检索结果-内蒙古大学图书馆

Two-step approach to scheduling quantum circuits

学校读者我要写书评

暂无评论

arXiv 2017年

作者： Guerreschi, Gian Giacomo Park, Jongsoo Parallel Computing Lab Intel Corporation

As the effort to scale up existing quantum hardware proceeds, it becomes necessary to schedule quantum gates in a way that minimizes the number of operations. There are three constraints that have to be satisfied: the order or dependency of the quantum gates in the specific algorithm, the fact that any qubit may be involved in at most one gate at a time, and the restriction that two-qubit gates are implementable only between connected qubits. The last aspect implies that the compilation depends not only on the algorithm, but also on hardware properties like connectivity. Here we suggest a two-step approach in which logical gates are initially scheduled neglecting connectivity considerations, while routing operations are added at a later step in a way that minimizes their overhead. We rephrase the subtasks of gate scheduling in terms of graph problems like edge-coloring and maximum subgraph isomorphism. While this approach is general, we specialize to a one dimensional array of qubits to propose a routing scheme that is minimal in the number of exchange operations. As a practical application, we schedule the Quantum Approximate Optimization Algorithm in a linear geometry and quantify the reduction in the number of gates and circuit depth that results from increasing the efficacy of the scheduling strategies. Copyright © 2017, The Authors. All rights reserved.

关键词： Logic gates

Bridging the gap between HPC and big data frameworks 43rd

学校读者我要写书评

暂无评论

Bridging the gap between HPC and big data frameworks

43rd International Conference on Very Large Data Bases, VLDB 2017

作者： Anderson, Michael Smith, Shaden Sundaram, Narayanan Capotă, Mihai Zhao, Zheguang Dulloor, Subramanya Satish, Nadathur Willke, Theodore L. Parallel Computing Lab United States University of Minnesota United States Brown University United States Infrastructure Research Lab Intel Corporation United States

Apache Spark is a popular framework for data analytics with attractive features such as fault tolerance and interoperability with the Hadoop ecosystem. Unfortunately, many analytics operations in Spark are an order of magnitude or more slower compared to native implementations written with high performance computing tools such as MPI. There is a need to bridge the performance gap while retaining the benefits of the Spark ecosystem such as availability, productivity, and fault tolerance. In this paper, we propose a system for integrating MPI with Spark and analyze the costs and benefits of doing so for four distributed graph and machine learning applications. We show that offloading computation to an MPI environment from within Spark provides 3.1-17.7 × speedups on the four sparse applications, including all of the overheads. This opens up an avenue to reuse existing MPI libraries in Spark with little effort. © 2017 VLDB Endowment.

关键词： Fault tolerance

Mixed low-precision deep learning inference using dynamic fixed point

学校读者我要写书评

暂无评论

arXiv 2017年

作者： Mellempudi, Naveen Kundu, Abhisek Das, Dipankar Mudigere, Dheevatsa Kaul, Bharat Parallel Computing Lab Intel Labs Bangalore India

We propose a cluster-based quantization method to convert pre-trained full precision weights into ternary weights with minimal impact on the accuracy. In addition we also constrain the activations to 8-bits thus enabling sub 8-bit full integer inference pipeline. Our method uses smaller clusters of N filters with a common scaling factor to minimize the quantization loss, while also maximizing the number of ternary operations. We show that with cluster size of N=4 on Resnet-101, can achieve 71.8% TOP-1 accuracy, within 6% of the best full precision result, while replacing ≈ 85% of all multiplications with 8-bit accumulations. Using the same method with 4-bit weights achieves 76.3% TOP-1 accuracy which within 2% of the full precision result. We also study the impact of the size of the cluster on both performance and accuracy, larger cluster sizes N=64 can replace ≈ 98% of the multiplications with ternary operations but introduces significant drop in accuracy which necessitates fine tuning the parameters with retraining the network at lower precision. To address this we have also trained low-precision Resnet-50 with 8-bit activations and ternary weights by pre-initializing the network with full precision weights and achieve 68.9% TOP-1 accuracy within 4 additional epochs. Our final quantized model can run on a full 8-bit compute pipeline, with a potential 16x improvement in performance compared to baseline full-precision models. Copyright © 2017, The Authors. All rights reserved.

关键词： Chemical activation

Cataloging the visible universe through bayesian inference at petascale

学校读者我要写书评

暂无评论

arXiv 2018年

作者： Regier, Jeffrey Pamnany, Kiran Fischer, Keno Noack, Andreas Lam, Maximilian Revels, Jarrett Howard, Steve Giordano, Ryan Schlegel, David McAuliffe, Jon Thomas, Rollin Prabhat Department of Electrical Engineering and Computer Sciences University of California Berkeley United States Parallel Computing Lab Intel Corporation Julia Computing Computer Science and AI Laboratories Massachusetts Institute of Technology Department of Statistics University of California Berkeley United States Lawrence Berkeley National Laboratory

Astronomical catalogs derived from wide-field imaging surveys are an important tool for understanding the Universe. We construct an astronomical catalog from 55 TB of imaging data using Celeste, a Bayesian variational inference code written entirely in the high-productivity programming language Julia. Using over 1.3 million threads on 650,000 intel Xeon Phi cores of the Cori Phase II supercomputer, Celeste achieves a peak rate of 1.54 DP PFLOP/s. Celeste is able to jointly optimize parameters for 188M stars and galaxies, loading and processing 178 TB across 8192 nodes in 14.6 minutes. To achieve this, Celeste exploits parallelism at multiple levels (cluster, node, and thread) and accelerates I/O through Cori’s Burst Buffer. Julia’s native performance enables Celeste to employ high-level constructs without resorting to handwritten or generated low-level code (C/C++/Fortran), and yet achieve petascale *** Codes 85A35, 68W10, 62P35 Copyright © 2018, The Authors. All rights reserved.

关键词： Inference engines

Matrix-normal models for fMRI analysis

学校读者我要写书评

暂无评论

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

RAIL: Risk-Averse Imitation Learning

学校读者我要写书评

暂无评论

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

Assessing Trustworthy AI in Times of COVID-19: Deep Learning for Predicting a Multiregional Score Conveying the Degree of Lung Compromise in COVID-19 Patients

学校读者我要写书评

暂无评论

IEEE Transactions on Technology and Society

IEEE Transactions on Technology and Society 2022年第4期3卷 272-289页

作者： Allahabadi, Himanshi Amann, Julia Balot, Isabelle Beretta, Andrea Binkley, Charles Bozenhard, Jonas Bruneault, Frederick Brusseau, James Candemir, Sema Cappellini, Luca Alessandro Chakraborty, Subrata Cherciu, Nicoleta Cociancig, Christina Coffee, Megan Ek, Irene Espinosa-Leal, Leonardo Farina, Davide Fieux-Castagnet, Genevieve Frauenfelder, Thomas Gallucci, Alessio Giuliani, Guya Golda, Adam Van Halem, Irmhild Hildt, Elisabeth Holm, Sune Kararigas, Georgios Krier, Sebastien A. Kuhne, Ulrich Lizzi, Francesca Madai, Vince I. Markus, Aniek F. Masis, Serg Mathez, Emilie Wiinblad Mureddu, Francesco Neri, Emanuele Osika, Walter Ozols, Matiss Panigutti, Cecilia Parent, Brendan Pratesi, Francesca Moreno-Sanchez, Pedro A. Sartor, Giovanni Savardi, Mattia Signoroni, Alberto Sormunen, Hanna-Maria Spezzatti, Andy Srivastava, Adarsh Stephansen, Annette F. Theng, Lau Bee Tithi, Jesmin Jahan Tuominen, Jarno Umbrello, Steven Vaccher, Filippo Vetter, Dennis Westerlund, Magnus Wurth, Renee Zicari, Roberto V. Ey Netherlands Enterprise Intelligence Department Amsterdam1083 HP Netherlands Eth Zurich Health Ethics and Policy Lab Department of Health Sciences and Technology Zürich8092 Switzerland Center for Diplomatic and Strategic Studies Postgraduate Studies in Diplomacy and International Relations Paris75015 France Pisa56124 Italy Hackensack Meridian Health Bioethics Center EdisonNJ08820 United States University of Oxford Faculty of Philosophy OxfordOX2 6GG United Kingdom Collège André- Laurendeau Philosophie Department MontrealQCH8N 2J4 Canada Université du Québec À Montréal École des Médias MontrealQCH2L 2C4 Canada Pace University Philosophy Department New YorkNY10038 United States The Ohio State University Wexner Medical Center Department of Radiology ColumbusOH43210 United States Humanitas Research Hospital Department of Radiology Milan20089 Italy Humanitas University Department of Biomedical Sciences Milan20089 Italy University of New England Faculty of Science Agriculture Business and Law ArmidaleNSW2351 Australia University of Technology Sydney Faculty of Engineering and Information Technology SydneyNSW2007 Australia Scuola Superiore Sant'Anna European Centre of Excellence on the Regulation of Robotics and Ai Pisa56127 Italy University of Bremen Group of Computer Architecture Bremen28359 Germany New York University Grossman School of Medicine Division of Infectious Diseases and Immunology Department of Medicine New YorkNY10016 United States Digital Institute Ai Research Section Stockholm16731 Sweden Arcada University of Applied Sciences Department of Business Management and Analytics Helsinki00550 Finland University of Brescia Radiological Sciences and Public Health Department of Medical and Surgical Specialties Brescia25121 Italy Sncf Reseau Sa Ethique Groupe La Plaine93418 France Institute of Diagnostic and Interventional Radiology University Hospital Zurich Zürich8091 Switzerland Eindhoven University of Tech

This article's main contributions are twofold: 1) to demonstrate how to apply the general European Union's High-Level Expert Group's (EU HLEG) guidelines for trustworthy AI in practice for the domain of healthcare and 2) to investigate the research question of what does 'trustworthy AI' mean at the time of the COVID-19 pandemic. To this end, we present the results of a post-hoc self-assessment to evaluate the trustworthiness of an AI system for predicting a multiregional score conveying the degree of lung compromise in COVID-19 patients, developed and verified by an interdisciplinary team with members from academia, public hospitals, and industry in time of pandemic. The AI system aims to help radiologists to estimate and communicate the severity of damage in a patient's lung from Chest X-rays. It has been experimentally deployed in the radiology department of the ASST Spedali Civili clinic in Brescia, Italy, since December 2020 during pandemic time. The methodology we have applied for our post-hoc assessment, called Z-Inspection®, uses sociotechnical scenarios to identify ethical, technical, and domain-specific issues in the use of the AI system in the context of the pandemic. © 2020 IEEE.

关键词： COVID-19

Eliminating Irregularities of Protein Sequence Search on Multicore Architectures

学校读者我要写书评

暂无评论

Eliminating Irregularities of Protein Sequence Search on Mul...

International Symposium on parallel and Distributed Processing (IPDPS)

作者： Jing Zhang Sanchit Misra Hao Wang Wu-chun Feng Dept. of Computer Science Virginia Tech Parallel Computing Lab Intel Corporation

Finding regions of local similarity between biological sequences is a fundamental task in computational biology. BLAST is the most widely-used tool for this purpose, but it suffers from irregularities due to its heuristic nature. To achieve fast search, recent approaches construct the index from the database instead of the input query. However, database indexing introduces more challenges in the design of index structure and algorithm, especially for data access through the memory hierarchy on modern multicore processors. In this paper, based on existing heuristic algorithms, we design and develop a database indexed BLAST with the identical sensitivity as query indexed BLAST (i.e., NCBI-BLAST). Then, we identify that existing heuristic algorithms of BLAST can result in serious irregularities in database indexed search. To eliminate irregularities in BLAST algorithm, we propose muBLASTP, that uses multiple optimizations to improve data locality and parallel efficiency for multicore architectures and multi-node systems. Experiments on a single node demonstrate up to a 5.1-fold speedup over the multi-threaded NCBI BLAST. For the inter-node parallelism, we achieve nearly linear scaling on up to 128 nodes and gain up to 8.9-fold speedup over mpiBLAST.

关键词： Indexes Multicore processing Heuristic algorithms Protein sequence Tools

Sparse Tensor Factorization on Many-Core Processors with High-Bandwidth Memory

学校读者我要写书评

暂无评论

Sparse Tensor Factorization on Many-Core Processors with Hig...

International Symposium on parallel and Distributed Processing (IPDPS)

作者： Shaden Smith Jongsoo Park George Karypis Department of Computer Science and Engineering University of Minnesota Parallel Computing Lab Intel Corporation

HPC systems are increasingly used for data intensive computations which exhibit irregular memory accesses, non-uniform work distributions, large memory footprints, and high memory bandwidth demands. To address these challenging demands, HPC systems are turning to many-core architectures that feature a large number of energy-efficient cores backed by high-bandwidth memory. These features are exemplified in intel's recent Knights Landing many-core processor (KNL), which typically has 68 cores and 16GB of on-package multi-channel DRAM (MCDRAM). This work investigates how the novel architectural features offered by KNL can be used in the context of decomposing sparse, unstructured tensors using the canonical polyadic decomposition (CPD). The CPD is used extensively to analyze large multi-way datasets arising in various areas including precision healthcare, cybersecurity, and e-commerce. Towards this end, we (i) develop problem decompositions for the CPD which are amenable to hundreds of concurrent threads while maintaining load balance and low synchronization costs; and (ii) explore the utilization of architectural features such as MCDRAM. Using one KNL processor, our algorithm achieves up to 1.8x speedup over a dual socket intel Xeon system with 44 cores.

关键词： Tensile stress Instruction sets Synchronization Sparse matrices parallel processing Bandwidth