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Controlled vs. Automatic Processing: A Graph-Theoretic Approach to the Analysis of Serial vs. parallel Processing in Neural Network Architectures 38

Controlled vs. Automatic Processing: A Graph-Theoretic Appro...

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38th Annual Meeting of the Cognitive Science Society: Recognizing and Representing Events, CogSci 2016

作者： Musslick, Sebastian Dey, Biswadip Özcimder, Kayhan Patwary, Md. Mostofa Ali Willke, Theodore L. Cohen, Jonathan D. Princeton Neuroscience Institute Princeton University PrincetonNJ08544 United States Department of Mechanical and Aerospace Engineering Princeton University PrincetonNJ08544 United States Parallel Computing Lab Intel Corporation Santa ClaraCA95054 United States

ISBN: (纸本)9780991196739

The limited ability to simultaneously perform multiple tasks is one of the most salient features of human performance and a defining characteristic of controlled processing. Based on the assumption that multitasking constraints arise from shared representations between individual tasks, we describe a graph-theoretic approach to analyze these constraints. Our results are consistent with previous numerical work (Feng, Schwemmer, Gershman, & Cohen, 2014), showing that even modest amounts of shared representation induce dramatic constraints on the parallel processing capability of a network architecture. We further illustrate how this analysis method can be applied to specific neural networks to efficiently characterize the full profile of their parallel processing capabilities. We present simulation results that validate theoretical predictions, and discuss how these methods can be applied to empirical studies of controlled vs. and automatic processing and multitasking performance in humans. © 2016 Proceedings of the 38th Annual Meeting of the Cognitive Science Society, CogSci 2016. All rights reserved.

关键词： Multitasking

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Real-time full correlation matrix analysis of fMRI data

Real-time full correlation matrix analysis of fMRI data

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IEEE International Conference on Big Data

作者： Yida Wang Bryn Keller Mihai Capota Michael J. Anderson Narayanan Sundaram Jonathan D. Cohen Kai Li Nicholas B. Turk-Browne Theodore L. Willke Parallel Computing Lab Intel Corporation Princeton Neuroscience Institute Princeton University Department of Computer Science Princeton University

ISBN: (纸本)9781467390064

Real-time functional magnetic resonance imaging (rtfMRI) is an emerging approach for studying the functioning of the human brain. Computational challenges combined with high data velocity have to this point restricted rtfMRI analyses to studying regions of the brain independently. However, given that neural processing is accomplished via functional interactions among brain regions, neuroscience could stand to benefit from rtfMRI analyses of full-brain interactions. In this paper, we extend such an offline analysis method, full correlation matrix analysis (FCMA), to enable its use in rtfMRI studies. Specifically, we introduce algorithms capable of processing real-time data for all stages of the FCMA machine learning workflow: incremental feature selection, model updating, and real-time classification. We also present an actor-model based distributed system designed to support FCMA and other rtfMRI analysis methods. Experiments show that our system successfully analyzes a stream of brain volumes and returns neurofeedback with less than 180 ms of lag. Our real-time FCMA implementation provides the same accuracy as an optimized offline FCMA toolbox while running 3.6-6.2× faster.

关键词： Big data Machine-to-machine communications Conferences

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Enabling factor analysis on thousand-subject neuroimaging datasets

Enabling factor analysis on thousand-subject neuroimaging da...

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IEEE International Conference on Big Data

作者： Michael J. Anderson Mihai Capota Javier S. Turek Xia Zhu Theodore L. Willke Yida Wang Po-Hsuan Chen Jeremy R. Manning Peter J. Ramadge Kenneth A. Norman Parallel Computing Lab Intel Corporation Hillsboro Oregon Princeton University Princeton New Jersey Dartmouth College Hanover New Hampshire

ISBN: (纸本)9781467390064

The scale of functional magnetic resonance image data is rapidly increasing as large multi-subject datasets are becoming widely available and high-resolution scanners are adopted. The inherent low-dimensionality of the information in this data has led neuroscientists to consider factor analysis methods to extract and analyze the underlying brain activity. In this work, we consider two recent multi-subject factor analysis methods: the Shared Response Model and the Hierarchical Topographic Factor Analysis. We perform analytical, algorithmic, and code optimization to enable multi-node parallel implementations to scale. Single-node improvements result in 99χ and 2062x speedups on the two methods, and enables the processing of larger datasets. Our distributed implementations show strong scaling of 3.3x and 5.5χ respectively with 20 nodes on real datasets. We demonstrate weak scaling on a synthetic dataset with 1024 subjects, equivalent in size to the biggest fMRI dataset collected until now, on up to 1024 nodes and 32,768 cores.

关键词： Optimization Computational modeling Neuroimaging Brain modeling Algorithm design and analysis Data models

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Using behavior to decode allocation of attention in context dependent decision making 14

Using behavior to decode allocation of attention in context ...

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14th International Conference on Cognitive Modeling, ICCM 2016

作者： Shvartsman, Michael Srivastava, Vaibhav Sundaram, Narayanan Cohen, Jonathan D. Princeton Neuroscience Institute Washington Rd. PrincetonNJ08544 United States Department of Mechanical and Aerospace Engineering 41 Olden St. PrincetonNJ08544 United States Intel Parallel Computing Lab 2200 Mission College Blvd. Santa ClaraCA95054 United States

ISBN: (纸本)9780998508207

We present a model of the dynamics of adaptive attention allocation in the AX Continuous Performance Test (AX-CPT), a simple context dependent decision making task of interest to the research communities concerned with cognitive control, schizophrenia, anxiety and aging (Braver et al., 2001;Cohen et al., 1999;Eysenck et al., 2007). We ground it in our recent theory of decision making under dynamic context, that assumes humans use sequential Bayesian inference to combine information from multiple sources in perception and (optionally) memory over time. The theory generalizes the well-known diffusion decision model of single-stimulus decision making (DDM;Ratcliff, 1978). Our first result is a new analysis that shows how memory encoding and retention can yield a variable initial condition for either a single- or multiple-stimulus decision, providing a theoretical grounding to the assumption of variability in initial condition previously shown to improve data fits for the DDM. Our second result is using this model to decode attention allocation from behavioral data in a novel quantitative payoff manipulation in the AX-CPT, showing that our model can capture the differences in how subjects encode and retrieve contextual information when the relative emphasis on task speed and accuracy is changed. Copyright © TRECVID *** rights reserved.

关键词： Decision making

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MILC staggered conjugate gradient performance on intel KNL 34

MILC staggered conjugate gradient performance on Intel KNL

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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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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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Using the parallel Research Kernels to Study PGAS Models

Using the Parallel Research Kernels to Study PGAS Models

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International Conference on Partitioned Global Address Space Programming Models (PGAS)

作者： Rob F. Van Der Wijngaart Srinivas Sridharan Abdullah Kayi Gabriele Jost Jeff R. Hammond Timothy G. Mattson Jacob E. Nelson University of Washington Seattle WA US Intel Corp. Parallel Computing Lab USA Intel Corp. Parallel Computing Lab University of Washington

A subset of the parallel Research Kernels (PRK),simplified parallel application patterns, are used to study the behavior of different runtimes implementing the PGAS programming model. The goal of this paper is to show that such an approach is practical and effective as we approach the exascale era. Our experimental results indicate that forthe kernels we selected, MPI with two-sided communications outperforms the PGAS runtimes SHMEM, UPC, Grappa, and MPI-3 with RMA extensions.

关键词： Kernel Runtime Synchronization Electronics packaging Programming Bandwidth Protocols

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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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Exploring Shared-Memory Optimizations for an Unstructured Mesh CFD Application on Modern parallel Systems

Exploring Shared-Memory Optimizations for an Unstructured Me...

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International Symposium on parallel and Distributed Processing (IPDPS)

作者： Dheevatsa Mudigere Srinivas Sridharan Anand Deshpande Jongsoo Park Alexander Heinecke Mikhail Smelyanskiy Bharat Kaul Pradeep Dubey Dinesh Kaushik David Keyes Parallel Computing Lab Intel Corporation Bangalore India Parallel Computing Lab Intel Corporation Santa Clara CA Qatar Foundation Doha Qatar King Abdullah University of Science and Technology Thuwal Saudi Arabia

ISBN: (纸本)9781479986507

In this work, we revisit the 1999 Gordon Bell Prize winning PETSc-FUN3D aerodynamics code, extending it with highly-tuned shared-memory parallelization and detailed performance analysis on modern highly parallel architectures. An unstructured-grid implicit flow solver, which forms the backbone of computational aerodynamics, poses particular challenges due to its large irregular working sets, unstructured memory accesses, and variable/limited amount of parallelism. This code, based on a domain decomposition approach, exposes tradeoffs between the number of threads assigned to each MPI-rank sub domain, and the total number of domains. By applying several algorithm- and architecture-aware optimization techniques for unstructured grids, we show a 6.9X speed-up in performance on a single-node intel® XeonTM1 E5 2690 v2 processor relative to the out-of-the-box compilation. Our scaling studies on TACC Stampede supercomputer show that our optimizations continue to provide performance benefits over baseline implementation as we scale up to 256 nodes.

关键词： Optimization Kernel parallel processing Jacobian matrices Computational fluid dynamics Instruction sets Sparse matrices

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