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Distinguishing Medical Drugs from a Large Set of Side Effects Using a distributed Genetic Algorithm on a PC Cluster

Distinguishing Medical Drugs from a Large Set of Side Effect...

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IEEE international symposium on Circuits and systems

作者： Fazal Noor Majed Alhaisoni Mashaan A. Alshammari Ravi P. Ramachandran Computer Science and Software Engineering University of Hail Electrical and Computer Engineering Rowan University

ISBN: (纸本)9781479983926

A distributed Genetic Algorithm to compute minimal reducts is presented for a novel biomedical application to distinguish 50 medical drugs from 228 side effects. The results indicate that 15 side effects are sufficient to differentiate among all the 50 drugs. In fact, any one of 4 sets of 15 side effects can be used. The distributed Genetic Algorithm is inherently parallel, uses a variable mutation rate and is efficiently implemented on a PC cluster using 5, 10 and 20 nodes each with a Message Passing Interface. Results show that the distributed algorithm with 20 nodes uses much less computation time than two sequential methods (savings of about a factor of 5).

关键词： Side Effect Genetic Algorithm (GA) Pharmaceutical Preparations Distribute distributed algorithms Mutation Rate computational time Medical

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Vertex-centric parallel Algorithms for Identifying Key Vertices in Large-Scale Graphs

Vertex-centric Parallel Algorithms for Identifying Key Verti...

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IEEE international Conference on High Performance Computing and Communications (HPCC)

作者： Bo Li Zhuangliang Gao Jianwei Niu YanFei Lv Hong Zhang School of Computer Science and Engineering Beihang University Beijing China CNCERT/CC Beijing China

ISBN: (纸本)9781479989386

Betweenness centrality is a metric to measure the relative importance of vertices within a graph. The computation of betweenness centrality is based on shortest paths which requires O(n+m) space and O(mn) and O(nm+n 2 log n) time on unweighted and weighted graphs, respectively. It is time-consuming to deal with large-scale graphs, which motivates us resort to distributed computing and parallel algorithms. In this paper, we design a vertex-based parallel algorithm following the shortest path approach (SPBC). Moreover, we propose a distributed algorithm based on message propagation(MPBC) to quantify the importance of vertices. MPBC takes into account the real situation of information diffusion in social networks. We implement our algorithms on Graphlab and evaluate them through comprehensive experiments. The results show that both SPBC and MPBC scale well with the increasing number of machines. SPBC on 2 machines outperforms the classical centralized algorithm by 1.59 times in terms of running time. MPBC can handle graph with ten millions of vertices and edges within an acceptable time where classical algorithms become infeasible.

关键词： Algorithm design and analysis Synchronization Social network services Mirrors distributed algorithms parallel algorithms

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GPU Accelerated Molecular Dynamics with Method of Heterogeneous Load Balancing

GPU Accelerated Molecular Dynamics with Method of Heterogene...

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

作者： Takuro Udagawa Masakazu Sekijima NTT Software Innovation Center NTT Corporation Tokyo Japan Graduate School of Information Science and Engineering Tokyo Institute of Technollogy Global Scientific Information and Computing Center Tokyo Institute of Technology Tokyo Japan

ISBN: (纸本)9781467376853

Molecular Dynamics simulations are widely used to obtain a deeper understanding of chemical reactions, fluid flows, phase transitions, and other physical phenomena due to molecular interactions. The main problem with this method is that it is computationally demanding because of its amount of O (N2) and requirements for prolonged simulations. The use of Graphics Processing Units (GPUs) is an attractive solution and has been applied to this problem thus far. However, such heterogeneous approaches occasionally cause load imbalances between CPUs and GPUs and they don't utilize all computational resources. We propose a method of balancing the workload between CPUs and GPUs, which we implemented. Our method is based on formulating and observing workloads and it statically distributes work according to spatial decomposition. We succeeded in utilizing processors more efficiently and accelerating simulations by 20.7 % at most compared to the original GPU optimized code.

关键词： Graphics processing units Force Computational modeling Acceleration Biological system modeling Message systems

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Experience report: A characteristic study on out of memory errors in distributed data-parallel applications

Experience report: A characteristic study on out of memory e...

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international symposium on software Reliability engineering (ISSRE)

作者： Lijie Xu Wensheng Dou Feng Zhu Chushu Gao Jie Liu Hua Zhong Jun Wei University of Chinese Academy of Sciences State Key Lab of Computer Science Chinese Academy of Sciences

Out of memory (OOM) errors occur frequently in data-intensive applications that run atop distributed data-parallel frameworks, such as MapReduce and Spark. In these applications, the memory space is shared by the framework and user code. Since the framework hides the details of distributed execution, it is challenging for users to pinpoint the root causes and fix these OOM errors. This paper presents a comprehensive characteristic study on 123 real-world OOM errors in Hadoop and Spark applications. Our major findings include: (1) 12% errors are caused by the large data buffered/cached in the framework, which indicates that it is hard for users to configure the right memory quota to balance the memory usage of the framework and user code. (2) 37% errors are caused by the unexpected large runtime data, such as large data partition, hotspot key, and large key/value record. (3) Most errors (64%) are caused by memory-consuming user code, which carelessly processes unexpected large data or generates large in-memory computing results. Among them, 13% errors are also caused by the unexpected large runtime data. (4) There are three common fix patterns (used in 34% errors), namely changing the memory/dataflow-related configurations, dividing runtime data, and optimizing user code logic. Our findings inspire us to propose potential solutions to avoid the OOM errors: (1) providing dynamic memory management mechanisms to balance the memory usage of the framework and user code at runtime; (2) providing users with memory+disk data structures, since accumulating large computing results in in-memory data structures is a common cause (15% errors).

关键词： Runtime Sparks Programming Fault tolerance Fault tolerant systems Memory management Data structures

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Improving Storage Availability in Cloud-of-Clouds with Hybrid Redundant Data Distribution

Improving Storage Availability in Cloud-of-Clouds with Hybri...

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international symposium on parallel and distributed Processing (IPDPS)

作者： Bo Mao Suzhen Wu Hong Jiang Software School of Xiamen University China Computer Science Department of Xiamen University China State Key Laboratory of High-end Server & Storage Technology Jinan China Department of Computer Science and Engineering University of Nebraska-Lincoln USA

ISBN: (纸本)9781479986507

With the increasing utilization and popularity of the cloud infrastructure, more and more data are moved to the cloud storage systems. This makes the availability of cloud storage services critically important, particularly given the fact that outages of cloud storage services have indeed happened from time to time. Thus, solely depending on a single cloud storage provider for storage services can risk violating the service-level agreement (SLA) due to the weakening of service availability. This has led to the notion of Cloud-of-Clouds, where data redundancy is introduced to distribute data among multiple independent cloud storage providers, to address the problem. The key in the effectiveness of the Cloud-of-Clouds approaches lies in how the data redundancy is incorporated and distributed among the clouds. However, the existing Cloud-of-Clouds approaches utilize either replication or erasure codes to redundantly distribute data across multiple clouds, thus incurring either high space or high performance overheads. In this paper, we propose a hybrid redundant data distribution approach, called HyRD, to improve the cloud storage availability in Cloud-of-Clouds by exploiting the workload characteristics and the diversity of cloud providers. In HyRD, large files are distributed in multiple cost-efficient cloud storage providers with erasure-coded data redundancy while small files and file system metadata are replicated on multiple high-performance cloud storage providers. The experiments conducted on our lightweight prototype implementation of HyRD show that HyRD improves the cost efficiency by 33.4% and 20.4%, and reduces the access latency by 58.7% and 34.8% than the DuraCloud and RACS schemes, respectively.

关键词： Cloud computing Redundancy Bandwidth Switches Prototypes distributed databases

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A Data-Oriented Method for Scheduling Dependent Tasks on High-Density Multi-GPU systems

A Data-Oriented Method for Scheduling Dependent Tasks on Hig...

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IEEE international Conference on High Performance Computing and Communications (HPCC)

作者： Peng Zhang Yuxiang Gao Meikang Qiu Biomedical Engineering Department Stony Brook University Stony Brook NY United States Cluster Solution Department Cray Inc. San Jose CA United States Computer Science Department Pace University New York NY United States

ISBN: (纸本)9781479989386

The rapidly-changing computer architectures, though improving the performance of computers, have been challenging the programming environments for efficiently harnessing the potential of novel architectures. In this area, though the high-density multi-GPU architecture enabled unparalleled performance advantage of dense GPUs in a single server, it has increased the difficulty for scheduling diversified and dependent tasks. We therefore propose a data-oriented method for scheduling dependent tasks for this architecture while providing its implementation. In our method, we model a parallel program as a collection of data-dependent tasks for which data dependencies are managed by an expressive matrix. Accordingly, we develop a hierarchical scheduler infrastructure for our model. In this, a top scheduler is built for querying the data-dependency matrix; three downstream schedulers for queuing computation tasks that are exclusively assigned to processor, accelerator or either; and a multitude of bottom schedulers each for providing a processing element with assigned tasks. We experiment our scheduler for examples of Strassen matrix multiplication and Cholesky matrix inversion algorithms on a computer that has 8 Tesla K40 GPUs. The results show that our method is capable of offering the efficient task parallelism while fulfilling the complex task dependencies. When advanced task-oriented schedulers have been widely designed for distributed systems, a lightweight data-driven scheduler could be an alternative and handy approach that can handle the dependent yet diversified tasks of data-intensive applications for the novel high-density multi-accelerator system.

关键词： Computer architecture Processor scheduling Computers Symmetric matrices Partitioning algorithms Runtime Graphics processing units

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PLDSRC: A Multi-threaded Compressor/Decompressor for Massive DNA Sequencing Data 13

PLDSRC: A Multi-threaded Compressor/Decompressor for Massive...

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13th international symposium on distributed Computing and Applications to Business, engineering and Science (DCABES)

作者： Zhan, Ke Yang, Chao Zhang, Changyou Zheng, Jingjing Wang, Ting Chinese Acad Sci Inst Software Beijing 100190 Peoples R China Univ Chinese Acad Sci Beijing 100049 Peoples R China Chinese Acad Sci State Key Lab Comp Sci Beijing 100190 Peoples R China

ISBN: (纸本)9781479941698

To face the rapid growth of DNA sequencing data, it is of great importance to study high efficiency compression techniques to reduce the cost of storing the massive amount of sequencing data. In this paper, we propose a parallel DNA data compressor/decompressor, PLDSRC, based on the famous serial DSRC software. We first analyze the compression and decompression algorithm in DSRC and identity three basic operations, namely read, work, and write. Then a single pipeline parallel algorithm is proposed to accelerate the compression/decompression procedure. To further exploit today's popular multi-core, multi-socket systems based on the non-uniform memory access (NUMA) architecture, we extend the single pipeline approach to the multi-pipeline case. Experiments on two different platforms are done and show that PLDSRC in both single and multiple pipeline forms is able to speed up DNA sequencing data compression/decompression greatly, while maintaining the same compressing ratio. Examples indicate that the maximum speedup of PLDSRC on compressing and decompressing is respectively around 24.71x and 22.00x, as compared to the serial DSRC software.

关键词： DNA sequencing compression DSRC PLDSRC Multi-pipeline NUMA

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Autonomy Requirements engineering for Self-adaptive Science Clouds 28

Autonomy Requirements Engineering for Self-adaptive Science ...

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28th IEEE international parallel & distributed Processing symposium Workshops (IPDPSW)

作者： Vassev, Emil Hinchey, Mike Univ Limerick Lero The Irish Software Engn Res Ctr Limerick Ireland

ISBN: (纸本)9781479941162

Self-adaptive clouds extend upstream the regular cloud platforms with special autonomy features dedicated to handling increasing workload and service failures. The identification of such features is not necessarily an easy task. Sometimes those can be explicitly stated by QoS requirements or in preliminary material available to requirements engineers. Often though, they are implicit so that autonomy features capturing has to be undertaken. This paper elaborates on a methodology of capturing autonomy requirements for self-adaptive clouds with ARE, the Autonomy Requirements engineering approach. In this approach, autonomy features are detected as special self-* objectives backed up by different capabilities and quality characteristics.

关键词： self-adaptive clouds autonomy requirements autonomic systems

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A distributed-memory parallelization of a Shared-memory parallel Ensemble Kalman Filter 16

A Distributed-memory Parallelization of a Shared-memory Para...

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16th international symposium on Symbolic and Numeric Algorithms for Scientific Computing (SYNASC)

作者： Rostami, M. Ali Buecker, H. Martin Vogt, Christian Seidler, Ralf Neuhaeuser, David Rath, Volker Univ Jena Inst Comp Sci D-07743 Jena Germany Rhein Westfal TH Aachen Inst Appl Geophys & Geothermal Energy EON Energy Res Ctr D-52056 Aachen Germany Dublin Inst Adv Studies Sch Cosm Phys Dublin 4 Ireland Univ Complutense Madrid Dept Fis Tierra Astron & Astrofis 2 Madrid Spain

ISBN: (纸本)9781479984480

Inverse problems arise in various areas of science and engineering. These problems are not only difficult to solve numerically, but they also require a large amount of computer resources both in time and memory. It is therefore not surprising that inverse problems are often solved using techniques from high-performance computing. We consider the parallelization of an inverse problem in the field of geothermal reservoir engineering. In this particular scientific application, the underlying software package is already parallelized using the shared-memory programming paradigm OpenMP. Here, we present an extension of this parallelization to distributed memory enabling a hybrid OpenMP/MPI parallelization. The situation is different from the standard way of hybrid parallel programming because the data structures of the OpenMP-parallelized code differ from those in the serial implementation. We exploit this transformation of the data structures in our distributed-memory strategy for parallelizing an ensemble Kalman filter, a particular method for the solution of inverse problems. We describe this novel parallelization strategy, introduce a performance model, and present timing results on a compute cluster using nodes with 2 sockets, each equipped with Intel Xeon X5675 Westmere EP processors with 6 cores. All timing results are obtained with a pure MPI parallelization without using any OpenMP threads.

关键词： Kalman filters message passing parallel programming shared memory systems Intel Xeon X5675 Westmere EP processors computer resource data structures distributed-memory parallelization geothermal reservoir engineering hybrid Open MP-MPI parallelization hybrid parallel programming message passing interface parallelization strategy shared-memory parallel ensemble Kalman filter shared-memory programming paradigm Algorithm design and analysis Arrays Computational modeling Inverse problems Mathematical model Standards Inverse method message passing Kalman filters parallelization Algorithm design and analysis shared memory systems parallel programming Data structures Reservoir engineering distributed memory Computational modeling Mathematical Model

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ISPASS 2014 - IEEE international symposium on Performance Analysis of systems and software

ISPASS 2014 - IEEE International Symposium on Performance An...

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2014 IEEE international symposium on Performance Analysis of systems and software, ISPASS 2014

ISBN: (纸本)9781479936052

The proceedings contain 31 papers. The topics discussed include: BarrierPoint: sampled simulation of multi-threaded applications;exploiting spatial architectures for edit distance algorithms;a top-down method for performance analysis and counters architecture;Moby: a mobile benchmark suite for architectural simulators;optimized hardware for suboptimal software: the case for SIMD-aware benchmarks;extending statistical cache models to support detailed pipeline simulators;manifold: a parallel simulation framework for multicore systems;prime: a parallel and distributed simulator for thousand-core chips;steps towards wider use of concurrency code patterns;power modeling and other new features in the graphite simulator;evaluating trace aggregation for performance visualization of large distributed systems;reverse engineering of cache replacement policies in Intel microprocessors and their evaluation;and a software based profiling method for obtaining speedup stacks on commodity multi-cores.

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