distributed Multi-GPUs 34

A Study of Graph Analytics for Massive Datasets on Distribut...

引用

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

作者： Jatala, Vishwesh Dathathri, Roshan Gill, Gurbinder Hoang, Loc Nandivada, V. Krishna Pingali, Keshav Univ Texas Austin Austin TX 78712 USA IIT Madras Chennai Tamil Nadu India

ISBN: (纸本)9781728168760

there are relatively few studies of distributed GPU graph analytics systems in the literature and they are limited in scope since they deal with small data-sets, consider only a few applications, and do not consider the interplay between partitioning policies and optimizations for computation and communication. In this paper, we present the first detailed analysis of graph analytics applications for massive real-world datasets on a distributed multi-GPU platform and the first analysis of strong scaling of smaller real-world datasets. We use D-IrGL, the state-of-the-art distributed GPU graph analytical framework, in our study. Our evaluation shows that (1) the Cartesian vertex-cut partitioning policy is critical to scale computation out on GPUs even at a small scale, (2) static load imbalance is a key factor in performance since memory is limited on GPUs, (3) device-host communication is a significant portion of execution time and should be optimized to gain performance, and (4) asynchronous execution is not always better than bulk-synchronous execution.

关键词： Graphics processing Units Graph processing distributed Systems Performance

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BigKernel - High Performance CPU-GPU Communication Pipelining for Big Data-style applications

BigKernel - High Performance CPU-GPU Communication Pipelinin...

引用

IEEE 28th international parallel & distributed processing symposium (IPDPS)

作者： Mokhtari, Reza Stumm, Michael Univ Toronto Dept Elect & Comp Engn Toronto ON Canada

ISBN: (纸本)9780769552071

GPUs offer an order of magnitude higher compute power and memory bandwidth than CPUs. GPUs therefore might appear to be well suited to accelerate computations that operate on voluminous data sets in independent ways;e.g., for transformations, filtering, aggregation, partitioning or other "Big Data" style processing. Yet experience indicates that it is difficult, and often error-prone, to write GPGPU programs which efficiently process data that does not fit in GPU memory, partly because of the intricacies of GPU hardware architecture and programming models, and partly because of the limited bandwidth available between GPUs and CPUs. In this paper, we propose BigKernel, a scheme that provides pseudo-virtual memory to GPU applications and is implemented using a 4-stage pipeline with automated prefetching to (i) optimize CPU-GPU communication and (ii) optimize GPU memory accesses. BigKernel simplifies the programming model by allowing programmers to write kernels using arbitrarily large data structures that can be partitioned into segments where each segment is operated on independently;these kernels are transformed into BigKernel using straight-forward compiler transformations. Our evaluation on six data-intensive benchmarks shows that BigKernel achieves an average speedup of 1.7 over state-of-the-art double-buffering techniques and an average speedup of 3.0 over corresponding multi-threaded CPU implementations.

关键词： GPU CPU communication management optimization stream processing

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