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The Bounded Data Reuse Problem in Scientific Workflows

The Bounded Data Reuse Problem in Scientific Workflows

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IEEE international parallel & distributed processing symposium

作者： Mohsen Zohrevandi Rida A. Bazzi School of Computing Informatics and Decision Systems Engineering Arizona State University 699 S Mill Ave Tempe AZ USA

ISBN: (纸本)9781467360661

Large datasets and time-consuming processes have become the norm in scientific computing applications. The exploration phase in the development of scientific workflows involves trial-and-error with workflow components, which can take a lot of time given the time-consuming nature of the workflow tasks. These facts suggest the possibility of reducing the development time by reusing intermediate data whenever possible. However the storage space is always limited. This introduces a problem: which intermediate datasets from one workflow should be kept to be reused in another workflow, with a limited amount of storage. For the general class of series parallel graphs, we model this problem using a non-linear integer programming formulation and show that it is NP-Hard. We provide a branch and bound optimal algorithm as well as efficient heuristics. We conducted experiments over a large set of randomly-generated workflows as well as a smaller set of synthetic workflows which are based on real-world workflows used by scientists in different disciplines. Our experiments show that the best solution produced by the heuristics only differs from the optimal value by less than 1% on average.

关键词： Scientific Workflows Intermediate Data Data Reuse Series-parallel.

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GPU-Accelerated protein family identification for metagenomics

GPU-Accelerated protein family identification for metagenomi...

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2013 IEEE 37th Annual Computer Software and applications Conference, COMPSAC 2013

作者： Wu, Changjun Kalyanaraman, Ananth Xerox Innovation Group Xerox Research Center Webster NY United States School of Electrical Engineering and Computer Science Washington State University Pullman WA United States

ISBN: (纸本)9780769549798

The clustering of putative protein/Open Reading Frame (ORF) sequences available from large-scale metagenomics survey projects is a core analytical function that has led to the identification and characterization of novel protein families of environmental microbial communities. The implementation of this function, however, is currently challenged not only by data size but also by data complexity. In this paper, we present a CPU-GPU implementation of a randomized graph clustering heuristic called Shingling, which was originally developed by Gibson et al. Our implementation uses the CPU and GPU for different stages of computation, using GPUs for the most time-consuming steps. Experimental results of a 2M ocean metagenomics data set obtained from the Sorcerer II Global Ocean Sampling project show that our new implementation is able to achieve a ~7X speedup over our serial implementation without using asynchronous CPU-GPU communication, with the GPU part alone contributing to over ~374X speedup in the accelerated part. Qualitative evaluation of the 2M data set shows that our method is able to improve sensitivity of clustering over existing methods, and is more successful in recruiting more sequences into the clustering without impacting the overall specificity. As a demonstration of a large scale run, we were able to cluster a real world homology graph, containing 11M vertices and 640M edges, and constructed from sequences of an ongoing Pacific Ocean metagenomics survey project, in about 94 minutes. © 2013 IEEE.

关键词： Graphics processing unit

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On Graphs, GPUs, and Blind Dating: A Workload to Processor Matchmaking Quest

On Graphs, GPUs, and Blind Dating: A Workload to Processor M...

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international symposium on parallel and distributed processing (IPDPS)

作者： Abdullah Gharaibeh Lauro Beltrão Beltrão Costa Elizeu Santos-Neto Matei Ripeanu Department of Electrical and Computer Engineering University of British Columbia Canada

Graph processing has gained renewed attention. The increasing large scale and wealth of connected data, such as those accrued by social network applications, demand the design of new techniques and platforms to efficiently derive actionable information from large scale graphs. Hybrid systems that host processing units optimized for both fast sequential processing and bulk processing (e.g., GPUaccelerated systems) have the potential to cope with the heterogeneous structure of real graphs and enable high performance graph processing. Reaching this point, however, poses multiple challenges. The heterogeneity of the processing elements (e.g., GPUs implement a different parallel processing model than CPUs and have much less memory) and the inherent irregularity of graph workloads require careful graph partitioning and load assignment. In particular, the workload generated by a partitioning scheme should match the strength of the processing element the partition is allocated to. This work explores the feasibility and quantifies the performance gains of such low-cost partitioning schemes. We propose to partition the workload between the two types of processing elements based on vertex connectivity. We show that such partitioning schemes offer a simple, yet efficient way to boost the overall performance of the hybrid system. Our evaluation illustrates that processing a 4-billion edges graph on a system with one CPU socket and one GPU, while offloading as little as 25% of the edges to the GPU, achieves 2x performance improvement over state-of-the-art implementations running on a dual-socket symmetric system. Moreover, for the same graph, a hybrid system with dualsocket and dual-GPU is capable of 1.13 Billion breadth-first search traversed edge per second, a performance rate that is competitive with the latest entries in the Graph500 list, yet at a much lower price point.

关键词： Graphics processing units Partitioning algorithms Computational modeling parallel processing Hardware Kernel Algorithm design and analysis

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Loop Chaining: A Programming Abstraction for Balancing Locality and parallelism

Loop Chaining: A Programming Abstraction for Balancing Local...

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IEEE international symposium on parallel and distributed processing Workshops and Phd Forum (IPDPSW)

作者： Christopher D. Krieger Michelle Mills Strout Catherine Olschanowsky Andrew Stone Stephen Guzik Xinfeng Gao Carlo Bertolli Paul H.J. Kelly Gihan Mudalige Brian Van Straalen Sam Williams Computer Science Department Colorado State University Fort Collins CO Mechanical Engineering Department Colorado State University Fort Collins CO Imperial College London London UK Oxford e-Research Centre University of Oxford Oxford UK Lawrence Berkeley National Lab Berkeley CA

ISBN: (纸本)9781479913725

There is a significant, established code base in the scientific computing community. Some of these codes have been parallelized already but are now encountering scalability issues due to poor data locality, inefficient data distributions, or load imbalance. In this work, we introduce a new abstraction called loop chaining in which a sequence of parallel and/or reduction loops that explicitly share data are grouped together into a chain. Once specified, a chain of loops can be viewed as a set of iterations under a partial ordering. This partial ordering is dictated by data dependencies that, as part of the abstraction, are exposed, thereby avoiding inter-procedural program analysis. Thus a loop chain is a partially ordered set of iterations that makes scheduling and determining data distributions across loops possible for a compiler and/or run-time system. The flexibility of being able to schedule across loops enables better management of the data locality and parallelism tradeoff. In this paper, we define the loop chaining concept and present three case studies using loop chains in scientific codes: the sparse matrix Jacobi benchmark, a domain-specific library, OP2, used in full applications with unstructured grids, and a domain-specific library, Chombo, used in full applications with structured grids. Preliminary results for the Jacobi benchmark show that a loop chain enabled optimization, full sparse tiling, results in a speedup of as much as 2.68x over a parallelized, blocked implementation on a multicore system with 40 cores.

关键词： Jacobian matrices Schedules Optimization parallel processing Sparse matrices Libraries Vectors

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Power Measurement and Concurrency Throttling for Energy Reduction in OpenMP Programs

Power Measurement and Concurrency Throttling for Energy Redu...

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IEEE international symposium on parallel and distributed processing Workshops and Phd Forum (IPDPSW)

作者： Allan K. Porterfield Stephen L. Olivier Sridutt Bhalachandra Jan F. Prins RENCI Univ. of North Carolina at Chapel Hill Chapel Hill NC USA Scalable System Software Dept Sandia National Laboratories Albuquerque NM USA Dept. of Computer Science Univ. of North Carolina at Chapel Hill Chapel Hill NC USA

ISBN: (纸本)9781479913725

Understanding on-node application power and performance characteristics is critical to the push toward exascale computing. In this paper, we present an analysis of factors that impact both performance and energy usage of OpenMP applications. Using hardware performance counters in the Intel Sandy bridge X86-64 architecture, we measure energy usage and power draw for a variety of OpenMP programs: simple micro-benchmarks, a task parallel benchmark suite, and a hydrodynamics mini-app of a few thousand lines. The evaluation reveals substantial variations in energy usage depending on the algorithm, the compiler, the optimization level, the number of threads, and even the temperature of the chip. Variations of 20% were common and in the extreme were over 2X. In most cases, performance increases and energy usage decreases as more threads are used. However, for programs with sub-linear speedup, minimal energy usage often occurs at a lower thread count than peak performance. Our findings informed the design and implementation of an adaptive run time system that automatically throttles concurrency using data measured on-line from hardware performance counters. Without source code changes or user intervention, the thread scheduler accurately decides when energy can be conserved by limiting the number of active threads. For the target programs, dynamic runtime throttling consistently reduces power and overall energy usage by up to 3%.

关键词： Optimization Hardware Runtime Instruction sets Radiation detectors parallel processing

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Optimal peer selection strategy in p2p-vod systems using dynamic evolution strategy

Optimal peer selection strategy in p2p-vod systems using dyn...

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2013 IEEE 37th Annual Computer Software and applications Conference, COMPSAC 2013

作者： Rohmer, Thibaud Nakib, Amir Nafaa, Abdelhamid Universite de Paris Est Creteil LISSI 122 rue Paul Armangot 94440 Vitry sur seine France University College Dublin PEL Stillorgan Rd Belfield-Dubline-4 Ireland

ISBN: (纸本)9780769549798

Peer-to-Peer Video-on-Demand (VoD) systems are rising as a new dominant way to distribute video content over IP networks. Although this approach improves the overall VoD system scalability, it still poses new challenges such as peers resource allocation. There has been numerous research works versed into the P2P streaming with different focus areas, and different approaches. Some work cover the resource allocation issue in P2P streaming systems where the real-time streaming add another dimension to the problem. Most work on P2P resource allocation approaches the problem with static rules strategies that fail to dynamically adjust in face of changing content demand (popularity) trends. In this paper, we focus on the problem of enhancing the performances of a P2P system by adapting the peer allocation strategy. The proposed resource allocation system dynamically switches between multiple strategies to optimally respond to observed and predicted shifts in the content popularity. To do so, a dynamic estimation problem is solved using a Levy distribution based Dynamic Evolution Strategy algorithm. The obtained results show that using a dynamic resource allocation reduces the rejection rate while maintaining high diversification in the face of a dynamically changing title demand. © 2013 IEEE.

关键词： Video on demand

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Integrating Multi-GPU Execution in an OpenACC Compiler

Integrating Multi-GPU Execution in an OpenACC Compiler

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international Conference on parallel processing (ICPP)

作者： Toshiya Komoda Shinobu Miwa Hiroshi Nakamura Naoya Maruyama Graduate School of Information Science and Technology The University of Tokyo Tokyo Japan RIKEN Advanced Institute for Computational Science Kobe Japan

GPUs have become promising computing devices in current and future computer systems due to its high performance, high energy efficiency, and low price. However, lack of high level GPU programming models hinders the wide spread of GPU applications. To resolve this issue, OpenACC is developed as the first industry standard of a directive-based GPU programming model and several implementations are now available. Although early evaluations of the OpenACC systems showed significant performance improvement with modest programming efforts, they also revealed the limitations of the systems. One of the biggest limitations is that the current OpenACC compilers do not automate the utilization of multiple GPUs. In this paper, we present an OpenACC compiler with the capability to execute single GPU OpenACC programs on multiple GPUs. By orchestrating the compiler and the runtime system, the proposed system can efficiently manage the necessary data movements among multiple GPUs memories. To enable advanced communication optimizations in the proposed system, we propose a small set of directives as extensions of OpenACC API. The directives allow programmers to express the patterns of memory accesses in the parallel loops to be offloaded. Inserting a few directives into an OpenACC program can reduce a large amount of unnecessary data movements and thus helps the proposed system drawing great performance from multi-GPU systems. We implemented and evaluated the prototype system on top of CUDA with three data parallel applications. The proposed system achieves up to 6.75x of the performance compared to OpenMP in the 1CPU with 2GPU machine, and up to 2.95x of the performance compared to OpenMP in the 2CPU with 3GPU machine. In addition, in two of the three applications, the multi-GPU OpenACC compiler outperforms the single GPU system where hand-written CUDA programs run.

关键词： Graphics processing units Arrays Kernel Programming Runtime distributed databases

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Handling Uncertainty: Pareto-Efficient BoT Scheduling on Hybrid Clouds

Handling Uncertainty: Pareto-Efficient BoT Scheduling on Hyb...

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international Conference on parallel processing (ICPP)

作者： M. Reza Hoseinyfarahabady Hamid R.D. Samani Luke M. Leslie Young Choon Lee Albert Y. Zomaya Centre for Distributed and High Performance Computing The University of Sydney NSW Australia

Coping with uncertainty is a challenging and complex problem particularly in hybrid cloud environments-private cloud plus public cloud. Conflicting goals of minimizing the cost and performance, unknown prior knowledge about task running times, and a lack of estimation tools are just a few of the challenges that resource management systems in those environments encounter. The aim in this paper is to find Pareto-optimal schedules for large-scale Bag-of-Tasks (BoT) applications that meet user defined constraints, such as deadline or budget or some tradeoff between them. BoT applications are common in science and engineering and consist of many independent tasks. To achieve the user's chosen Pareto-optimal schedule, we develop a dynamic resource allocation process for hybrid clouds. We also present a hybrid approach to estimating task running times that incorporates several estimators with a feedback control system to cope with the inherent uncertainty in such estimation. Through extensive experiments on a test bed hybrid cloud, using Amazon EC2 as a public cloud, we show that the proposed approach can achieve near optimality with little overhead, and consistently achieves a solution within 2% of the user's chosen Pareto-optimal schedule. Further, we demonstrate that our approach performs better than an extended List scheduling approach by reducing both the total cost and time needed to run the application by almost 20% and 5% on average, respectively.

关键词： Estimation Cloud computing Resource management Schedules Approximation algorithms Uncertainty Scheduling

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CASS 2012: The 2nd Workshop on Communication Architecture for Scalable Systems

CASS 2012: The 2nd Workshop on Communication Architecture fo...

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

作者： Flich, Jose Pakin, Scott Stunkel, Craig Univ Politecn Valencia Valencia Spain Los Alamos Natl Lab Los Alamos NM USA IBM Res Armonk NY 10504 USA

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Load balancing in large-scale epidemiological simulations 13

Load balancing in large-scale epidemiological simulations

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Proceedings of the 22nd international symposium on High-performance parallel and distributed computing

作者： Tariq Kamal Keith R. Bisset Ali R. Butt Youngyun Chungbaek Madhav Marathe Virginia Tech Blacksburg VA USA

ISBN: (纸本)9781450319102

Despite the recent advancements in graph partitioning techniques and algorithms, achieving static load balancing in agent-based epidemiological applications is challenging. Input to these simulations is a large agent-location bipartite graph that is highly complex and non-uniform. In this paper, we compare several static load distribution schemes, including our custom strategies, for partitioning a class of bipartite graphs. Computations over such graphs happen between classes of nodes in phases. Our performance evaluations on a 768 core system show that our lower-overhead custom load balancing strategy achieves a 2-fold increase in strong scaling performance compared to the default Round Robin data distribution.

关键词： distributed systems epidemiological simulations load balancing

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