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distributed parallel #SAT Solving

Distributed Parallel #SAT Solving

引用

IEEE international conference on Cluster Computing

作者： Jan Burchard Tobias Schubert Bernd Becker Albert Ludwigs University Freiburg Freiburg Germany

ISBN: (纸本)9781509036547

The #SAT problem, that is counting the number of solutions of a propositional formula, extends the well-known SAT problem into the realm of probabilistic reasoning. However, the higher computational complexity and lack of fast solvers still limits its applicability for real world problems. In this work we present our distributed parallel #SAT solver dCountAntom which utilizes both local, shared-memory parallelism as well as distributed (cluster computing) parallelism. Although highly parallel solvers are known in SAT solving, such techniques have never been applied to the #SAT problem. Furthermore we introduce a solve progress indicator which helps the user to assess whether the presented problem is likely solvable within a reasonable time. Our analysis shows a high accuracy of the estimated progress. Our experiments with up to 256 CPU cores working in parallel yield large speedups across different benchmarks derived from real world problems: With the maximum number of available cores dCountAntom solved problems on average 141 times faster than a single core implementation.

关键词： Decision trees parallel processing Computational complexity Benchmark testing Planning Hardware

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Private and secure secret shared mapreduce (Extended Abstract) 30th

Private and secure secret shared mapreduce (Extended Abstrac...

引用

30th IFIP WG 11.3 conference on Data and applications Security, DBSec 2016

作者： Dolev, Shlomi Li, Yin Sharma, Shantanu Ben-Gurion University Beersheba Israel Xinyang Normal University Xinyang China

ISBN: (纸本)9783319414829

Data outsourcing allows data owners to keep their data in public clouds, which do not ensure the privacy of data and computations. One fundamental and useful framework for processing data in a distributed fashion is Map Reduce. In this paper, we investigate and present techniques for executing Map Reduce computations in the public cloud while preserving privacy. Specifically, we propose a technique to outsource a database using Shamir secret-sharing scheme to public clouds, and then, provide privacy-preserving algorithms for performing search and fetch, equijoin, and range queries using Map Reduce. Consequently, in our proposed algorithms, the public cloud cannot learn the database or computations. All the proposed algorithms eliminate the role of the database owner, which only creates and distributes secret-shares once, and minimize the role of the user, which only needs to perform a simple operation for result reconstructing. We evaluate the efficiency by (i) the number of communication rounds (between a user and a cloud), (ii) the total amount of bit flow (between a user and a cloud), and (iii) the computational load at the user-side and the cloud-side. © IFIP international Federation for Information processing 2016.

关键词： Privacy-preserving techniques

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A new efficient distributed computing middleware based on cloud micro-services for HPC

A new efficient distributed computing middleware based on cl...

引用

IEEE international conference on Multimedia Computing and Systems (ICMCS)

作者： Fatéma Zahra Benchara Mohamed Youssfi Omar Bouattane Hassan Ouajji Laboratory SSDIA ENSET Mohammedia Hassan II University of Casablanca Mohammedia Morocco

The aim of this paper is to present a new distributed computing middleware for High Performance Computing (HPC) based cloud micro-services. The great challenge is to maintain the scalability and efficiency of massively parallel and distributed computational system when the intensive big data processed by its applications is widely increased. Besides, the proposed middleware implements a new cooperative micro-services team works model for massively parallel and distributed computing. This model is constituted by distributed micro-services as Micro-service Virtual processing Units (MsVPUs) with integrated load balancing service and an AMQP communication protocol that grant HPC. The paper shows the proposed distributed computational scheme and its integrated middleware accompanying by some experimental results.

关键词： Computational modeling Cloud computing Load management Load modeling Nickel

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A Scalable, parallel Module for Overlapping Grids and Its applications

A Scalable, Parallel Module for Overlapping Grids and Its Ap...

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IEEE international conference on Big Data Security on Cloud (BigDataSecurity), High Performance and Smart Computing (HPSC) and Intelligent Data and Security (IDS)

作者： Hong Guo Aiqing Zhang Zeyao Mo Ye Pu Laboratory of Computational Physics Institute of Applied Physics and Computational Mathematics Beijing China State Key Laboratory of Numerical Modeling Chinese Academy of Sciences Beijing China

A parallel module for applications based on overlapping grids has been devised and implemented in JASMIN (J parallel Adaptive Structured Mesh applications INfrastructure). In this module, a patch-based data structure, a grid mapping method and a unified communication schedule have been designed and adopted to overcome the communication bottleneck broadly existing in overlapping grids parallel computing. A grid mapping method library has been designed to make the module be adaptive to all kinds of structured grids, and an interpolator library has also been designed to gather interpolators. Meanwhile, by encapsulating parallel computing strategies, such as distributed storage, data communications, etc. and providing standard interfaces, this module can help users realize overlapping grids parallel computing conveniently. According to our test results, applications based on this module can be run efficiently on thousands of processors, which prove the module's satisfying parallel performance.

关键词： Interpolation Program processors Schedules Atmospheric modeling parallel processing Libraries Receivers

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Efficient GPU Computing Framework of Cloud Filtering in Remotely Sensed Image processing

Efficient GPU Computing Framework of Cloud Filtering in Remo...

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Proceedings of the Digital Image Computing: Technqiues and applications (DICTA)

作者： Jing Ke Arcot Sowmya Yi Guo Tomasz Bednarz Michael Buckley School of Computer Science and Engineering UNSW / DATA61 CSIRO Sydney Australia School of Computer Science and Engineering UNSW Sydney Australia Mathematics Western Sydney University Sydney Australia DATA61 CSIRO Brisbane Australia DATA61 CSIRO Sydney Australia

For optical remote sensing images, an effective method to reduce or eliminate the impact of clouds is important. With big data input and real-time processing demands, efficient parallelization strategies are essential for high performance computing on multi-core systems. This paper proposes an efficient high performance parallel computing framework for cloud filtering and smoothing. A comparison and benchmarking of two parallel algorithms for cloud filtering that incorporates spatial smoothing solved by two-dimensional dynamic programming is implemented. The experiments were carried out on an NVIDIA GPU accelerator with evaluations of approximation, parallelism and performance. The test results show significant performance improvements with high accuracy compared with sequential CPU implementation, and can be applied to other multi-core systems.

关键词： Graphics processing units Smoothing methods Dynamic programming Indexes Instruction sets Heuristic algorithms parallel processing

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parallelizing Back Propagation Neural Network on Speculative Multicores

Parallelizing Back Propagation Neural Network on Speculative...

引用

international conference on parallel and distributed Systems (ICPADS)

作者： Yaobin Wang Hong An Zhiqin Liu Tao Liu Dongmei Zhao Department of Computer Science and Technology University of Science and Technology of China China Department of Computer Science and Technology Southwest University of Science and Technology China

ISBN: (纸本)9781509053827

applications typically exhibit extremely different performance characteristics depending on the accelerator. Back propagation neural network (BPNN) has been parallelized into different platforms. However, it has not yet been explored on speculative multicore architecture thoroughly. This paper presents a study of parallelizing BPNN on a speculative multicore architecture, including its speculative execution model, hardware design and programming model. The implementation was analyzed with seven well-known benchmark data sets. Furthermore, it trades off several important design factors in coming speculative multicore architecture. The experimental results show that: (1) the BPNN performs well on speculative multicore platform. It can achieve similar speedup (17.7x to 57.4x) compared with graphics processors (GPU) while provides a more friendly programmability. (2) 64 cores' computing resources can be used efficiently and 4k is the proper speculative buffer capacity in the model.

关键词： Multicore processing Instruction sets Graphics processing units Programming Neural networks Hardware

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I/O optimization in the checkpointing of OpenMP parallel applications 23

I/O optimization in the checkpointing of OpenMP parallel app...

引用

23rd Euromicro international conference on parallel, distributed, and Network-Based processing (PDP)

作者： Losada, Nuria Martin, Maria J. Rodriguez, Gabriel Gonzalez, Patricia Univ A Coruna Comp Architecture Grp La Coruna Spain

ISBN: (纸本)9781479984909

Despite the increasing popularity of shared-memory systems, there is a lack of tools for providing fault tolerance support to shared-memory applications. Checkpointing is one of the most popular fault tolerance techniques. However, checkpointing cost in terms of computing time, network utilization or storage resources can be a limitation for its practical use. This work proposes different techniques for the optimization of the I/O cost in the checkpointing of shared-memory parallel applications. The proposals are extensively evaluated using the OpenMP NAS parallel Benchmarks. Results show a significant decrease of the checkpointing overhead.

关键词： OpenMP Fault Tolerance Checkpointing

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distributed Graph Path Queries Using Spark

Distributed Graph Path Queries Using Spark

引用

IEEE Annual international Computer Software and applications conference (COMPSAC)

作者： Janani Balaji Rajshekhar Sunderraman Department of Computer Science Georgia State University Atlanta Georgia

Graphs are increasingly being used as the data structure of choice to represent interactions between heterogeneous entities. Graph path querying is a primary operation in the network graph space, for both real time querying and inferential analysis. The rate and volume of interconnected data being generated warrants efficient distributed solutions to manage and query network graphs in a scalable fashion. Existing distributed solutions have proposed several optimization techniques, including intelligent joins and partial evaluations to process path queries. However, the former relies on comprehensive indices while the latter involves extensive driver-side processing to combine the partial results, neither of which is efficient for processing large graphs. In this paper, we propose a novel distributed graph path query processing system using the Apache Spark framework.

关键词： Sparks Internet of things Joining processes Data models Computer science Electronic mail Data structures

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Realizing Extremely Large-Scale Stencil applications on GPU Supercomputers 21

Realizing Extremely Large-Scale Stencil Applications on GPU ...

引用

21st IEEE international conference on parallel and distributed Systems ICPADS

作者： Endo, Toshio Takasaki, Yuki Matsuoka, Satoshi Tokyo Inst Technol Tokyo Japan

ISBN: (纸本)9780769557854

The problem of deepening memory hierarchy towards exascale is becoming serious for applications such as those based on stencil kernels, as it is difficult to satisfy both high memory bandwidth and capacity requirements simultaneously. This is evident even today, where problem sizes of stencil-based applications on GPU supercomputers are limited by aggregated capacity of GPU device memory. There have been locality improvement techniques such as temporal blocking to enhance performance, but integrating those techniques into existing stencil applications results requires substantially higher programming cost, especially for complex applications and as a result are not typically utilized. We alleviate this problem with a run-time GPU-MPI process oversubscription library we call HHRT that automates data movement across the memory hierarchy, and a systematic methodology to convert, and optimize the code to accommodate temporal blocking. The proposed methodology has shown to significantly eases the adaptation of real applications, such as the whole-city airflow simulator embodying more than 12,000 lines of code;with careful tuning, we successfully maintain up to 85% performance even with problems whose footprint is four time larger than GPU device memory capacity, and scale to hundreds of GPUs on the TSUBAME2.5 supercomputer.

关键词： Graphics processing unit

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Optimization of Computation-Intensive applications in cc-NUMA Architecture

Optimization of Computation-Intensive Applications in cc-NUM...

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international conference on Networking and Network applications (NaNA)

作者： Ming Zhang Naijie Gu Kaixin Ren USTC and Loongson Technology Associated Laboratory Hefei China

ISBN: (纸本)9781467398046

Remote memory access brings lower bandwidth and higher latency compared with local memory access in Cache Coherent Non-Uniform Memory Access (cc-NUMA) architecture. Especially in the cc-NUMA platform where computing nodes are connected with network, the latency and bandwidth of network perform much worse than Hyper Transport (HT) and PCI-Express (PCI-E) bus. In order to enhance the performance of applications, a Hybrid parallel Framework for Computation-intensive applications (HPFCA) was proposed. Task distribution, data storage, multicore parallelism and kernel optimization were discussed in the HPFCA. "MPI+OpenMP/Pthreads" mechanism was used for multi-node platforms. MPI was used for distributed memory parallelism, and "OpenMP/Pthreads" was used for shared memory parallelism. Moreover, GEMM and FFT, the representatives of the computation-intensive applications in the Godson-3B, were studied. According to the HPFCA, the parallel algorithms of GEMM and FFT were optimized. Finally, experimental results demonstrated that HPFCA could bring ideal performance in the Godson-3B.

关键词： Kernel Multicore processing Optimization Bandwidth parallel algorithms

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