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34th Annual International Symposium on Lattice Field Theory, LATTICE 2016

作者： Li, Ruizi DeTar, Carleton Doerfler, Douglas Gottlieb, Steven Jha, Ashish Kalamkar, Dhiraj Toussaint, Doug Department of Physics Indiana University BloomingtonIN47405 United States Department of Physics and Astronomy University of Utah Salt Lake CityUT84112 United States National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA94720 United States Software and Services Group Intel Corporation HillsboroOR97124 United States Parallel Computing Lab Intel Labs Bangalore560103 India Physics Department University of Arizona TucsonAZ85721 United States

We review our work done to optimize the staggered conjugate gradient (CG) algorithm in the MILC code for use with the Intel Knights Landing (KNL) architecture. KNL is the second generation Intel Xeon Phi processor. It is capable of massive thread parallelism, data parallelism, and high on-board memory bandwidth and is being adopted in supercomputing centers for scientific research. The CG solver consumes the majority of time in production running, so we have spent most of our effort on it. We compare performance of an MPI+OpenMP baseline version of the MILC code with a version incorporating the QPhiX staggered CG solver, for both one-node and multi-node runs. © Copyright owned by the author(s).

关键词： Application programming interfaces (API)

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Preface

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International Journal of High Performance computing and Networking 2017年第1-2期10卷 1-2页

作者： Vijayakumar, V. Anderst-Kotsis, Gabriele Cai, Zhipeng Abawajy, Jemal H. School of Computing Science and Engineering VIT University Chennai600127 India Department of Telecooperation Johannes Kepler Universität AltenbergerStrasse 69 Linz4040 Austria Department of Computer Science Georgia State University AtlantaGA30303 United States Parallel and Distributed Computing Lab. Deakin University GeelongVIC3220 Australia

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GB-RC4: Effective brute force attacks on RC4 algorithm using GPU 7

GB-RC4: Effective brute force attacks on RC4 algorithm using...

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7th International Green and Sustainable computing Conference, IGSC 2016

作者： Xue, Pei Li, Tao Dong, Han Liu, Chunbo Ma, Wenjing Pei, Songwen College of Computer and Control Engineering Nankai University Tianjin300071 China Information Security Evaluation Center Civil Aviation University of China Tianjin300300 China Laboratory of Parallel Software and Computational Science State Key Laboratory of Computing Science Institute of Software CAS Beijing China Shanghai Key Lab of Modern Optical Systems Department of Computer Science and Engineering University of Shanghai for Science and Technology Shanghai200093 China

ISBN: (纸本)9781509051175

Encryption algorithms are applied to a variety of fields and the security of encryption algorithms depends heavily on the computational infeasibility of exhaustive key-space search. RC4 algorithm has an extensive application for stream encryption, however, the disadvantages of traditional RC4 serial algorithm are large computational quantity and slow computation speed, which means a great challenge for throughput of computing system. Graphics processors recently have been widely used as accelerators for high throughput computing. In this paper, GPU is used to improve the efficiency and performance of attacking RC4 algorithm. First, we revised RC4 algorithm to make it easier to parallelize on a GPU. Then we take advantage of constant memory and shared memory in GPU to search the key space of RC4 algorithm efficiently. Besides, we solved the bank conflict problem in shared memory to further improve the performance. Our approach was evaluated in the whole key space of 40-bit, and the results show it can complete searching of the whole key space within 12.8 hours. © 2016 IEEE.

关键词： Graphics processing unit

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A proposal to OpenMP for addressing the CPU oversubscription challenge

Lecture Notes in Computer Science (including subseries Lectu...

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Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) 2016年 9903 LNCS卷 187-202页

作者： Yan, Yonghong Hammond, Jeff R. Liao, Chunhua Eichenberger, Alexandre E. Department of Computer Science and Engineering Oakland University Rochester United States Parallel Computing Lab Intel Corp. Santa Clara United States Center for Applied Scientific Computing Lawrence Livermore National Laboratory Livermore United States Thomas J. Watson Research Center IBM Yorktown Heights United States OpenMP Interoperability Language Subcommittee Houston United States

ISBN: (纸本)9783319455495

OpenMP has become a successful programming model for developing multi-threaded applications. However, there are still some challenges in terms of OpenMP’s interoperability within itself and with other parallel programming APIs. In this paper, we explore typical use cases that expose OpenMP’s interoperability challenges and report our proposed solutions for addressing the resource oversubscription issue as the efforts by the OpenMP Interoperability language subcommittee. The solutions include OpenMP runtime routines for changing the wait policies, which include ACTIVE(SPIN BUSY or SPIN PAUSE), PASSIVE (SPIN YIELD or SUSPEND), of idling threads for improved resource management, and routines for supporting contributing OpenMP threads to other thread libraries or tasks. Our initial implementations are being done by extending two OpenMP runtime libraries, Intel OpenMP (IOMP) and GNU OpenMP (GOMP). The evaluation results demonstrate the effectiveness of the proposed approach to address the CPU oversubscription challenge and detailed analysis provide heuristics for selecting an optimal wait policy according to the oversubscription ratios. © Springer International Publishing Switzerland 2016.

关键词： Interoperability

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Full correlation matrix analysis of fMRI data on Intel Xeon Phi coprocessors

Full correlation matrix analysis of fMRI data on Intel Xeon ...

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International Conference for High Performance computing, Networking, Storage and Analysis, SC 2015

作者： Wang, Yida Anderson, Michael J. Cohen, Jonathan D. Heinecke, Alexander Li, Kai Satish, Nadathur Sundaram, Narayanan Turk-Browne, Nicholas B. Willke, Theodore L. Department of Computer Science Princeton University United States Parallel Computing Lab Intel Corporation United States Princeton Neuroscience Institute Princeton University United States

ISBN: (纸本)9781450337236

Full correlation matrix analysis (FCMA) is an unbiased approach for exhaustively studying interactions among brain regions in functional magnetic resonance imaging (fMRI) data from human participants. In order to answer neuroscientific questions efficiently, we are developing a closed-loop analysis system with FCMA on a cluster of nodes with Intel Xeon Phi coprocessors. Here we propose several ideas for data-driven algorithmic modification to improve the performance on the coprocessor. Our experiments with real datasets show that the optimized single-node code runs 5x-16x faster than the baseline implementation using the well-known Intel MKL and LibSVM libraries, and that the cluster implementation achieves near linear speedup on 5760 cores. © 2015 ACM.

关键词： Coprocessor

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Improving graph partitioning for modern graphs and architectures 5

Improving graph partitioning for modern graphs and architect...

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5th Workshop on Irregular Applications: Architectures and Algorithms, IA3 2015

作者： Lasalle, Dominique Patwary, Md Mostofa Ali Satish, Nadathur Sundaram, Narayanan Dubey, Pradeep Karypis, George Department of Computer Science and Engineering University of Minnesota Minneapolis MN55455 United States Parallel Computing Lab Intel Corporation Santa ClaraCA95054 United States

ISBN: (纸本)9781450340014

Graph partitioning is an important preprocessing step in applications dealing with sparse-irregular data. As such, the ability to efficiently partition a graph in parallel is crucial to the performance of these applications. The number of compute cores in a compute node continues to increase, demanding ever more scalability from shared-memory graph partitioners. In this paper we present algorithmic improvements to the multithreaded graph partitioner mt-Metis. We experimentally evaluate our methods on a 36 core machine, using 20 difierent graphs from a variety of domains. Our improvements decrease the runtime by 1.5-11.7 and improve strong scaling by 82%.

关键词： Graph theory

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Scalable Bayesian optimization using deep neural networks 32

Scalable Bayesian optimization using deep neural networks

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32nd International Conference on Machine Learning, ICML 2015

作者： Snoek, Jasper Ripped, Oren Swersky, Kevin Kiros, Ryan Satish, Nadathur Sundaram, Narayanan Patwary, Md. Mostofa Ali Prabhat Adams, Ryan P. Harvard University School of Engineering and Applied Sciences United States Massachusetts Institute of Technology Department of Mathematics United States University of Toronto Department of Computer Science Canada Intel Labs Parallel Computing Lab Switzerland NERSC Lawrence Berkeley National Laboratory United States

ISBN: (纸本)9781510810587

Bayesian optimization is an effective methodology for the global optimization of functions with expensive evaluations. It relies on querying a distribution over functions defined by a relatively cheap surrogate model. An accurate model for this distribution over functions is critical to the effectiveness of the approach, and is typically fit using Gaussian processes (GPs). However, since GPs scale cubically with the number of observations, it has been challenging to handle objectives whose optimization requires many evaluations, and as such, massively parallelizing the optimization. In this work, we explore the use of neural networks as an alternative to GPs to model distributions over functions. We show that performing adaptive basis function regression with a neural network as the parametric form performs competitively with state-of-the-art GP-based approaches, but scales linearly with the number of data rather than cubically. This allows us to achieve a previously intractable degree of parallelism, which we apply to large scale hyperparameter optimization, rapidly finding competitive models on benchmark object recognition tasks using convolutional networks, and image caption generation using neural language models. © Copyright 2015 by International Machine Learning Society (IMLS). All rights reserved.

关键词： Global optimization

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Madness: A multiresolution, adaptive numerical environment for scientific simulation

Madness: A multiresolution, adaptive numerical environment f...

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作者： Harrison, Robert J. Beylkin, Gregory Bischoff, Florian A. Calvin, Justus A. Fann, George I. Fosso-Tande, Jacob Galindo, Diego Hammond, Jeff R. Hartman-Baker, Rebecca Hill, Judith C. Jia, Jun Kottmann, Jakob S. Ou, M-J. Yvonne Pei, Junchen Ratcliff, Laura E. Reuter, Matthew G. Richie-Halford, Adam C. Romero, Nichols A. Sekino, Hideo Shelton, William A. Sundahl, Bryan E. Thornton, W. Scott Valeev, Edward F. Vázquez-Mayagoitia, Álvaro Vence, Nicholas Yanai, Takeshi Yokoi, Yukina Stony Brook University Stony BrookNY11794 United States University of Colorado at Boulder BoulderCO80309 United States Institut für Chemie Humboldt-Universität Zu Berlin Unter den Linden 6 Berlin10099 Germany Department of Chemistry Virginia Tech. BlacksburgVA24061 United States Oak Ridge National Laboratory Oak RidgeTN37831 United States Department of Chemistry and Biochemistry Florida State University TallahasseeFL32306 United States Parallel Computing Lab Intel Corporation PortlandOR97219 United States National Energy Research Scientific Computing Center Lawrence Berkeley National Laboratory BerkeleyCA94720 United States LinkedIn Mountain ViewCA94043 United States Department of Mathematical Sciences University of Delaware NewarkDE19716 United States State Key Laboratory of Nuclear Physics and Technology School of Physics Peking University Beijing100871 China Argonne Leadership Computing Facility Argonne National Laboratory ArgonneIL60439 United States Department of Physics University of Washington SeattleWA98195 United States Computer Science and Engineering Toyohashi University of Technology ToyohashiAichi441-8580 Japan Louisiana State University Baton RougeLA70803 United States Department of Physics LaSierra University RiversideCA92505 United States Theoretical and Computational Molecular Science Institute for Molecular Science OkazakiAichi444-8585 Japan

MADNESS (multiresolution adaptive numerical environment for scientific simulation) is a high-level software environment for solving integral and differential equations in many dimensions that uses adaptive and fast harmonic analysis methods with guaranteed precision that are based on multiresolution analysis and separated representations. Underpinning the numerical capabilities is a powerful petascale parallel programming environment that aims to increase both programmer productivity and code scalability. This paper describes the features and capabilities of MADNESS and briefly discusses some current applications in chemistry and several areas of physics. © 2016 Society for Industrial and Applied Mathematics.

关键词： Multiresolution analysis

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Anatomy of High-Performance Many-Threaded Matrix Multiplication

Anatomy of High-Performance Many-Threaded Matrix Multiplicat...

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IEEE International parallel and Distributed Processing Symposium

作者： Tyler M. Smith Robert van de Geijn Mikhail Smelyanskiy Jeff R. Hammond Field G. Van Zee Institute for Computational Engineering and Sciences and Department of Computer Science The University of Texas at Austin Parallel Computing Lab Intel Corporation Leadership Computing Facility Argonne National Lab

ISBN: (纸本)9781479938018

BLIS is a new framework for rapid instantiation of the BLAS. We describe how BLIS extends the "GotoBLAS approach" to implementing matrix multiplication (GEMM). While GEMM was previously implemented as three loops around an inner kernel, BLIS exposes two additional loops within that inner kernel, casting the computation in terms of the BLIS micro-kernel so that porting GEMM becomes a matter of customizing this micro-kernel for a given architecture. We discuss how this facilitates a finer level of parallelism that greatly simplifies the multithreading of GEMM as well as additional opportunities for parallelizing multiple loops. Specifically, we show that with the advent of many-core architectures such as the IBM PowerPC A2 processor (used by Blue Gene/Q) and the Intel Xeon Phi processor, parallelizing both within and around the inner kernel, as the BLIS approach supports, is not only convenient, but also necessary for scalability. The resulting implementations deliver what we believe to be the best open source performance for these architectures, achieving both impressive performance and excellent scalability.

关键词： Linear algebra Libraries High-performance Matrix BLAS Multicore

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Versatile and scalable parallel histogram construction

Versatile and scalable parallel histogram construction

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International Conference on parallel Architecture and Compilation Techniques (PACT)

作者： Wookeun Jung Jongsoo Park Jaejin Lee Department of Computer Science and Engineering Seoul National University Seoul Korea Intel Corporation Parallel Computing Lab California USA

ISBN: (纸本)9781509066070

Histograms are used in various fields to quickly profile the distribution of a large amount of data. However, it is challenging to efficiently utilize abundant parallel resources in modern processors for histogram construction. To make matters worse, the most efficient implementation varies depending on input parameters (e.g., input distribution, number of bins, and data type) or architecture parameters (e.g., cache capacity and SIMD width). This paper presents versatile histogram methods that achiev competitive performance across a wide range of input types and target architectures. Our open source implementations are highly optimized for various cases and are scalable for more threads and wider SIMD units. We also show that histogram construction can be significantly accelerated by Intel ® Xeon Phi coprocessors for common input data sets because of their compute power from many cores and instructions for efficient vectorization, such as gather-scatter. For histograms with 256 fixed-width bins, a dual-socket 8-core Intel ® Xeon ® E5-2690 achieves 13 billion bin updates per second (GUPS), while a 60-core Intel ® Xeon Phi 5110P coprocessor achieves 18 GUPS for a skewed input. For histograms with 256 variable-width bins, the Xeon processor achieves 4.7 GUPS, while the Xeon Phi coprocessor achieves 9.7 GUPS for a skewed input. For text histogram, or word count, the Xeon processor achieves 342.4 million words per seconds (MWPS). This is 4.12×, 3.46× faster than PHOENIX and TBB. The Xeon phi processor achieves 401.4 MWPS, which is 1.17× faster than the Xeon processor. Since histogram construction captures essential characteristics of more general reduction-heavy operations, our approach can be extended to other settings.

关键词： Histograms Instruction sets Computer architecture Hardware parallel processing Coprocessors

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