Ian Foster

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Carl F Kesselman (55)
Michael Wilde (48)
Kyle Chard (46)
Steven Tuecke (33)
Ravi K Madduri (26)
Justin Michael Joseph Wozniak (24)
Rajkumar Kettimuthu (23)
Daniel S. Katz (21)
Ryan Chard (18)
Ioan Raicu (16)
Adriana Ioana Iamnitchi (15)
Matei R Ripeanu (15)
Steven Tuecke (14)
Yong Zhao (14)
Rachana Ananthakrishnan (13)
Timothy G. Armstrong (13)
Wei Tan (12)
Catalin Lucian Dumitrescu (11)
Dinanath Sulakhe (9)

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Journal/Magazine Names

Concurrency and Computation: Practice & Experience (12)
International Journal of High Performance Computing Applications (11)
Computer (9)
Cluster Computing (8)
Future Generation Computer Systems (8)
IEEE Transactions on Parallel and Distributed Systems (7)
IEEE Internet Computing (6)
Journal of Parallel and Distributed Computing (6)
Parallel Computing (6)
Communications of the ACM (5)
International Journal of Parallel Programming (3)
Journal of Grid Computing (3)
Scientific Programming (3)
Computer Networks: The International Journal of Computer and Telecommunications Networking (2)
IEEE Parallel & Distributed Technology: Systems & Technology (2)
Proceedings of the VLDB Endowment (2)

Proceedings/Book Names

Sourcebook of parallel computing (4)
CCGRID '06: Proceedings of the Sixth IEEE International Symposium on Cluster Computing and the Grid (3)
GRID '04: Proceedings of the 5th IEEE/ACM International Workshop on Grid Computing (3)
Grid resource management (3)
HPDC '02: Proceedings of the 11th IEEE International Symposium on High Performance Distributed Computing (3)
HPDC '04: Proceedings of the 13th IEEE International Symposium on High Performance Distributed Computing (3)
HPDC '10: Proceedings of the 19th ACM International Symposium on High Performance Distributed Computing (3)
PEARC '19: Proceedings of the Practice and Experience in Advanced Research Computing on Rise of the Machines (learning) (3)
XSEDE '12: Proceedings of the 1st Conference of the Extreme Science and Engineering Discovery Environment: Bridging from the eXtreme to the campus and beyond (3)
AI-Science'18: Proceedings of the 1st International Workshop on Autonomous Infrastructure for Science (2)
Computational Science – ICCS 2022 (2)
GCE '09: Proceedings of the 5th Grid Computing Environments Workshop (2)
HPDC '08: Proceedings of the 17th international symposium on High performance distributed computing (2)
HPDC '13: Proceedings of the 22nd international symposium on High-performance parallel and distributed computing (2)
HPDC '17: Proceedings of the 26th International Symposium on High-Performance Parallel and Distributed Computing (2)
PEARC '20: Practice and Experience in Advanced Research Computing (2)
SC '07: Proceedings of the 2007 ACM/IEEE conference on Supercomputing (2)
SCREAM '15: Proceedings of the 1st Workshop on The Science of Cyberinfrastructure: Research, Experience, Applications and Models (2)
TG '11: Proceedings of the 2011 TeraGrid Conference: Extreme Digital Discovery (2)
XSEDE '13: Proceedings of the Conference on Extreme Science and Engineering Discovery Environment: Gateway to Discovery (2)

All Publications

Proceedings (275)
Other periodicals (104)
Books (19)
Magazines (7)
Journals (5)
Newsletters (5)
Reports (2)

Content Type

Research Article (142)
Poster (5)
Invited Talk (2)
Opinion (2)
Short Paper (2)
Abstract (1)
Extended Abstract (1)
Keynote (1)
Panel (1)

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PDF (143)
Image (17)
HTML (14)
Video (6)
Txt (4)
Ps (1)

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Association for Computing Machinery (114)
IEEE Computer Society (109)
Springer-Verlag (47)
IEEE Press (20)
Kluwer Academic Publishers (18)
Elsevier Science Publishers B. V. (17)
IEEE Computer Society Press (16)
John Wiley and Sons Ltd. (12)
Sage Publications, Inc. (11)
IEEE Educational Activities Department (8)
Morgan Kaufmann Publishers Inc. (8)
Academic Press, Inc. (6)
IOS Press (5)
Society for Industrial and Applied Mathematics (3)
Australian Computer Society, Inc. (2)
California Institute of Technology (2)
Elsevier North-Holland, Inc. (2)
USENIX Association (2)
VLDB Endowment (2)
Academic Press Ltd. (1)
American Institute of Physics Inc. (1)
Chapman & Hall/CRC (1)
Elsevier Science (1)
Elsevier Science Inc. (1)
IBM Corp. (1)
ICST (Institute for Computer Sciences, Social-Informatics and Telecommunications Engineering) (1)
John Wiley & Sons, Inc. (1)
Ohmsha (1)
Oxford University Press, Inc. (1)
Prentice-Hall, Inc. (1)
Simulation Councils, Inc. (1)
The MIT Press (1)

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research-article
Globus automation services: Research process automation across the space–time continuum
- Ryan Chard
  Argonne National Laboratory, 9700 S Cass Ave, Lemont, 60439, IL, USA
  ,
- Jim Pruyne
  Argonne National Laboratory, 9700 S Cass Ave, Lemont, 60439, IL, USA
  University of Chicago, 5730 S Ellis Ave, Chicago, 60615, IL, USA
  ,
- Kurt McKee
  University of Chicago, 5730 S Ellis Ave, Chicago, 60615, IL, USA
  ,
- Josh Bryan
  University of Chicago, 5730 S Ellis Ave, Chicago, 60615, IL, USA
  ,
- Brigitte Raumann
  Argonne National Laboratory, 9700 S Cass Ave, Lemont, 60439, IL, USA
  University of Chicago, 5730 S Ellis Ave, Chicago, 60615, IL, USA
  ,
- Rachana Ananthakrishnan
  Argonne National Laboratory, 9700 S Cass Ave, Lemont, 60439, IL, USA
  University of Chicago, 5730 S Ellis Ave, Chicago, 60615, IL, USA
  ,
- Kyle Chard
  Argonne National Laboratory, 9700 S Cass Ave, Lemont, 60439, IL, USA
  University of Chicago, 5730 S Ellis Ave, Chicago, 60615, IL, USA
  ,
- Ian T. Foster
  Argonne National Laboratory, 9700 S Cass Ave, Lemont, 60439, IL, USA
  University of Chicago, 5730 S Ellis Ave, Chicago, 60615, IL, USA
Future Generation Computer Systems, Volume 142, Issue C•May 2023, pp 393-409 • https://doi.org/10.1016/j.future.2023.01.010
Abstract
Research process automation–the reliable, efficient, and reproducible execution of linked sets of actions on scientific instruments, computers, data stores, and other resources–has emerged as an essential element of modern science. We ...
Highlights

The research process automation problem, fundamental to modern science, is defined.
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Optimizing Error-Bounded Lossy Compression for Scientific Data With Diverse Constraints
- Yuanjian Liu
  Department of Computer Science, University of Chicago, Chicago, IL, USA
  ,
- Sheng Di
  Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, USA
  ,
- Kai Zhao
  Department of Computer Science and Engineering, University of California Riverside, Riverside, CA, USA
  ,
- Sian Jin
  School of EECS, Washington State University, Pullman, WA, USA
  ,
- Cheng Wang
  Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, USA
  ,
- Kyle Chard
  Department of Computer Science, University of Chicago, Chicago, IL, USA
  ,
- Dingwen Tao
  School of EECS, Washington State University, Pullman, WA, USA
  ,
- Ian Foster
  Department of Computer Science, University of Chicago, Chicago, IL, USA
  ,
- Franck Cappello
  Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, USA
IEEE Transactions on Parallel and Distributed Systems, Volume 33, Issue 12•Dec. 2022, pp 4440-4457 • https://doi.org/10.1109/TPDS.2022.3194695
Vast volumes of data are produced by today's scientific simulations and advanced instruments. These data cannot be stored and transferred efficiently because of limited I/O bandwidth, network speed, and storage capacity. Error-bounded lossy ...
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Deep Neural Network Training With Distributed K-FAC
- J. Gregory Pauloski
  Department of Computer Science, University of Chicago, Chicago, IL, USA
  ,
- Lei Huang
  Texas Advanced Computing Center, Austin, TX, USA
  ,
- Weijia Xu
  Texas Advanced Computing Center, Austin, TX, USA
  ,
- Kyle Chard
  Department of Computer Science, University of Chicago, Chicago, IL, USA
  ,
- Ian T. Foster
  Department of Computer Science, University of Chicago, Chicago, IL, USA
  ,
- Zhao Zhang
  Texas Advanced Computing Center, Austin, TX, USA
IEEE Transactions on Parallel and Distributed Systems, Volume 33, Issue 12•Dec. 2022, pp 3616-3627 • https://doi.org/10.1109/TPDS.2022.3161187
Scaling deep neural network training to more processors and larger batch sizes is key to reducing end-to-end training time; yet, maintaining comparable convergence and hardware utilization at larger scales is challenging. Increases in training scales have ...
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Intelligent resolution: Integrating Cryo-EM with AI-driven multi-resolution simulations to observe the severe acute respiratory syndrome coronavirus-2 replication-transcription machinery in action
- Anda Trifan
  1291Argonne National Laboratory, Lemont, IL, USA
  14589University of Illinois Urbana-Champaign, Urbana, IL, USA
  ,
- Defne Gorgun
  14589University of Illinois Urbana-Champaign, Urbana, IL, USA
  ,
- Michael Salim
  1291Argonne National Laboratory, Lemont, IL, USA
  ,
- Zongyi Li
  6469California Institute of Technology, Pasadena, CA, USA
  ,
- Alexander Brace
  1291Argonne National Laboratory, Lemont, IL, USA
  2462University of Chicago, Chicago, IL, USA
  ,
- Maxim Zvyagin
  1291Argonne National Laboratory, Lemont, IL, USA
  ,
- Heng Ma
  1291Argonne National Laboratory, Lemont, IL, USA
  ,
- Austin Clyde
  1291Argonne National Laboratory, Lemont, IL, USA
  2462University of Chicago, Chicago, IL, USA
  ,
- David Clark
  196328NVIDIA, Santa Clara, CA, USA
  ,
- David J Hardy
  14589University of Illinois Urbana-Champaign, Urbana, IL, USA
  ,
- Tom Burnley
  15585Science and Technology Facilities Council, Swindon, UK
  ,
- Lei Huang
  351845Texas Advanced Computing Center, Austin, TX, USA
  ,
- John McCalpin
  351845Texas Advanced Computing Center, Austin, TX, USA
  ,
- Murali Emani
  1291Argonne National Laboratory, Lemont, IL, USA
  ,
- Hyenseung Yoo
  1291Argonne National Laboratory, Lemont, IL, USA
  ,
- Junqi Yin
  6146Oak Ridge National Laboratory, Oak Ridge, TN, USA
  ,
- Aristeidis Tsaris
  6146Oak Ridge National Laboratory, Oak Ridge, TN, USA
  ,
- Vishal Subbiah
  Cerebras Inc., Los Gatos, CA, USA
  ,
- Tanveer Raza
  Cerebras Inc., Los Gatos, CA, USA
  ,
- Jessica Liu
  Cerebras Inc., Los Gatos, CA, USA
  ,
- Noah Trebesch
  14589University of Illinois Urbana-Champaign, Urbana, IL, USA
  ,
- Geoffrey Wells
  4919University College of London, London, UK
  ,
- Venkatesh Mysore
  196328NVIDIA, Santa Clara, CA, USA
  ,
- Thomas Gibbs
  196328NVIDIA, Santa Clara, CA, USA
  ,
- James Phillips
  14589University of Illinois Urbana-Champaign, Urbana, IL, USA
  ,
- S Chakra Chennubhotla
  12317University of Pittsburgh, Pittsburgh, PA, USA
  ,
- Ian Foster
  1291Argonne National Laboratory, Lemont, IL, USA
  2462University of Chicago, Chicago, IL, USA
  ,
- Rick Stevens
  1291Argonne National Laboratory, Lemont, IL, USA
  2462University of Chicago, Chicago, IL, USA
  ,
- Anima Anandkumar
  6469California Institute of Technology, Pasadena, CA, USA
  196328NVIDIA, Santa Clara, CA, USA
  ,
- Venkatram Vishwanath
  1291Argonne National Laboratory, Lemont, IL, USA
  ,
- John E Stone
  14589University of Illinois Urbana-Champaign, Urbana, IL, USA
  ,
- Emad Tajkhorshid
  14589University of Illinois Urbana-Champaign, Urbana, IL, USA
  ,
- Sarah A Harris
  4468University of Leeds, Leeds, UK
  ,
- Arvind Ramanathan
  1291Argonne National Laboratory, Lemont, IL, USA
International Journal of High Performance Computing Applications, Volume 36, Issue 5-6•Nov 2022, pp 603-623 • https://doi.org/10.1177/10943420221113513

The severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) replication transcription complex (RTC) is a multi-domain protein responsible for replicating and transcribing the viral mRNA inside a human cell. Attacking RTC function with pharmaceutical ...
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CUF-Links: Continuous and Ubiquitous FAIRness Linkages for Reproducible Research
- Ian Foster
  Argonne National Laboratory and University of Chicago
  ,
- Carl Kesselman
  University of Southern California
Computer, Volume 55, Issue 8•Aug. 2022, pp 20-30 • https://doi.org/10.1109/MC.2022.3160876
The ability to share data is critical to reproducible research, yet data sharing is often limited because issues of findability, accessibility, interoperability, and reusability—the FAIR principles—are not integrated into every step of the scientific ...
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Data station: delegated, trustworthy, and auditable computation to enable data-sharing consortia with a data escrow
- Siyuan Xia
  The University of Chicago
  ,
- Zhiru Zhu
  The University of Chicago
  ,
- Chris Zhu
  The University of Chicago
  ,
- Jinjin Zhao
  The University of Chicago
  ,
- Kyle Chard
  The University of Chicago
  ,
- Aaron J. Elmore
  The University of Chicago
  ,
- Ian Foster
  The University of Chicago
  ,
- Michael Franklin
  The University of Chicago
  ,
- Sanjay Krishnan
  The University of Chicago
  ,
- Raul Castro Fernandez
  The University of Chicago
Proceedings of the VLDB Endowment, Volume 15, Issue 11•July 2022, pp 3172-3185 • https://doi.org/10.14778/3551793.3551861

Pooling and sharing data increases and distributes its value. But since data cannot be revoked once shared, scenarios that require controlled release of data for regulatory, privacy, and legal reasons default to not sharing. Because selectively ...
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opinion
More Real Than Real: The Race to Simulate Everything
- Rosa M. Badia
  Barcelona Supercomputing Center
  ,
- Ian Foster
  Argonne National Laboratory and University of Chicago
  ,
- Dejan Milojicic
  Hewlett Packard Labs
  ,
- Dejan Milojicic
  Hewlett Packard Labs
Computer, Volume 55, Issue 7•July 2022, pp 67-72 • https://doi.org/10.1109/MC.2022.3173359
Extreme times require extreme measures. In this column, we discuss how high-performance computing embraces artificial intelligence and data analytics to address global challenges.
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Open Access
Published By ACM
Published By ACM
Large Scale Caching and Streaming of Training Data for Online Deep Learning
- Jie Liu
  University of California, Merced, Merced, CA, USA
  ,
- Bogdan Nicolae
  Argonne National Laboratory, Lemont, IL, USA
  ,
- Dong Li
  University of California, Merced, Merced, CA, USA
  ,
- Justin M. Wozniak
  Argonne National Laboratory, Lemont, IL, USA
  ,
- Tekin Bicer
  Argonne National Laboratory, Lemont, IL, USA
  ,
- Zhengchun Liu
  Argonne National Laboratory, Lemont, IL, USA
  ,
- Ian Foster
  Argonne National Laboratory, Lemont, IL, USA
FlexScience '22: Proceedings of the 12th Workshop on AI and Scientific Computing at Scale using Flexible Computing Infrastructures•July 2022, pp 19-26• https://doi.org/10.1145/3526058.3535453

The training of deep neural network models on large data remains a difficult problem, despite progress towards scalable techniques. In particular, there is a mismatch between the random but predetermined order in which AI flows select training samples ...
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abstract
Published By ACM
Published By ACM
HiPS2022: The 2nd Workshop on High Performance Serverless Computing
- Yadu Babuji
  Argonne National Lab & University of Chicago, Chicago, IL, USA
  ,
- Kyle Chard
  Argonne National Laboratory & University of Chicago, Chicago, IL, USA
  ,
- Ian Foster
  Argonne National Laboratory & University of Chicago, Chicago, IL, USA
  ,
- Zhuozhao Li
  Southern University of Science and Technology, Shenzhen, China
HPDC '22: Proceedings of the 31st International Symposium on High-Performance Parallel and Distributed Computing•June 2022, pp 290-291• https://doi.org/10.1145/3502181.3535104

Serverless computing presents an attractive model for general distributed computing as it focuses on abstracting the infrastructure required to execute an application. This workshop investigates the intersection between high performance computing and ...
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research-article
Published By ACM
Published By ACM
SciStream: Architecture and Toolkit for Data Streaming between Federated Science Instruments
- Joaquin Chung
  The University of Chicago & Argonne National Lab., Chicago, IL, USA
  ,
- Wojciech Zacherek
  Rose-Hulman Institute of Technology, Terre Haute, IN, USA
  ,
- AJ Wisniewski
  University of Illinois at Urbana-Champaign, Champaign, IL, USA
  ,
- Zhengchun Liu
  Argonne National Lab. & The University of Chicago, Lemont, IL, USA
  ,
- Tekin Bicer
  Argonne National Lab. & The University of Chicago, Lemont, IL, USA
  ,
- Rajkumar Kettimuthu
  Argonne National Lab. & The University of Chicago, Lemont, IL, USA
  ,
- Ian Foster
  Argonne National Lab. & The University of Chicago, Lemont, IL, USA
HPDC '22: Proceedings of the 31st International Symposium on High-Performance Parallel and Distributed Computing•June 2022, pp 185-198• https://doi.org/10.1145/3502181.3531475

Modern scientific instruments, such as detectors at synchrotron light sources, generate data at such high rates that online processing is needed for data reduction, feature detection, experiment steering, and other purposes. The same high data rates ...
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Article
Models and Metrics for Mining Meaningful Metadata
- Tyler J. Skluzacek
  University of Chicago, Chicago, IL, USA
  ,
- Matthew Chen
  University of Illinois at Urbana-Champaign, Champaign, IL, USA
  ,
- Erica Hsu
  Carnegie Mellon University, Pittsburgh, PA, USA
  ,
- Kyle Chard
  University of Chicago, Chicago, IL, USA
  Argonne National Lab, Lemont, IL, USA
  ,
- Ian Foster
  University of Chicago, Chicago, IL, USA
  Argonne National Lab, Lemont, IL, USA
Computational Science – ICCS 2022•June 2022, pp 417-430• https://doi.org/10.1007/978-3-031-08751-6_30
Abstract
The increasing volume and variety of science data has led to the creation of metadata extraction systems that automatically derive and synthesize relevant information from files. A critical component of metadata extraction systems is a mechanism ...
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Ultrafast Focus Detection for Automated Microscopy
- Maksim Levental
  University of Chicago, Chicago, IL, USA
  ,
- Ryan Chard
  Argonne National Lab, Lemont, IL, USA
  ,
- Kyle Chard
  University of Chicago, Chicago, IL, USA
  Argonne National Lab, Lemont, IL, USA
  ,
- Ian Foster
  University of Chicago, Chicago, IL, USA
  Argonne National Lab, Lemont, IL, USA
  ,
- Gregg Wildenberg
  Argonne National Lab, Lemont, IL, USA
Computational Science – ICCS 2022•June 2022, pp 403-416• https://doi.org/10.1007/978-3-031-08751-6_29
Abstract
Technological advancements in modern scientific instruments, such as scanning electron microscopes (SEMs), have significantly increased data acquisition rates and image resolutions enabling new questions to be explored; however, the resulting ...
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The Exascale Framework for High Fidelity coupled Simulations (EFFIS): Enabling whole device modeling in fusion science
- Eric Suchyta
  Oak Ridge National Laboratory, Oak Ridge, TN, USA
  ,
- Scott Klasky
  Oak Ridge National Laboratory, Oak Ridge, TN, USA
  ,
- Norbert Podhorszki
  Oak Ridge National Laboratory, Oak Ridge, TN, USA
  ,
- Matthew Wolf
  Oak Ridge National Laboratory, Oak Ridge, TN, USA
  ,
- Abolaji Adesoji
  Rensselaer Polytechnic Institute, Troy, NY, USA
  ,
- CS Chang
  Princeton Plasma Physics Laboratory, Princeton, NJ, USA
  ,
- Jong Choi
  Oak Ridge National Laboratory, Oak Ridge, TN, USA
  ,
- Philip E Davis
  Department of Computer Science, The Rutgers Discovery Informatics Institute, Rutgers University, New Brunswick, NJ, USA
  ,
- Julien Dominski
  Princeton Plasma Physics Laboratory, Princeton, NJ, USA
  ,
- Stéphane Ethier
  Princeton Plasma Physics Laboratory, Princeton, NJ, USA
  ,
- Ian Foster
  Argonne National Laboratory, Lemont, IL, USA
  ,
- Kai Germaschewski
  Department of Physics and Astronomy, University of New Hampshire, Durham, NH, USA
  ,
- Berk Geveci
  Kitware, Inc., Clifton Park, NY, USA
  ,
- Chris Harris
  Kitware, Inc., Clifton Park, NY, USA
  ,
- Kevin A Huck
  Oregon Advanced Computing Institute for Science and Society, University of Oregon, Eugene, OR, USA
  ,
- Qing Liu
  New Jersey Institute of Technology, Newark, NJ, USA
  ,
- Jeremy Logan
  Oak Ridge National Laboratory, Oak Ridge, TN, USA
  ,
- Kshitij Mehta
  Oak Ridge National Laboratory, Oak Ridge, TN, USA
  ,
- Gabriele Merlo
  Oden Institute for Computational Engineering and Sciences, University of Texas at Austin, Austin, TX, USA
  ,
- Shirley V Moore
  University of Texas, El Paso, TX, USA
  ,
- Todd Munson
  Argonne National Laboratory, Lemont, IL, USA
  ,
- Manish Parashar
  Scientific Computing and Imaging Institute, University of Utah, Salt Lake City, UT, USA
  ,
- David Pugmire
  Oak Ridge National Laboratory, Oak Ridge, TN, USA
  ,
- Mark S Shephard
  Rensselaer Polytechnic Institute, Troy, NY, USA
  ,
- Cameron W Smith
  Rensselaer Polytechnic Institute, Troy, NY, USA
  ,
- Pradeep Subedi
  Department of Computer Science, The Rutgers Discovery Informatics Institute, Rutgers University, New Brunswick, NJ, USA
  ,
- Lipeng Wan
  Oak Ridge National Laboratory, Oak Ridge, TN, USA
  ,
- Ruonan Wang
  Oak Ridge National Laboratory, Oak Ridge, TN, USA
  ,
- Shuangxi Zhang
  Rensselaer Polytechnic Institute, Troy, NY, USA
  ,
- Thomas M Evans
International Journal of High Performance Computing Applications, Volume 36, Issue 1•Jan 2022, pp 106-128 • https://doi.org/10.1177/10943420211019119

We present the Exascale Framework for High Fidelity coupled Simulations (EFFIS), a workflow and code coupling framework developed as part of the Whole Device Modeling Application (WDMApp) in the Exascale Computing Project. EFFIS consists of a library, ...
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Cross Inference of Throughput Profiles Using Micro Kernel Network Method
- Nageswara S. V. Rao
  Oak Ridge National Laboratory, Oak Ridge, TN, USA
  ,
- Anees Al-Najjar
  Oak Ridge National Laboratory, Oak Ridge, TN, USA
  ,
- Neena Imam
  Oak Ridge National Laboratory, Oak Ridge, TN, USA
  ,
- Zhengchun Liu
  Argonne National Laboratory, Argonne, IL, USA
  ,
- Rajkumar Kettimuthu
  Argonne National Laboratory, Argonne, IL, USA
  ,
- Ian Foster
  Argonne National Laboratory, Argonne, IL, USA
Machine Learning for Networking•December 2021, pp 48-68• https://doi.org/10.1007/978-3-030-98978-1_4
Abstract
Dedicated network connections are being increasingly deployed in cloud, centralized and edge computing and data infrastructures, whose throughput profiles are critical indicators of the underlying data transfer performance. Due to the cost and ...
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Published By ACM
Published By ACM
Bootstrapping in-situ workflow auto-tuning via combining performance models of component applications
- Tong Shu
  Southern Illinois University
  ,
- Yanfei Guo
  Argonne National Laboratory
  ,
- Justin Wozniak
  Argonne National Laboratory
  ,
- Xiaoning Ding
  New Jersey Institute of Technology
  ,
- Ian Foster
  Univ. Chicago
  ,
- Tahsin Kurc
  Stony Brook University
SC '21: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis•November 2021, Article No.: 28, pp 1-15• https://doi.org/10.1145/3458817.3476197

In an in-situ workflow, multiple components such as simulation and analysis applications are coupled with streaming data transfers. The multiplicity of possible configurations necessitates an auto-tuner for workflow optimization. Existing auto-tuning ...
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Bootstrapping In-Situ Workflow Auto-Tuning via Combining Performance Models of Component Applications.mp4.mp4
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KAISA: an adaptive second-order optimizer framework for deep neural networks
- J. Gregory Pauloski
  University of Chicago
  ,
- Qi Huang
  University of Texas at Austin
  ,
- Lei Huang
  Texas Advanced Computing Center
  ,
- Shivaram Venkataraman
  University of Wisconsin, Madison
  ,
- Kyle Chard
  University of Chicago
  ,
- Ian Foster
  University of Chicago
  ,
- Zhao Zhang
  Texas Advanced Computing Center
SC '21: Proceedings of the International Conference for High Performance Computing, Networking, Storage and Analysis•November 2021, Article No.: 13, pp 1-14• https://doi.org/10.1145/3458817.3476152

Kronecker-factored Approximate Curvature (K-FAC) has recently been shown to converge faster in deep neural network (DNN) training than stochastic gradient descent (SGD); however, K-FAC's larger memory footprint hinders its applicability to large models. ...
4Citation
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Efficient Deep Learning Tools - KAISA_ An Adaptive Second-Order Optimizer Framework for Deep Neural Networks.mp4.mp4
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Co-design Center for Exascale Machine Learning Technologies (ExaLearn)
- Francis J Alexander
  8099Brookhaven National Laboratory, Upton, NY, USA
  ,
- James Ang
  Pacific Northwest National Laboratory, Richland, WA, USA
  ,
- Jenna A Bilbrey
  Pacific Northwest National Laboratory, Richland, WA, USA
  ,
- Jan Balewski
  Lawrence Berkeley National Laboratory, Berkeley, CA, USA
  ,
- Tiernan Casey
  Sandia National Laboratories, Albuquerque, NM, USA
  ,
- Ryan Chard
  Argonne National Laboratory, Lemont, IL, USA
  ,
- Jong Choi
  Oak Ridge National Laboratory, Oak Ridge, TN, USA
  ,
- Sutanay Choudhury
  Pacific Northwest National Laboratory, Richland, WA, USA
  ,
- Bert Debusschere
  Sandia National Laboratories, Albuquerque, NM, USA
  ,
- Anthony M DeGennaro
  8099Brookhaven National Laboratory, Upton, NY, USA
  ,
- Nikoli Dryden
  Lawrence Livermore National Laboratories, Livermore, CA, USA
  ETH Zurich, Zurich, Switzerland
  ,
- J Austin Ellis
  Sandia National Laboratories, Albuquerque, NM, USA
  ,
- Ian Foster
  Argonne National Laboratory, Lemont, IL, USA
  ,
- Cristina Garcia Cardona
  Oak Ridge National Laboratory, Oak Ridge, TN, USA
  ,
- Sayan Ghosh
  Pacific Northwest National Laboratory, Richland, WA, USA
  ,
- Peter Harrington
  Lawrence Berkeley National Laboratory, Berkeley, CA, USA
  ,
- Yunzhi Huang
  Pacific Northwest National Laboratory, Richland, WA, USA
  ,
- Shantenu Jha
  8099Brookhaven National Laboratory, Upton, NY, USA
  ,
- Travis Johnston
  Pacific Northwest National Laboratory, Richland, WA, USA
  ,
- Ai Kagawa
  8099Brookhaven National Laboratory, Upton, NY, USA
  ,
- Ramakrishnan Kannan
  Pacific Northwest National Laboratory, Richland, WA, USA
  ,
- Neeraj Kumar
  Pacific Northwest National Laboratory, Richland, WA, USA
  ,
- Zhengchun Liu
  Argonne National Laboratory, Lemont, IL, USA
  ,
- Naoya Maruyama
  Lawrence Livermore National Laboratories, Livermore, CA, USA
  ,
- Satoshi Matsuoka
  RIKEN Center for Computational Science, Kobe, Japan
  Tokyo Institute of Technology, Tokyo, Japan
  ,
- Erin McCarthy
  Lawrence Livermore National Laboratories, Livermore, CA, USA
  University of Oregon, Eugene, OR, USA
  ,
- Jamaludin Mohd-Yusof
  5112Los Alamos National Laboratory, Los Alamos, NM, USA
  ,
- Peter Nugent
  Lawrence Berkeley National Laboratory, Berkeley, CA, USA
  ,
- Yosuke Oyama
  Lawrence Livermore National Laboratories, Livermore, CA, USA
  Tokyo Institute of Technology, Tokyo, Japan
  ,
- Thomas Proffen
  Oak Ridge National Laboratory, Oak Ridge, TN, USA
  ,
- David Pugmire
  Oak Ridge National Laboratory, Oak Ridge, TN, USA
  ,
- Sivasankaran Rajamanickam
  Sandia National Laboratories, Albuquerque, NM, USA
  ,
- Vinay Ramakrishniah
  5112Los Alamos National Laboratory, Los Alamos, NM, USA
  ,
- Malachi Schram
  Pacific Northwest National Laboratory, Richland, WA, USA
  ,
- Sudip K Seal
  Pacific Northwest National Laboratory, Richland, WA, USA
  ,
- Ganesh Sivaraman
  Argonne National Laboratory, Lemont, IL, USA
  ,
- Christine Sweeney
  5112Los Alamos National Laboratory, Los Alamos, NM, USA
  ,
- Li Tan
  8099Brookhaven National Laboratory, Upton, NY, USA
  ,
- Rajeev Thakur
  Argonne National Laboratory, Lemont, IL, USA
  ,
- Brian Van Essen
  Lawrence Livermore National Laboratories, Livermore, CA, USA
  ,
- Logan Ward
  Argonne National Laboratory, Lemont, IL, USA
  ,
- Paul Welch
  5112Los Alamos National Laboratory, Los Alamos, NM, USA
  ,
- Michael Wolf
  Sandia National Laboratories, Albuquerque, NM, USA
  ,
- Sotiris S Xantheas
  Pacific Northwest National Laboratory, Richland, WA, USA
  ,
- Kevin G Yager
  8099Brookhaven National Laboratory, Upton, NY, USA
  ,
- Shinjae Yoo
  8099Brookhaven National Laboratory, Upton, NY, USA
  ,
- Byung-Jun Yoon
  8099Brookhaven National Laboratory, Upton, NY, USA
  ,
- Tim Germann
International Journal of High Performance Computing Applications, Volume 35, Issue 6•Nov 2021, pp 598-616 • https://doi.org/10.1177/10943420211029302

Rapid growth in data, computational methods, and computing power is driving a remarkable revolution in what variously is termed machine learning (ML), statistical learning, computational learning, and artificial intelligence. In addition to highly ...
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Online data analysis and reduction: An important Co-design motif for extreme-scale computers
- Ian Foster
  1291Argonne National Laboratory, Lemont, IL, USA
  University of Chicago, Chicago, IL, USA
  ,
- Mark Ainsworth
  Brown University, Providence, RI, USA
  ,
- Julie Bessac
  1291Argonne National Laboratory, Lemont, IL, USA
  ,
- Franck Cappello
  1291Argonne National Laboratory, Lemont, IL, USA
  ,
- Jong Choi
  Oak Ridge National Laboratory, Oak Ridge, TN, USA
  ,
- Sheng Di
  1291Argonne National Laboratory, Lemont, IL, USA
  ,
- Zichao Di
  1291Argonne National Laboratory, Lemont, IL, USA
  ,
- Ali M Gok
  1291Argonne National Laboratory, Lemont, IL, USA
  ,
- Hanqi Guo
  1291Argonne National Laboratory, Lemont, IL, USA
  ,
- Kevin A Huck
  University of Oregon, Eugene, OR, USA
  ,
- Christopher Kelly
  Brookhaven National Laboratory, Upton, NY, USA
  ,
- Scott Klasky
  Oak Ridge National Laboratory, Oak Ridge, TN, USA
  ,
- Kerstin Kleese van Dam
  Brookhaven National Laboratory, Upton, NY, USA
  ,
- Xin Liang
  Oak Ridge National Laboratory, Oak Ridge, TN, USA
  ,
- Kshitij Mehta
  Oak Ridge National Laboratory, Oak Ridge, TN, USA
  ,
- Manish Parashar
  Rutgers University, New Brunswick, NJ, USA
  ,
- Tom Peterka
  1291Argonne National Laboratory, Lemont, IL, USA
  ,
- Line Pouchard
  Brookhaven National Laboratory, Upton, NY, USA
  ,
- Tong Shu
  1291Argonne National Laboratory, Lemont, IL, USA
  Southern Illinois University, Carbondale, IL, USA
  ,
- Ozan Tugluk
  Brown University, Providence, RI, USA
  ,
- Hubertus van Dam
  Brookhaven National Laboratory, Upton, NY, USA
  ,
- Lipeng Wan
  Oak Ridge National Laboratory, Oak Ridge, TN, USA
  ,
- Matthew Wolf
  Oak Ridge National Laboratory, Oak Ridge, TN, USA
  ,
- Justin M Wozniak
  1291Argonne National Laboratory, Lemont, IL, USA
  ,
- Wei Xu
  Brookhaven National Laboratory, Upton, NY, USA
  ,
- Igor Yakushin
  1291Argonne National Laboratory, Lemont, IL, USA
  ,
- Shinjae Yoo
  Brookhaven National Laboratory, Upton, NY, USA
  ,
- Todd Munson
  1291Argonne National Laboratory, Lemont, IL, USA
  ,
- Tim Germann
International Journal of High Performance Computing Applications, Volume 35, Issue 6•Nov 2021, pp 617-635 • https://doi.org/10.1177/10943420211023549

A growing disparity between supercomputer computation speeds and I/O rates means that it is rapidly becoming infeasible to analyze supercomputer application output only after that output has been written to a file system. Instead, data-generating ...
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IMPECCABLE: Integrated Modeling PipelinE for COVID Cure by Assessing Better LEads
- Aymen Al Saadi
  Rutgers University, United States
  ,
- Dario Alfe
  University College London and University of Naples Federico II, United Kingdom
  ,
- Yadu Babuji
  University of Chicago, United States
  ,
- Agastya Bhati
  University College London, United Kingdom
  ,
- Ben Blaiszik
  Argonne National Laboratory, United States
  ,
- Alexander Brace
  Argonne National Laboratory, United States
  ,
- Thomas Brettin
  Argonne National Laboratory, United States
  ,
- Kyle Chard
  University of Chicago, United States
  ,
- Ryan Chard
  Argonne National Laboratory, United States
  ,
- Austin Clyde
  University of Chicago, United States
  ,
- Peter Coveney
  University College London, United Kingdom
  ,
- Ian Foster
  Argonne National Laboratory, United States
  ,
- Tom Gibbs
  NVIDIA Corporation, United States
  ,
- Shantenu Jha
  Brookhaven National Laboratory, Rutgers University, United States
  ,
- Kristopher Keipert
  NVIDIA Corporation, United States
  ,
- Dieter Kranzlmüller
  Leibniz Supercomputing Centre, Germany
  ,
- Thorsten Kurth
  NVIDIA Corporation, United States
  ,
- Hyungro Lee
  Rutgers University, United States
  ,
- Zhuozhao Li
  University of Chicago, United States
  ,
- Heng Ma
  Argonne National Laboratory, United States
  ,
- Gerald Mathias
  Leibniz Supercomputing Centre, Germany
  ,
- Andre Merzky
  Rutgers University, United States
  ,
- Alexander Partin
  Argonne National Laboratory, United States
  ,
- Arvind Ramanathan
  Argonne National Laboratory, United States
  ,
- Ashka Shah
  University of Chicago, United States
  ,
- Abraham Stern
  NVIDIA Corporation, United States
  ,
- Rick Stevens
  Argonne National Lab and University of Chicago, USA
  ,
- Li Tan
  Brookhaven National Laboratory, United States
  ,
- Mikhail Titov
  Brookhaven National Laboratory, United States
  ,
- Anda Trifan
  University of Illinois at Urbana Champaign, United States
  ,
- Aristeidis Tsaris
  Oak Ridge National Laboratory, United States
  ,
- Matteo Turilli
  Rutgers University, United States
  ,
- Huub Van Dam
  Brookhaven National Laboratory, United States
  ,
- Shunzhou Wan
  University College London, United Kingdom
  ,
- David Wifling
  Leibniz Supercomputing Centre, Germany
  ,
- Junqi Yin
  Oak Ridge National Laboratory, United States
ICPP '21: Proceedings of the 50th International Conference on Parallel Processing•August 2021, Article No.: 40, pp 1-12• https://doi.org/10.1145/3472456.3473524

The drug discovery process currently employed in the pharmaceutical industry typically requires about 10 years and $2–3 billion to deliver one new drug. This is both too expensive and too slow, especially in emergencies like the COVID-19 pandemic. In ...
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FTK: A Simplicial Spacetime Meshing Framework for Robust and Scalable Feature Tracking
- Hanqi Guo
  Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, USA
  ,
- David Lenz
  Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, USA
  ,
- Jiayi Xu
  Department of Computer Science and Engineering, Ohio State University, Columbus, OH, USA
  ,
- Xin Liang
  Department of Computer Science, Missouri University of Science and Technology, Rolla, MO, USA
  ,
- Wenbin He
  Bosch Research North America, Sunnyvale, CA, USA
  ,
- Iulian R. Grindeanu
  Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, USA
  ,
- Han-Wei Shen
  Department of Computer Science and Engineering, Ohio State University, Columbus, OH, USA
  ,
- Tom Peterka
  Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, USA
  ,
- Todd Munson
  Mathematics and Computer Science Division, Argonne National Laboratory, Lemont, IL, USA
  ,
- Ian Foster
  Data Science and Learning Division, Argonne National Laboratory, Lemont, IL, USA
IEEE Transactions on Visualization and Computer Graphics, Volume 27, Issue 8•Aug. 2021, pp 3463-3480 • https://doi.org/10.1109/TVCG.2021.3073399
We present the Feature Tracking Kit (FTK), a framework that simplifies, scales, and delivers various feature-tracking algorithms for scientific data. The key of FTK is our simplicial spacetime meshing scheme that generalizes both regular and unstructured ...
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Author Profile Pages

Description: The Author Profile Page initially collects all the professional information known about authors from the publications record as known by the ACM bibliographic database, the Guide. Coverage of ACM publications is comprehensive from the 1950's. Coverage of other publishers generally starts in the mid 1980's. The Author Profile Page supplies a quick snapshot of an author's contribution to the field and some rudimentary measures of influence upon it. Over time, the contents of the Author Profile page may expand at the direction of the community.
Please see the following 2007 Turing Award winners' profiles as examples:
History: Disambiguation of author names is of course required for precise identification of all the works, and only those works, by a unique individual. Of equal importance to ACM, author name normalization is also one critical prerequisite to building accurate citation and download statistics. For the past several years, ACM has worked to normalize author names, expand reference capture, and gather detailed usage statistics, all intended to provide the community with a robust set of publication metrics. The Author Profile Pages reveal the first result of these efforts.
Normalization: ACM uses normalization algorithms to weigh several types of evidence for merging and splitting names.
These include:
- co-authors: if we have two names and cannot disambiguate them based on name alone, then we see if they have a co-author in common. If so, this weighs towards the two names being the same person.
- affiliations: names in common with same affiliation weighs toward the two names being the same person.
- publication title: names in common whose works are published in same journal weighs toward the two names being the same person.
- keywords: names in common whose works address the same subject matter as determined from title and keywords, weigh toward being the same person.
The more conservative the merging algorithms, the more bits of evidence are required before a merge is made, resulting in greater precision but lower recall of works for a given Author Profile. Many bibliographic records have only author initials. Many names lack affiliations. With very common family names, typical in Asia, more liberal algorithms result in mistaken merges.

Automatic normalization of author names is not exact. Hence it is clear that manual intervention based on human knowledge is required to perfect algorithmic results. ACM is meeting this challenge, continuing to work to improve the automated merges by tweaking the weighting of the evidence in light of experience.
Bibliometrics: In 1926, Alfred Lotka formulated his power law (known as Lotka's Law) describing the frequency of publication by authors in a given field. According to this bibliometric law of scientific productivity, only a very small percentage (~6%) of authors in a field will produce more than 10 articles while the majority (perhaps 60%) will have but a single article published. With ACM's first cut at author name normalization in place, the distribution of our authors with 1, 2, 3..n publications does not match Lotka's Law precisely, but neither is the distribution curve far off. For a definition of ACM's first set of publication statistics, see Bibliometrics
Future Direction: The initial release of the Author Edit Screen is open to anyone in the community with an ACM account, but it is limited to personal information. An author's photograph, a Home Page URL, and an email may be added, deleted or edited. Changes are reviewed before they are made available on the live site.
ACM will expand this edit facility to accommodate more types of data and facilitate ease of community participation with appropriate safeguards. In particular, authors or members of the community will be able to indicate works in their profile that do not belong there and merge others that do belong but are currently missing.

A direct search interface for Author Profiles will be built.

An institutional view of works emerging from their faculty and researchers will be provided along with a relevant set of metrics.

It is possible, too, that the Author Profile page may evolve to allow interested authors to upload unpublished professional materials to an area available for search and free educational use, but distinct from the ACM Digital Library proper. It is hard to predict what shape such an area for user-generated content may take, but it carries interesting potential for input from the community.

Bibliometrics

The ACM DL is a comprehensive repository of publications from the entire field of computing.

It is ACM's intention to make the derivation of any publication statistics it generates clear to the user.

Average citations per article = The total Citation Count divided by the total Publication Count.
Citation Count = cumulative total number of times all authored works by this author were cited by other works within ACM's bibliographic database. Almost all reference lists in articles published by ACM have been captured. References lists from other publishers are less well-represented in the database. Unresolved references are not included in the Citation Count. The Citation Count is citations TO any type of work, but the references counted are only FROM journal and proceedings articles. Reference lists from books, dissertations, and technical reports have not generally been captured in the database. (Citation Counts for individual works are displayed with the individual record listed on the Author Page.)
Publication Count = all works of any genre within the universe of ACM's bibliographic database of computing literature of which this person was an author. Works where the person has role as editor, advisor, chair, etc. are listed on the page but are not part of the Publication Count.
Publication Years = the span from the earliest year of publication on a work by this author to the most recent year of publication of a work by this author captured within the ACM bibliographic database of computing literature (The ACM Guide to Computing Literature, also known as "the Guide".
Available for download = the total number of works by this author whose full texts may be downloaded from an ACM full-text article server. Downloads from external full-text sources linked to from within the ACM bibliographic space are not counted as 'available for download'.
Average downloads per article = The total number of cumulative downloads divided by the number of articles (including multimedia objects) available for download from ACM's servers.
Downloads (cumulative) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server since the downloads were first counted in May 2003. The counts displayed are updated monthly and are therefore 0-31 days behind the current date. Robotic activity is scrubbed from the download statistics.
Downloads (12 months) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server over the last 12-month period for which statistics are available. The counts displayed are usually 1-2 weeks behind the current date. (12-month download counts for individual works are displayed with the individual record.)
Downloads (6 weeks) = The cumulative number of times all works by this author have been downloaded from an ACM full-text article server over the last 6-week period for which statistics are available. The counts displayed are usually 1-2 weeks behind the current date. (6-week download counts for individual works are displayed with the individual record.)

ACM Author-Izer Service

Summary Description

ACM Author-Izer is a unique service that enables ACM authors to generate and post links on both their homepage and institutional repository for visitors to download the definitive version of their articles from the ACM Digital Library at no charge.

Downloads from these sites are captured in official ACM statistics, improving the accuracy of usage and impact measurements. Consistently linking to definitive version of ACM articles should reduce user confusion over article versioning.

ACM Author-Izer also extends ACM’s reputation as an innovative “Green Path” publisher, making ACM one of the first publishers of scholarly works to offer this model to its authors.

To access ACM Author-Izer, authors need to establish a free ACM web account. Should authors change institutions or sites, they can utilize the new ACM service to disable old links and re-authorize new links for free downloads from a different site.

How ACM Author-Izer Works

Authors may post ACM Author-Izer links in their own bibliographies maintained on their website and their own institution’s repository. The links take visitors to your page directly to the definitive version of individual articles inside the ACM Digital Library to download these articles for free.

The Service can be applied to all the articles you have ever published with ACM.

Depending on your previous activities within the ACM DL, you may need to take up to three steps to use ACM Author-Izer.

For authors who do not have a free ACM Web Account:

Go to the ACM DL http://dl.acm.org/ and click SIGN UP. Once your account is established, proceed to next step.

For authors who have an ACM web account, but have not edited their ACM Author Profile page:

Sign in to your ACM web account and go to your Author Profile page. Click "Add personal information" and add photograph, homepage address, etc. Click ADD AUTHOR INFORMATION to submit change. Once you receive email notification that your changes were accepted, you may utilize ACM Author-izer.

For authors who have an account and have already edited their Profile Page:

Sign in to your ACM web account, go to your Author Profile page in the Digital Library, look for the ACM Author-izer link below each ACM published article, and begin the authorization process. If you have published many ACM articles, you may find a batch Authorization process useful. It is labeled: "Export as: ACM Author-Izer Service"

ACM Author-Izer also provides code snippets for authors to display download and citation statistics for each “authorized” article on their personal pages. Downloads from these pages are captured in official ACM statistics, improving the accuracy of usage and impact measurements. Consistently linking to the definitive version of ACM articles should reduce user confusion over article versioning.

Note: You still retain the right to post your author-prepared preprint versions on your home pages and in your institutional repositories with DOI pointers to the definitive version permanently maintained in the ACM Digital Library. But any download of your preprint versions will not be counted in ACM usage statistics. If you use these AUTHOR-IZER links instead, usage by visitors to your page will be recorded in the ACM Digital Library and displayed on your page.

FAQ

Q. What is ACM Author-Izer?
A. ACM Author-Izer is a unique, link-based, self-archiving service that enables ACM authors to generate and post links on either their home page or institutional repository for visitors to download the definitive version of their articles for free.
Q. What articles are eligible for ACM Author-Izer?
A. ACM Author-Izer can be applied to all the articles authors have ever published with ACM. It is also available to authors who will have articles published in ACM publications in the future.

Q. Are there any restrictions on authors to use this service?
A. No. An author does not need to subscribe to the ACM Digital Library nor even be a member of ACM.

Q. What are the requirements to use this service?
A. To access ACM Author-Izer, authors need to have a free ACM web account, must have an ACM Author Profile page in the Digital Library, and must take ownership of their Author Profile page.

Q. What is an ACM Author Profile Page?
A. The Author Profile Page initially collects all the professional information known about authors from the publications record as known by the ACM Digital Library. The Author Profile Page supplies a quick snapshot of an author's contribution to the field and some rudimentary measures of influence upon it. Over time, the contents of the Author Profile page may expand at the direction of the community. Please visit the ACM Author Profile documentation page for more background information on these pages.

Q. How do I find my Author Profile page and take ownership?
A. You will need to take the following steps:
- Create a free ACM Web Account
- Sign-In to the ACM Digital Library
- Find your Author Profile Page by searching the ACM Digital Library for your name
- Find the result you authored (where your author name is a clickable link)
- Click on your name to go to the Author Profile Page
- Click the "Add Personal Information" link on the Author Profile Page
- Wait for ACM review and approval; generally less than 24 hours

Q. Why does my photo not appear?
A. Make sure that the image you submit is in .jpg or .gif format and that the file name does not contain special characters

Q. What if I cannot find the Add Personal Information function on my author page?
A. The ACM account linked to your profile page is different than the one you are logged into. Please logout and login to the account associated with your Author Profile Page.

Q. What happens if an author changes the location of his bibliography or moves to a new institution?
A. Should authors change institutions or sites, they can utilize ACM Author-Izer to disable old links and re-authorize new links for free downloads from a new location.

Q. What happens if an author provides a URL that redirects to the author’s personal bibliography page?
A. The service will not provide a free download from the ACM Digital Library. Instead the person who uses that link will simply go to the Citation Page for that article in the ACM Digital Library where the article may be accessed under the usual subscription rules.

However, if the author provides the target page URL, any link that redirects to that target page will enable a free download from the Service.

Q. What happens if the author’s bibliography lives on a page with several aliases?
A. Only one alias will work, whichever one is registered as the page containing the author’s bibliography. ACM has no technical solution to this problem at this time.

Q. Why should authors use ACM Author-Izer?
A. ACM Author-Izer lets visitors to authors’ personal home pages download articles for no charge from the ACM Digital Library. It allows authors to dynamically display real-time download and citation statistics for each “authorized” article on their personal site.

Q. Does ACM Author-Izer provide benefits for authors?
A. Downloads of definitive articles via Author-Izer links on the authors’ personal web page are captured in official ACM statistics to more accurately reflect usage and impact measurements.

Authors who do not use ACM Author-Izer links will not have downloads from their local, personal bibliographies counted. They do, however, retain the existing right to post author-prepared preprint versions on their home pages or institutional repositories with DOI pointers to the definitive version permanently maintained in the ACM Digital Library.

Q. How does ACM Author-Izer benefit the computing community?
A. ACM Author-Izer expands the visibility and dissemination of the definitive version of ACM articles. It is based on ACM’s strong belief that the computing community should have the widest possible access to the definitive versions of scholarly literature. By linking authors’ personal bibliography with the ACM Digital Library, user confusion over article versioning should be reduced over time.

In making ACM Author-Izer a free service to both authors and visitors to their websites, ACM is emphasizing its continuing commitment to the interests of its authors and to the computing community in ways that are consistent with its existing subscription-based access model.

Q. How do authors access ACM Author-Izer?
A. See How to Use

Q. Why can’t I find my most recent publication in my ACM Author Profile Page?
A. There is a time delay between publication and the process which associates that publication with an Author Profile Page. Right now, that process usually takes 4-8 weeks.

Q. How does ACM Author-Izer expand ACM’s “Green Path” Access Policies?
A. ACM Author-Izer extends the rights and permissions that authors retain even after copyright transfer to ACM, which has been among the “greenest” publishers. ACM enables its author community to retain a wide range of rights related to copyright and reuse of materials. They include:
- Posting rights that ensure free access to their work outside the ACM Digital Library and print publications
- Rights to reuse any portion of their work in new works that they may create
- Copyright to artistic images in ACM’s graphics-oriented publications that authors may want to exploit in commercial contexts
- All patent rights, which remain with the original owner