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检索条件"主题词=Data-Intensive Computing"

共 139 条记录，以下是91-100 订阅

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Optimizing Load Balancing and data-Locality with data-aware Scheduling 2

Optimizing Load Balancing and Data-Locality with Data-aware ...

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IEEE International Conference on Big data

作者： Wang, Ke Zhou, Xiaobing Li, Tonglin Zhao, Dongfang Lang, Michael Raicu, Ioan IIT Chicago IL 60616 USA Hortonworks Inc Santa Clara CA USA Los Alamos Natl Lab Los Alamos NM USA Argonne Natl Lab Argonne IL 60439 USA

ISBN: (纸本)9781479956661

Load balancing techniques (e.g. work stealing) are important to obtain the best performance for distributed task scheduling systems that have multiple schedulers making scheduling decisions. In work stealing, tasks are randomly migrated from heavy-loaded schedulers to idle ones. However, for data-intensive applications where tasks are dependent and task execution involves processing a large amount of data, migrating tasks blindly yields poor data-locality and incurs significant data-transferring overhead. This work improves work stealing by using both dedicated and shared queues. Tasks are organized in queues based on task data size and location. We implement our technique in MATRIX, a distributed task scheduler for many-task computing. We leverage distributed key-value store to organize and scale the task metadata, task dependency, and data-locality. We evaluate the improved work stealing technique with both applications and micro-benchmarks structured as direct acyclic graphs. Results show that the proposed data-aware work stealing technique performs well.

关键词： data-intensive computing data-aware scheduling work stealing key-value stores many-task computing

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SODA: A Semantics-Aware Optimization Framework for data-intensive Applications Using Hybrid Program Analysis 14

SODA: A Semantics-Aware Optimization Framework for Data-Inte...

引用

IEEE 14th International Conference on Cloud computing (CLOUD)

作者： Rao, Bingbing Liu, Zixia Zhang, Hong Lu, Siyang Wang, Liqiang Univ Cent Florida Dept Comp Sci Orlando FL 32816 USA Hebei Univ Sch Cyber Secur & Comp Baoding Heibei Peoples R China Beijing Jiaotong Univ Sch Comp & Informat Technol Beijing Peoples R China

ISBN: (纸本)9781665400602

In the era of data explosion, a growing number of data-intensive computing frameworks, such as Apache Hadoop and Spark, have been proposed to handle the massive volume of unstructured data in parallel. Since programming models provided by these frameworks allow users to specify complex and diversified user-defined functions (UDFs) with predefined operations, the grand challenge of tuning up entire system performance arises if programmers do not fully understand the semantics of code, data, and runtime systems. In this paper, we design a holistic semantics-aware optimization for data-intensive applications using hybrid program analysis (SODA) to assist programmers to tune performance issues. SODA is a two-phase framework: the offline phase is a static analysis that analyzes code and performance profiling data from the online phase of prior executions to generate a parameterized and instrumented application;the online phase is a dynamic analysis that keeps track of the application's execution and collects runtime information of data and system. Extensive experimental results on lour realworld Spark applications show that SODA can gain up to 60%, 10%, 8%, faster than its original implementation, with the three proposed optimization strategies, i.e., cache management, operation reordering, and element pruning, respectively.

关键词： data-intensive computing program analysis semantics-aware cache management

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File Clustering Based Replication Algorithm in a Grid Environment 09

File Clustering Based Replication Algorithm in a Grid Enviro...

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9th IEEE/ACM International Symposium on Cluster computing and the Grid

作者： Sato, Hitoshi Matsuoka, Satoshi Endo, Toshio Tokyo Inst Technol Tokyo Japan

ISBN: (纸本)9781424439355

Replication in grid file systems can significantly improve I/O performance of data-intensive applications. However most of existing replication techniques apply to individual files, which may introduce inefficient replication overheads for a large number of files. We propose a file clustering based replication algorithm for grid file systems. Our algorithm groups files according to a relationship of simultaneous accesses between files and stores replicas of the clustered files into storage nodes, to satisfy expected most of future read access times to the clustered files and replication times for individual files being minimized under the given storage capacity limitation. Our experiments on a given grid environment, 20 nodes of 5 sites, suggest that the proposed algorithm achieves accurate file clustering and efficient replica management;our clustering policy with the file cluster size limit of 5120 MB and the storage capacity limit for replicas of 10240 MB exhibits 1.58 times efficiency than the policy that never groups related files. The results also indicate that the overheads required for introducing our algorithm significantly affect I/O performance of running applications.

关键词： data-intensive computing

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Novelties in Teaching High Performance computing 29

Novelties in Teaching High Performance Computing

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29th IEEE International Parallel and Distributed Processing Symposium (IPDPS)

作者： Shamsi, Jawwad A. Durrani, Nauman Kafi, Nadeem FAST Natl Univ Comp & Emerging Sci Syst Res Lab Comp Sci Karachi Pakistan

ISBN: (纸本)9781467376846

High Performance computing (HPC) involves solving intensive problems beyond desktop computing. Teaching HPC to undergraduate students requires special consideration of taking students comprehension to clusters of parallel machines - which can solve intensive tasks. In addition, the concepts of HPC should be well-aided with programming examples and assignments, in order make them understand the concepts, clearly. This paper focuses on the aspect of teaching HPC curriculum with a practical approach. The HPC course described in this paper, utilizes development and solving programming assignments on multiple HPC platforms in order to teach different paradigms of HPC. These include data-intensive computing, CPU-intensive computing, shared-memory, and message passing. The course also focuses on developing problem solving skills among students by aiding lecture contents with programming examples. In addition, the course focuses on maintaining effective interaction between the students and the instructor in order to develop their motivation and include topics of their interest. Evaluations revealed increased popularity of the course and improved student comprehension.

关键词： component Computer Science Education High Performance computing Parallel computing data-intensive computing CPU-intensive computing

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Concurrent Dynamic Memory Coalescing on GoblinCore-64 Architecture 16

Concurrent Dynamic Memory Coalescing on GoblinCore-64 Archit...

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International Symposium on Memory Systems (MEMSYS)

作者： Wang, Xi Leidel, John D. Chen, Yong Texas Tech Univ Whitacre Coll Engn Dept Comp Sci Box 43104 Lubbock TX 79409 USA

ISBN: (纸本)9781450343053

The majority of modern microprocessors are architected to utilize multi-level data caches as a primary optimization to reduce the latency and increase the perceived bandwidth from an application. The spatial and temporal locality provided by data caches work well in conjunction with applications that access memory in a linear fashion. However, applications that exhibit random or non-deterministic memory access patterns often induce a significant number of data cache misses, thus reducing the natural performance benefit from the data cache. In response to the performance penalties inherently present with non-deterministic applications, we have constructed a unique memory hierarchy within the GoblinCore-64 (GC64) architecture explicitly designed to exploit memory performance from irregular memory access patterns. The GC64 architecture combines a RISC-V-based core coupled with latency-hiding architectural features to a memory hierarchy with Hybrid Memory Cube (HMC) devices. In order to cope with the inherent non-determinism of applications and to exploit the packetized interface presented by the HMC device, we develop a methodology and associated implementation of a dynamic memory coalescing unit for the GC64 memory hierarchy that permits us to statistically sample memory requests from non-deterministic applications and coalesce them into the largest possible HMC payload requests. In this work, we present two parallel methodologies and associated implementations for coalescing non-deterministic memory requests into the largest potential HMC request by constructing a binary tree representation of the live memory requests from disparate cores. We present the coalesced HMC memory request results from applications that exhibit linear and non-linear memory request patterns compiled for a RISC-V core in contrast with a traditional memory hierarchy.

关键词： Memory Coalescing Microcode GoblinCore-64 RISC-V Parallel computing data-intensive computing

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PAC: Paged Adaptive Coalescer for 3D-Stacked Memory 20

PAC: Paged Adaptive Coalescer for 3D-Stacked Memory

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29th International Conference on High-Performance Parallel and Distributed computing (HPDC)

作者： Wang, Xi Leidel, John D. Williams, Brody Chen, Yong Texas Tech Univ Lubbock TX 79409 USA Tactical Comp Labs Muenster TX 76252 USA

ISBN: (纸本)9781450370523

Many contemporary data-intensive applications exhibit irregular and highly concurrent memory access patterns and thus challenge the performance of conventional memory systems. Driven by an expanding need for high-bandwidth memory featuring low access latency, 3D-stacked memory devices, such as the Hybrid Memory Cube (HMC) and High Bandwidth Memory (HBM), were designed to provide significantly higher throughput as compared to standard JEDEC DDR devices. However, existing memory interfaces and coalescing models, designed for conventional DDR devices, are unable to fully exploit the bandwidth potential inherent in these new 3D-stacked memory devices. In order to remedy this disparity, we introduce in this work a novel paged adaptive coalescer (PAC) infrastructure with a scalable coalescing network for 3D-stacked memory. We present the design and simulated implementation of this approach on RISC-V embedded cores with attached HMC devices. We have carried out extensive evaluations and the results show that the proposed PAC methodology yields an average coalescing efficiency of 56.01%. Further, our evaluation results also show that the PAC reduces bank conflicts and the power consumption by 85.16% and 59.21%, respectively. Overall, PAC achieves an average performance gain of 14.35% (and up to 26.06%) across 14 test suites. These results showcase the potential of the PAC methodology as applied to architecture design for increasingly critical data-intensive algorithms and applications.

关键词： 3D-stacked memory Memory coalescing data-intensive computing

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Efficient data Redistribution to Speedup Big data Analytics in Large Systems 23

Efficient Data Redistribution to Speedup Big Data Analytics ...

引用

23rd IEEE International Conference on High Performance computing (HiPC)

作者： Cheng, Long Li, Tao Eindhoven Univ Technol Dept Math & Comp Sci Eindhoven Netherlands Tech Univ Dresden Fac Comp Sci Dresden Germany

ISBN: (纸本)9781509054114

The performance of parallel data analytics systems becomes increasingly important with the rise of Big data. An essential operation in such environment is parallel join, which always incurs significant cost on network communication. State-of-the-art approaches have achieved performance improvements over conventional implementations through minimizing network traffic or communication time. However, these approaches still face performance issues in the presence of big data and/or large-scale systems, due to their heavy overhead of data redistribution scheduling. In this paper, we propose near-join, a network-aware redistribution approach targeting to efficiently reduce both network traffic and communication time of join executions. Particularly, near-join is lightweight and adaptable to processing large datasets over large systems. We present the details of our algorithm and its implementation. The experiments performed on a cluster of up to 400 nodes and datasets of about 100GB have demonstrated that our scheduling algorithm is much faster than the state-of-the-art methods. Moreover, our join implementation can also achieve speedups over the conventional approaches.

关键词： data analytics parallel joins data locality data-intensive computing high performance computing

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Parallel Processing of Massive EEG data with MapReduce

Parallel Processing of Massive EEG Data with MapReduce

引用

IEEE 18th International Conference on Parallel and Distributed Systems (ICPADS)

作者： Wang, Lizhe Chen, Dan Ranjan, Rajiv Khan, Samee U. Kolodziej, Joanna Wang, Jun Chinese Acad Sci Ctr Earth Observat & Digital Earth Beijing 100864 Peoples R China China Univ Geosciences Sch Comp Beijing Peoples R China CSIRO ICT Ctr Informat Engn lab Marshfield 2122 WI USA N Dakota State Univ Dept Elect & Comp Engn Grand Forks ND 58201 USA Univ Technol Inst Comp Sci Krakow Poland Univ Cent Florida Dept Elect Engn & Comp Sci Orlando FL 32816 USA

ISBN: (纸本)9781467345651;9780769549033

Analysis of neural signals like electroencephalogram (EEG) is one of the key technologies in detecting and diagnosing various brain disorders. As neural signals are non-stationary and non-linear in nature, it is almost impossible to understand their true physical dynamics until the recent advent of the Ensemble Empirical Mode Decomposition (EEMD) algorithm. The neural signal processing with EEMD is highly compute-intensive due to the high complexity of the EEMD algorithm. It is also data-intensive because 1) EEG signals contain massive data sets 2) EEMD has to introduce a large number of trials in processing to ensure precision. The MapReduce programming mode is a promising parallel computing paradigm for data intensive computing. To increase the efficiency and performance of the neural signal analysis, this research develops parallel EEMD neural signal processing with MapReduce. In this paper, we implement the parallel EEMD with Hadoop in a modern cyberinfrastructure. Test results and performance evaluation show that parallel EEMD can significantly improve the performance of neural signal processing.

关键词： data-intensive computing MapReduce parallel processing PARALLEL PROCESSING (COMPUTERS) signal processing Electroencephalography Brain Diseases Key technology MAPPING(MATHEMATICAL) Signal analysis data parallel computing data flow computing

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Canary: Fault-Tolerant FaaS for Stateful Time-Sensitive Applications

Canary: Fault-Tolerant FaaS for Stateful Time-Sensitive Appl...

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International Conference for High Performance computing, Networking, Storage and Analysis (HPC)

作者： Arif, Moiz Assogba, Kevin Rafique, M. Mustafa Rochester Inst Technol Dept Comp Sci Rochester NY 14623 USA

ISBN: (纸本)9781665454445

Function-as-a-Service (FaaS) platforms have recently gained rapid popularity. Many stateful applications have been migrated to FaaS platforms due to their ease of deployment, scalability, and minimal management overhead. However, failures in FaaS have not been thoroughly investigated, thus making these desirable platforms unreliable for guaranteeing function execution and ensuring performance requirements. In this paper, we propose Canary, a highly resilient and faulttolerant framework for FaaS that mitigates the impact of failures and reduces the overhead of function restart. Canary utilizes replicated container runtimes and application-level checkpoints to reduce application recovery time over FaaS platforms. Our evaluations using representative stateful FaaS applications show that Canary reduces the application recovery time and dollar cost by up to 83% and 12%, respectively over the default retry-based strategy. Moreover, it improves application availability with an additional average execution time and cost overhead of 14% and 8%, respectively, as compared to the ideal failure-free execution.

关键词： data-intensive computing Serverless computing Deep Learning data Parallelism OpenWhisk TensorFlow

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MATE-CG: A MapReduce-Like Framework for Accelerating data-intensive Computations on Heterogeneous Clusters

MATE-CG: A MapReduce-Like Framework for Accelerating Data-In...

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26th IEEE International Parallel and Distributed Processing Symposium (IPDPS) / Workshop on High Performance data intensive computing

作者： Jiang, Wei Agrawal, Gagan Ohio State Univ Dept Comp Sci & Engn Columbus OH 43210 USA

ISBN: (纸本)9780769546759

Clusters of GPUs have rapidly emerged as the means for achieving extreme-scale, cost-effective, and power-efficient high performance computing. At the same time, high-level APIs like map-reduce are being used for developing several types of high-end and/or data-intensive applications. Map-reduce, originally developed for data processing applications, has been successfully used for many classes of applications that involve a significant amount of computations, such as machine learning, image processing, and data mining applications. Because such applications can be accelerated using GPUs (and other accelerators), there has been interest in supporting map-reduce-like APIs on GPUs. However, while the use of map-reduce for a single GPU has been studied, developing map-reduce-like models for programming a heterogeneous CPU-GPU cluster remains an open challenge. This paper presents the MATE-CG system, which is a map-reduce-like framework based on the generalized reduction API. We develop support for enabling scalable and efficient implementation of data-intensive applications in a heterogeneous cluster of multi-core CPUs and many-core GPUs. Our contributions are three folds: 1) we port the generalized reduction model on clusters of modern GPUs with a map-reduce-like API, dealing with very large datasets;2) we further propose three schemes to better utilize the computing power of CPUs and/or GPUs and develop an auto-tuning strategy to achieve the best-possible heterogeneous configuration for iterative applications;3) we show how analytical models can be used to optimize important parameters in our system. We evaluate our system using three representative data-intensive applications and report results on a heterogeneous cluster of 128 CPU cores and 16 GPUs (7168 GPU cores). We show an average speedup of 87x on this cluster over execution with 2 CPU-cores. Our applications also achieve an average improvement of 25% by using CPU cores and GPUs simultaneously, over the best pe

关键词： data-intensive computing GPUs Heterogeneous Systems

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