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ACM/IEEE International Conference for High Performance computing, Networking, Storage, and Analysis

作者： Jatin Chhugani Changkyu Kim Hemant Shukla Jongsoo Park Pradeep Dubey John Shalf Horst D. Simon Parallel Computing Lab Intel Corporation Lawrence Berkeley National Laboratory

ISBN: (纸本)9781467308052

Two-point Correlation Function (TPCF) is widely used in astronomy to characterize the distribution of matter/energy in the Universe, and help derive the physics that can trace back to the creation of the universe. However, it is prohibitively slow for current sized datasets, and would continue to be a critical bottleneck with the trend of increasing dataset sizes to billions of particles and more, which makes TPCF a compelling benchmark application for future exa-scale architectures. State-of-the-art TPCF implementations do not map well to the underlying SIMD hardware, and also suffer from load-imbalance for large core counts. In this paper, we present a novel SIMD-friendly histogram update algorithm that exploits the spatial locality of histogram updates to achieve near-linear SIMD scaling. We also present a load-balancing scheme that combines domain-specific initial static division of work and dynamic task migration across nodes to effectively balance computation across nodes. Using Zin supercomputer at Lawrence Livermore National laboratory (25,600 cores of Intel? Xeon? E5-2670, each with 256-bit SIMD), we achieve 90% parallel efficiency and 96% SIMD efficiency, and perform TPCF computation on a 1.7 billion particle dataset in 5.3 hours (at least 35×faster than previous approaches). In terms of cost per performance (measured in flops/$), we achieve at least an order-of-magnitude (11.1 ×) higher flops/$ as compared to the best known results [1]. Consequently, we now have line-of-sight to achieving the processing power for correlation computation to process billion+ particles telescopic data.

关键词： TPCF universe trend of increasing dataset sizes Universe single instruction multiple datastream histograms spatial locality Ganglia Lawrence Livermore National laboratory Dataset ASTRONOMY particles arithmetic High Performance computing

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Deep graph library optimizations for intel® x86 architecture

arXiv

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arXiv 2020年

作者： Avancha, Sasikanth Md, Vasimuddin Mohanty, Ramanarayan Misra, Sanchit Parallel Computing Lab Intel Labs Intel Corporation Bangalore India

The Deep Graph Library (DGL) was designed as a tool to enable structure learning from graphs, by supporting a core abstraction for graphs, including the popular Graph Neural Networks (GNN). DGL contains implementations of all core graph operations for both the CPU and GPU. In this paper, we focus specifically on CPU implementations and present performance analysis, optimizations and results across a set of GNN applications using the latest version of DGL (0.4.3). Across 7 applications, we achieve speed-ups ranging from 1.5×-13× over the baseline CPU implementations. Copyright © 2020, The Authors. All rights reserved.

关键词： Graph neural networks

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Matrix-normal models for fMRI analysis

arXiv

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arXiv 2017年

作者： Shvartsman, Michael Sundaram, Narayanan Aoi, Mikio C. Charles, Adam Wilke, Theodore L. Cohen, Jonathan D. Princeton Neuroscience Institute Princeton University Parallel Computing Lab Intel Corporation

Multivariate analysis of fMRI data has benefited sub-stantially from advances in machine learning. Most recently, a range of probabilistic latent variable models applied to fMRI data have been successful in a variety of tasks, including identifying similarity patterns in neural data (Representational Similarity Analysis and its empirical Bayes variant, RSA and BRSA;Inter-subject Functional Connectivity, ISFC), combining multi-subject datasets (Shared Response Mapping;SRM), and mapping between brain and behavior (Si-multaneous Modeling). Although these methods share some underpinnings, they have been developed as dis-tinct methods, with distinct algorithms and software tools. We show how the matrix-variate normal (MN) formalism can unify some of these methods into a single framework. In doing so, we gain the ability to reuse noise modeling assumptions, algorithms, and code across models. Our primary theoretical con-tribution shows how some of these methods can be written as instantiations of the same model, allow-ing us to generalize them to flexibly modeling struc-tured noise covariances. Our formalism permits novel model variants and improved estimation strategies: in contrast to SRM, the number of parameters for MN-SRM does not scale with the number of voxels or subjects;in contrast to BRSA, the number of parameters for MN-RSA scales additively rather than multiplicatively in the number of voxels. We empir-ically demonstrate advantages of two new methods derived in the formalism: for MN-RSA, we show up to 10x improvement in runtime, up to 6x improve-ment in RMSE, and more conservative behavior under the null. For MN-SRM, our method grants a modest improvement to out-of-sample reconstruction while re-laxing an orthonormality constraint of SRM. We also provide a software prototyping tool for MN models that can flexibly reuse noise covariance assumptions and algorithms across models. Copyright © 2017, The Authors. All rights reserved.

关键词： Software prototyping

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A prompt-based approach to adversarial example generation and robustness enhancement

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Frontiers of Computer Science 2024年第4期18卷 85-96页

作者： Yuting YANG Pei HUANG Juan CAO Jintao LI Yun LIN Feifei MA Key Lab of Intelligent Information Processing of Chinese Academy of Sciences(CAS) Institute of Computing TechnologyCASBeijing 100190China School of Computer Science and Technology University of Chinese Academy of SciencesBeijing 100049China Department of Computer Science Stanford UniversityCA 94305USA School of Computing National University of SingaporeSingapore 119077Singapore Laboratory of Parallel Software and Computational Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China

Recent years have seen the wide application of natural language processing(NLP)models in crucial areas such as finance,medical treatment,and news media,raising concerns about the model robustness and *** find that prompt paradigm can probe special robust defects of pre-trained language *** prompt texts are first constructed for inputs and a pre-trained language model can generate adversarial examples for victim models via *** results show that prompt paradigm can efficiently generate more diverse adversarial examples besides synonym ***,we propose a novel robust training approach based on prompt paradigm which incorporates prompt texts as the alternatives to adversarial examples and enhances robustness under a lightweight minimax-style optimization *** on three real-world tasks and two deep neural models show that our approach can significantly improve the robustness of models to resist adversarial attacks.

关键词： robustness adversarial example prompt learning pre-trained language model

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Next Generation Cloud computing Reliability:Issues and Challenges

Next Generation Cloud Computing Reliability:Issues and Chall...

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The 7th International Symposium on parallel Architectures, Algorithms and Programming, PAAP2015(2015年第七届国际并行体系结构,算法和编程研讨会)

作者： Jemal Abawajy Parallel and Distributed Computing Lab School of Information Technology Deakin University Australia

Introduction Cloud computing is widely recognized for its cost savings,scaling and for accelerating business innovation.

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RAIL: Risk-Averse Imitation Learning

arXiv

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arXiv 2017年

作者： Santara, Anirban Naik, Abhishek Ravindran, Balaraman Das, Dipankar Mudigere, Dheevatsa Avancha, Sasikanth Kaul, Bharat IIT Kharagpur IIT Madras Parallel Computing Lab-Intel Labs India

Imitation learning algorithms learn viable policies by imitating an expert's behavior when reward signals are not available. Generative Adversarial Imitation Learning (GAIL) is a state-of-the-art algorithm for learning policies when the expert's behavior is available as a fixed set of trajectories. We evaluate in terms of the expert's cost function and observe that the distribution of trajectory-costs is often more heavy-tailed for GAIL-agents than the expert at a number of benchmark continuous-control tasks. Thus, high-cost trajectories, corresponding to tail-end events of catastrophic failure, are more likely to be encountered by the GAILagents than the expert. This makes the reliability of GAIL-agents questionable when it comes to deployment in risk-sensitive applications like robotic surgery and autonomous driving. In this work, we aim to minimize the occurrence of tail-end events by minimizing tail risk within the GAIL framework. We quantify tail risk by the Conditional-Value-at-Risk (CV aR) of trajectories and develop the Risk-Averse Imitation Learning (RAIL) algorithm. We observe that the policies learned with RAIL show lower tail-end risk than those of vanilla GAIL. Thus the proposed RAIL algorithm appears as a potent alternative to GAIL for improved reliability in risk-sensitive applications. Copyright © 2017, The Authors. All rights reserved.

关键词： Trajectories

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Performance evaluation of allgather algorithms on terascale Linux cluster with Fast Ethernet

Performance evaluation of allgather algorithms on terascale ...

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8th International Conference on High-Performance computing in Asia-Pacific Region

作者： Chen, Jing Zhang, Linbo Zhang, Yunquan Yuan, Wei Department of Computer Science University of Science and Technology of China China Lab. of Parallel Computing Institute of Software CAS China State Key Laboratory of Computer Science China Academy of Mathematics and Systems Sciences CAS China

ISBN: (纸本)0769524869

We report our work on evaluating performance of several MPI Allgather algorithms on Fast Ethernet. These algorithms are ring, recursive doubling, Bruck, and neighbor exchange. The first three algorithms are widely used today. The neighbor exchange algorithm which was recently proposed by the authors incorporates pair-wise exchange, and is expected to perform better with certain configurations, mainly when using TCP/IP over Ethernet. We tested the four algorithms on terascale Linux clusters DeepComp 6800 and DAWNING 4000A using TCP/IP over Fast Ethernet. Results show that our neighbor exchange algorithm performs the best for long messages, the ring algorithm performs the best for medium-size messages and the recursive doubling algorithm performs the best for short messages.

关键词： Computer networks

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RedLib: Nestable Reductions for Collections in Java

RedLib: Nestable Reductions for Collections in Java

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IEEE International Conference on High Performance computing and Communications

作者： Mostafa Mehrabi Xing Fan Nasser Giacaman Oliver Sinnen Department of Electrical and Computer Engineering The University of Auckland Parallel and Reconfigurable Computing Lab

ISBN: (纸本)9781509042982

A reduction is a parallel programming mechanism for combining two or more elements into one. Many parallel programming languages, tools and frameworks (e.g., OpenMP, MPI, etc.) directly support simple forms of reductions (e.g., building a total sum out of partial sums). Some of those tools and frameworks allow more complex reductions to be implemented as custom reductions. However, the success of network-based application frameworks like Hadoop have shown that there is a strong need for reductions of aggregate data structures, such as the union of sets or maps. Usually parallel programming frameworks on shared-memory systems do not support these types of complex reductions directly, and a user needs to implement them manually. To address the gap thereof, this paper proposes an object-oriented reduction framework that supports reductions of aggregate types, and proposes the nesting of reduction objects for flexible extensions of reduction operations. Based on the proposed framework, a reduction library (RedLib) has been developed for Java with direct support for many common reduction operations on collections and maps. Furthermore, the paper studies the usage of the framework for common and complex cases and evaluates its performance, based on operations found in standard benchmarks.

关键词： object oriented reductions shared memory reductions nestable reductions aggregate reductions collections maps :

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Scene-based Audio Implemented with Higher Order Ambisonics

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SMPTE Motion Imaging Journal 2016年第9期125卷 16-24页

作者： Peters, Nils Sen, Deep Kim, Moo-Young Wuebbolt, Oliver Weiss, S. Merrill School of Electrical Engineering University of New South Wales Australia Royal Institute of Technology Stockholm Sweden Parallel Computing Lab UC Berkeley CA United States Electronic Media Technology Electronic Media Technology Management United States Electrical Engineering University of Hannover Hannover Germany

Scene-based audio uses a sound-field technology called 'higher-order ambisonics' (HOA) to create holistic descriptions of both live-captured and artistically created sound scenes that are independent of specific loudspeaker layouts. For efficient representation, the audio can be carried as a set of PCM channels that contain predominant sounds and ambience in separate tracks. Standard audio bandwidth-compression techniques can then be applied to the PCM channels. This approach is in contrast to conventional channel-based sound representations in which one signal is used for each loudspeaker of a target reproduction system, with the implication that upmixing or downmixing is required when loudspeaker configurations other than the intended one are used for actual reproduction. This paper examines how, with scene-based audio, there can be satisfactory reproduction of immersive sound at bitrates corresponding to the equivalent of only six channels, whereas an alternative sound-field method that exclusively employs audio objects typically involves much higher bitrates. © 2002 Society of Motion Picture and Television Engineers, Inc.

关键词： Loudspeakers

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Three dimensional and thermal transient analysis of sedimentary rocks

Three dimensional and thermal transient analysis of sediment...

引用

Eighth International Conference on Advanced Computational Methods in Heat Transfer, HEAT TRANSFER VIII

作者： King, V.P.S. Landau, L. Alves, J.L.D. Martins, M.A.D. Lab. for Compl. Methods in Eng. COPPE Fed. University of Rio de Janeiro Brazil Centre for Parallel Computing COPPE Federal University of Rio de Janeiro Brazil

This work presents the implementation of a finite element code for the thermal transient analysis of sedimentary rocks. This analysis provides a means to assess the thermal history in sedimentary rocks, assisting the studies of hydrocarbon generation and migration processes. The unstructured 3D mesh of tetrahedral elements provides the discrete model. An iterative driver using an edge-by-edge data structure solves the resulting system of equations at each time step. This strategy provides considerable improvements in terms of CPU time and memory demand. This combination enables the study of complex 3D problems, a key issue to basin analysis.

关键词： Sedimentary rocks

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