Award Abstract # 1730158
CI-New: Cognitive Hardware and Software Ecosystem Community Infrastructure (CHASE-CI)

NSF Org: CNS
Division Of Computer and Network Systems
Recipient: UNIVERSITY OF CALIFORNIA, SAN DIEGO
Initial Amendment Date: June 8, 2017
Latest Amendment Date: November 4, 2020
Award Number: 1730158
Award Instrument: Standard Grant
Program Manager: Marilyn McClure
mmcclure@nsf.gov
 (703)292-5197
CNS
 Division Of Computer and Network Systems
CSE
 Directorate for Computer and Information Science and Engineering
Start Date: October 1, 2017
End Date: September 30, 2021 (Estimated)
Total Intended Award Amount: $1,000,000.00
Total Awarded Amount to Date: $1,231,998.00
Funds Obligated to Date: FY 2017 = $1,000,000.00
FY 2018 = $199,998.00

FY 2020 = $16,000.00

FY 2021 = $16,000.00
History of Investigator:
  • Larry Smarr (Principal Investigator)
    lsmarr@ucsd.edu
  • Kenneth Kreutz-Delgado (Co-Principal Investigator)
  • Tajana Rosing (Co-Principal Investigator)
  • Ilkay Altintas (Co-Principal Investigator)
  • Thomas DeFanti (Co-Principal Investigator)
Recipient Sponsored Research Office: University of California-San Diego
9500 GILMAN DR
LA JOLLA
CA  US  92093-0021
(858)534-4896
Sponsor Congressional District: 50
Primary Place of Performance: University of California-San Diego
9500 Gilman Drive
La Jolla
CA  US  92093-0934
Primary Place of Performance
Congressional District:
50
Unique Entity Identifier (UEI): UYTTZT6G9DT1
Parent UEI:
NSF Program(s): Special Projects - CNS,
CSR-Computer Systems Research,
CCRI-CISE Cmnty Rsrch Infrstrc
Primary Program Source: 01002021DB NSF RESEARCH & RELATED ACTIVIT
01002122DB NSF RESEARCH & RELATED ACTIVIT

01001819DB NSF RESEARCH & RELATED ACTIVIT

01001718DB NSF RESEARCH & RELATED ACTIVIT
Program Reference Code(s): 9251, 7359, 9178, 7354
Program Element Code(s): 171400, 735400, 735900
Award Agency Code: 4900
Fund Agency Code: 4900
Assistance Listing Number(s): 47.070

ABSTRACT

This project, called the Cognitive Hardware And Software Ecosystem Community Infrastructure (CHASE-CI), will build a cloud of hundreds of affordable Graphics Processing Units (GPUs), networked together with a variety of neural network machines to facilitate development of next generation cognitive computing. This cloud will be accessible by 30 researchers assembled from 10 universities via the NSF-funded Pacific Research Platform. These researchers will investigate a range of problems from image and video recognition, computer vision, contextual robotics to cognitive neurosciences using the cloud to be purpose-built in this project.

Training of neural network with large data-sets is best performed on GPUs. Lack of availability of affordable GPUs and lack of easy access to the new generation of Non-von Neumann (NvN) machines with embedded neural networks impede research in cognitive computing. The purpose-built cloud will be available over the network to address this bottleneck. PIs will study various Deep Neural Network, Recurrent Neural Network, and Reinforcement Learning Algorithms on this platform.

PUBLICATIONS PRODUCED AS A RESULT OF THIS RESEARCH

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Jiang, Y. and Balaban, M. and and Mirarab, S. "DEPP: Deep Learning Enables Extending Species Trees using Single Genes" bioRxiv , 2021 https://doi.org/10.1101/2021.01.22.427808 Citation Details
Coskun, Ayse and Eris, Furkan and Joshi, Ajay and Kahng, Andrew B. and Ma, Yenai and Narayan, Aditya and Srinivas, Vaishnav "Cross-Layer Co-Optimization of Network Design and Chiplet Placement in 2.5D Systems" IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems , 2020 10.1109/TCAD.2020.2970019 Citation Details
Das, Srinjoy and Politis, Dimitris N. "Nonparametric Estimation of the Conditional Distribution at Regression Boundary Points" The American Statistician , 2019 10.1080/00031305.2018.1558109 Citation Details
Das, Srinjoy and Politis, Dimitris N. "Predictive Inference for Locally Stationary Time Series With an Application to Climate Data" Journal of the American Statistical Association , 2020 https://doi.org/10.1080/01621459.2019.1708368 Citation Details
Du, X. "Learning by Passing Tests, with Application to Neural Architecture Search" ArXivorg , 2020 Citation Details
Du, X. and "Small-Group Learning, with Application to Neural Architecture Search" ArXivorg , 2020 Citation Details
Fajardo, Edgar and Arora, Aashay and Davila, Diego and Gao, Richard and Würthwein, Frank and Bockelman, Brian "Systematic benchmarking of HTTPS third party copy on 100Gbps links using XRootD" EPJ Web of Conferences , v.251 , 2021 https://doi.org/10.1051/epjconf/202125102001 Citation Details
Fang, Zhou and Hong, Dezhi and Gupta, Rajesh K. "Serving deep neural networks at the cloud edge for vision applications on mobile platforms" ACM Multimedia Systems Conference (MMSys) 2019 , 2019 10.1145/3304109.3306221 Citation Details
Fatemi, Hamed and Kahng, Andrew B. and Lee, Hyein and de Gyvez, Jose Pineda "Heuristic Methods for Fine-Grain Exploitation of FDSOI" IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems , 2019 10.1109/TCAD.2019.2935053 Citation Details
Fatemi, Hamed and Kahng, Andrew B. and Lee, Hyein and Li, Jiajia and Pineda de Gyvez, Jose "Enhancing sensitivity-based power reduction for an industry IC design context" Integration , v.66 , 2019 10.1016/j.vlsi.2019.01.008 Citation Details
Feng, Qiaojun and Atanasov, Nikolay "Fully Convolutional Geometric Features for Category-level Object Alignment" 2020 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS) , 2020 https://doi.org/10.1109/IROS45743.2020.9341550 Citation Details
(Showing: 1 - 10 of 176)

PROJECT OUTCOMES REPORT

Disclaimer

This Project Outcomes Report for the General Public is displayed verbatim as submitted by the Principal Investigator (PI) for this award. Any opinions, findings, and conclusions or recommendations expressed in this Report are those of the PI and do not necessarily reflect the views of the National Science Foundation; NSF has not approved or endorsed its content.

The Cognitive Hardware And Software Ecosystem Community Infrastructure (CHASE-CI) project successfully created a private cloud of hundreds of affordable Graphics Processing Units (GPUs) networked together to facilitate development of next generation cognitive computing, which was being impeded by high costs and limited access to hardware.

Machine learning (ML) with large datasets, either static or streaming, is one of the fastest growing fields in computer science and engineering. The CHASE-CI project designed and propagated a strong research community infrastructure and human support system to address current challenges to ML research by supporting the ML discipline and its allied applications with hardware, system software, data, networking, lab notebooks, and workflows. It also has proven to be useful to researchers in computational media.

Leveraging the existing NSF-funded Pacific Research Platform (PRP), CHASE-CI created an initial community of more than 30 ML researchers and facilitated their research efforts by making available several hundred 32-bit GPUs and large amounts of storage for off-line computing of training weights or parameters from large numbers of data samples. CHASE-CI built a custom distributed cyberinfrastructure called Nautilus and made it compatible with the commercial clouds and NSF Supercomputer Centers, which have vastly increased their offerings of a variety of GPUs over the past three years. To date, the project has made more than 500 GPUs accessible to the ML community, and at least 103 active projects were using approximately 3,500,000 GPU hours in the Nautilus cluster in the final year of funding. The largest ML GPU users in the last year were exploring enhancement to a variety of techniques, including self-supervised, reinforcement, deep CNN, multi-task, and 3D scene learning. Applications of these improved ML algorithms were to robotics, self-driving cars, genomics, medical records, and COVID-19 forecasting.

To sustain the success and usability of the CHASE-CI infrastructure, an important goal of the project was to train university IT professionals to care for the developed GPU clusters and to encourage building of similar clusters of GPUs for student use in classes and labs. By the final year of the project, 30,000 students in UCSD courses had used a campus-funded 128-GPU Nautilus-cloned cluster. The Kubernetes Nautilus cluster developed for the PRP has also been seamlessly offered as a means for researchers to access cloud resources over high-speed networks such as CENIC and Internet2 that are connected to Amazon, Google, and Microsoft clouds. CHASE-CI provides partners with access methods to use these commercial clouds; some NSF supercomputers, Google, and Amazon Web Services now support Kubernetes orchestration. In addition, tools for measuring and monitoring scientific use of CHASE-CI resources were created that allow developers to tabulate and visualize quantitative information so that CHASE-CI resources can be continually compared to commercial cloud offerings and updated as needed to remain state-of-the-art.

 


Last Modified: 11/29/2021
Modified by: Larry L Smarr

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