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2017 5th international Conference on Machinery,Materials and computing Technology(ICMMCT 2017)

作者： Xiaocheng Luo Jingfei Jiang Junjie Zhu Yong Dou National Laboratory for Parallel and Distributed Processing National University of Defense Technology

Audio feature extraction is a very important technique in the field of sound processing. It extremely impacts the effectiveness and correctness of sound recognition, sound verification, etc. It is a computation intensive stage in the whole sound recognition process, which is a challenging for acceleration. In this paper, a coarse-grained parallel feature extraction algorithm for high throughput of audio slices is proposed to improve the efficiency of audio feature extraction. three typical audio feature extraction algorithms, Mel Frequency Cepstrum Coefficients(MFCC), Spectrogram image features(SIF), Octave-Based Spectral Contrast, are chosen to parallelize. Experiments results on different platforms show that the speedup of accelerated audio feature extraction is up to 17.23 on the platform with 16 cores 32 threads.

关键词： Feature Extraction Sound Recognition parallel Algorithm

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A Study on Security Risk in Cloud computing

A Study on Security Risk in Cloud Computing

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ISCC 2017-the 5th international Conference on Information Science and Cloud computing

作者： Lijun Chen Xiaoru Chen Institute of software ecology and artificial intelligence South China Institute of Software Engineering of Guang Zhou university South China Institute of Software Engineering of Guang Zhou University

With the rapid development of the global economy and internet technology, cloud computing technology has been advanced at a rapid pace. Cloud computing technology is a new development model for large-scale computing after distributed computing, parallel computing and grid computing. In the in-depth application of cloud computing services, cloud programs continue to expand. Due to the popularity of cloud computing services, cloud service security has become an important factor affecting its application and development. Cloud computing technology will be a public resource shared among computing resources, storage resources and others;it has also led to the lack of transparency and less control of IT assets, resulting in many business concerns about data loss and destruction. At present, the main risks to cloud computing lie in sharing, insecure interfaces, privileged user visits, identity counterfeiting, data security,management and legal affairs. To reduce or eliminate the main risks of cloud computing,encryption technology is adopted, with the introduction of strict identity authentication mechanism, strengthen management and safety supervision, as well as improvement on information security laws, regulations and other countermeasures.

关键词： service A Study on Security Risk in Cloud computing Security

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Two parallel Implementations of Ehrlich-Aberth Algorithm for Root-Finding of Polynomials on Multiple GPUs with OpenMP and MPI 19

Two Parallel Implementations of Ehrlich-Aberth Algorithm for...

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19th IEEE international Conference on Computational Science and Engineering, 14th IEEE international Conference on Embedded and Ubiquitous computing and 15th international symposium on distributed computing and Applications to Business, Engineering and Science, CSE-EUC-DCABES 2016

作者： Ghidouche, Kahina Sider, Abderrahmane Khodja, Lilia Ziane Couturier, Raphaël Laboratoire LIMED Faculté des Sciences Exactes Université de Bejaia 06000 Algeria University Bourgogne Franche-Comte France

ISBN: (纸本)9781509035939

Finding the roots of polynomials is a very important part of solving real-life problems but the higher the degree of the polynomials is, the less easy it becomes. In this paper, we present two different parallel algorithms of the Ehrlich-Aberth method to find roots of sparse and fully defined polynomials of high degrees. Both algorithms are based on CUDA technology to be implemented on multi-GPU computing platforms but each use different parallel paradigms: OpenMP or MPI. the experiments show a quasi-linear speedup by using up-to 4 GPU devices compared to 1 GPU to find the roots of polynomials of degree up-to 1.4 million. Moreover, other experiments show it is possible to find the roots of polynomials of degree up-to 5 million. © 2016 IEEE.

关键词： Graphics processing unit

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Design and optimisation of an efficient HDF5 I/O Kernel for massive parallel fluid flow simulations

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CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE 2017年第24期29卷 e4165.1-e4165.12页

作者： Ertl, Christoph Frisch, Jerome Mundani, Ralf-Peter Tech Univ Munich Computat Engn Munich Germany Rhein Westfal TH Aachen Inst Energy Efficiency & Sustainable Bldg E3D Aachen Germany

More and more massive parallel codes running on several hundreds of thousands of cores are entering the computational science and engineering domain, allowing high-fidelity computations on up to trillions of unknowns for very detailed analyses of the underlying problems. Such runs typically produce gigabytes of data, hindering both efficient storage and (interactive) data exploration. Advanced approaches based on inherently distributed data formats such as hierarchical data format version 5 become necessary here to avoid long latencies when storing the data and to support fast (random) access when retrieving the data for visual processing. this paper shows considerations and implementation aspects of an I/O kernel based on hierarchical data format version 5 that supports fast checkpointing, restarting, and selective visualisation using a single shared output file for an existing computational fluid dynamics framework. this functionality is achieved by including the framework's hierarchical data structure in the file, which also opens the door for additional steering functionality. Finally, the performance of the kernel's write routines are presented. Bandwidths close to the theoretical peak on modern supercomputing clusters were achieved by avoiding file-locking and using collective buffering.

关键词： computational fluid dynamics computational steering HDF5 high-performance computing I/O

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CRC-based Memory Reliability for Task-parallel HPC Applications 30

CRC-based Memory Reliability for Task-parallel HPC Applicati...

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30th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Subasi, Omer Unsal, Osman Labarta, Jesus Yalcin, Gulay Cristal, Adrian Barcelona Supercomputing Ctr Barcelona Spain Univ Politecn Cataluna E-08028 Barcelona Spain Abdullah Gul Univ Kayseri Turkey CSIC Spanish Natl Res Council IIIA Artificial Intelligence Res Inst Madrid Spain

ISBN: (纸本)9781509021406

Memory reliability will be one of the major concerns for future HPC and Exascale systems. this concern is mostly attributed to the expected massive increase in memory capacity and the number of memory devices in Exascale systems. For memory systems Error Correcting Codes (ECC) are the most commonly used mechanism. However state-of-the art hardware ECCs will not be sufficient in terms of error coverage for future computing systems and stronger hardware ECCs providing more coverage have prohibitive costs in terms of area, power and latency. Software-based solutions are needed to cooperate with hardware. In this work, we propose a Cyclic Redundancy Checks (CRCs) based software mechanism for task-parallel HPC applications. Our mechanism incurs only 1.7% performance overhead with hardware acceleration while being highly scalable at large scale. Our mathematical analysis demonstrates the effectiveness of our scheme and its error coverage. Results show that our CRCbased mechanism reduces the memory vulnerability by 87% on average with up to 32-bit burst (consecutive) and 5-bit arbitrary error correction capability.

关键词： Cyclic Redundancy Checks Dataflow Model HPC Applications Memory Reliability Task-parallelism

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Enhancing scalability and load balancing of parallel Selected Inversion via tree-based asynchronous communication 30

Enhancing scalability and load balancing of Parallel Selecte...

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30th IEEE international parallel and distributed Processing symposium (IPDPS)

作者： Jacquelin, Mathias Yang, Chao Lin, Lin Wichmann, Nathan Lawrence Berkeley Natl Lab Scalable Solvers Grp Berkeley CA 94720 USA Univ Calif Berkeley Dept Math Berkeley CA 94720 USA Lawrence Berkeley Natl Lab Berkeley CA 94720 USA Cray Inc Seattle WA USA

ISBN: (纸本)9781509021406

We develop a method for improving the parallel scalability of computations that involve asynchronous task execution. We apply this method to the recently developed parallel selected inversion algorithm [Jacquelin, Lin and Yang 2014], named PSelInv, on massively parallel distributed memory machines. In the PSelInv method, we compute selected elements of the inverse of a sparse matrix A that can be decomposed as A = LU, where L is lower triangular and U is upper triangular. computing these selected elements of A(-1) requires restricted collective communications among a subset of processors within each column or row communication group created by a block cyclic distribution of L and U. We describe how this type of restricted collective communication can be implemented using asynchronous point-to-point MPI communications combined with a binary tree based data propagation scheme. Because multiple restricted collective communications may take place at the same time, we need to use a heuristic to prevent processors participating in multiple collective communications from receiving too many messages. this heuristic allows us to reduce communication load imbalance and improve the overall scalability of the selected inversion algorithm. For instance, when 6, 400 processors are used, we observe that the use of this heuristic leads to over 5x speedup for a number of test matrices. It also mitigates the performance variability introduced by an inhomogeneous network topology.

关键词： selected inversion distributed memory parallel algorithm asynchronous data communication collective communication high performance computation load balancing

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A Research on Minisat Using Coarse-grained parallel Technique Based on Multi-core and Multi-platform

A Research on Minisat Using Coarse-grained Parallel Techniqu...

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2017 5th international Conference on Machinery,Materials and computing Technology(ICMMCT 2017)

作者： Junjie Zhu Jingfei Jiang Xiaocheng Luo Yong Dou National Laboratory of Parallel and Distributed Processing National University of Defence Technology

In this paper, we make a research on a widely-used SAT solver, Minisat, aiming to improve its performance using coarse-grained parallel method on multi-core and multi-platform. Firstly, we parallel the Minisat by mean of Open MP and test its performance with different threads by running a test set consisting of 2000 SAT problems on an X86 computer. Besides, a scheduling strategy with time sequence is added to the process and achieves a better speed-up ratio. then, we move the algorithm to an ARM computer and repeat the same process, finding that the performance of Minisat on X86 is better than that on ARM, but ARM platform has a better scale effect than X86 platform when running at full load and is able to perform better than X86 when they have the same hardware configuration.

关键词： Minisat solver coarse-grained multi-core multi-platform

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Scalable distributed Meta-data Management In parallel NFS 5

Scalable Distributed Meta-data Management In Parallel NFS

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5th IIAI international Congress on Advanced Applied Informatics (IIAI-AAI)

作者： Arumugam, Rajesh Vellore Lin Wujuan Yi, Su Hnin Wut Hitachi Asia Ltd Singapore Singapore

ISBN: (纸本)9781467389853

High performance computing (HPC) applications demand high I/O throughput from file systems to match their fast processing requirements. these HPC applications create large amounts of file meta-data that can overwhelm current single meta-data server file systems leading to performance bottlenecks. We address this problem with a multiple meta-data server (M-MDS) design using standard parallel NFS (pNFS). We utilize NFS directory referrals mechanism with hashing to efficiently distribute meta-data across a cluster of meta-data servers. We show through a large scale setup in Amazon EC2 that our pNFS M-MDS can scale almost linearly and outperform Lustre CMD ( Clustered Metadata) by up to 3 times in some of the file system meta-data operation benchmarks.

关键词： Meta-data distribution parallel file systems

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Efficient detection of dangling pointer error for C/C++ programs 2

Efficient detection of dangling pointer error for C/C++ prog...

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2nd Annual international Conference on Information System and Artificial Intelligence, ISAI 2017

作者： Zhang, Wenzhe Science and Technology on Parallel and Distributed Laboratory State Key Laboratory of High Performance Computing State Key Laboratory of High-end Server and Storage Technology College of Computer National University of Defense Technology Changsha China

Dangling pointer error is pervasive in C/C++ programs and it is very hard to detect. this paper introduces an efficient detector to detect dangling pointer error in C/C++ programs. By selectively leave some memory accesses unmonitored, our method could reduce the memory monitoring overhead and thus achieves better performance over previous methods. Experiments show that our method could achieve an average speed up of 9% over previous compiler instrumentation based method and more than 50% over previous page protection based method. © Published under licence by IOP Publishing Ltd.

关键词： Errors

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SWAT: A programmable, in-memory, distributed, high-performance computing platform 16

SWAT: A programmable, in-memory, distributed, high-performan...

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25th ACM international symposium on High-Performance parallel and distributed computing, HPDC 2016

作者： Grossman, Max Sarkar, Vivek Rice University 6100 Main St HoustonTX United States

ISBN: (纸本)9781450343145

the field of data analytics is currently going through a renaissance as a result of ever-increasing dataset sizes, the value of the models that can be trained from those datasets, and a surge in exible, distributed programming models. In particular, the Apache Hadoop [1] and Spark [5] programming systems, as well as their supporting projects (e.g. HDFS, SparkSQL), have greatly simplified the analysis and transformation of datasets whose size exceeds the capacity of a single machine. While these programming models facilitate the use of distributed systems to analyze large datasets, they have been plagued by performance issues. the I/O performance bottlenecks of Hadoop are partially responsible for the creation of Spark. Performance bottlenecks in Spark due to the JVM object model, garbage collection, interpreted/-managed execution, and other abstraction layers are responsible for the creation of additional optimization layers, such as Project Tungsten [4]. Indeed, the Project Tungsten issue tracker states that the "majority of Spark workloads are not bottlenecked by I/O or network, but rather CPU and memory" [20]. In this work, we address the CPU and memory performance bottlenecks that exist in Apache Spark by accelerating user-written computational kernels using accelerators. We refer to our approach as Spark With Accelerated Tasks (SWAT). SWAT is an accelerated data analytics (ADA) framework that enables programmers to natively execute Spark applications on high performance hardware platforms with co-processors, while continuing to write their applications in a JVM-based language like Java or Scala. Runtime code generation creates OpenCL kernels from JVM bytecode, which are then executed on OpenCL accelerators. In our work we emphasize 1) full compatibility with a modern, existing, and accepted data analytics platform, 2) an asynchronous, event-driven, and resource-aware runtime, 3) multi-GPU memory management and caching, and 4) ease-of-use and programmability. Ou

关键词： Graphics processing unit

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