Panel on the Future of Machine Learning
California Institute for Telecommunications and Information Technology
University of California, Irvine
May 24, 2018
The document discusses the Pacific Research Platform (PRP), a distributed cyberinfrastructure that connects researchers and data across multiple campuses in California and beyond using optical fiber networking. Key points:
- The PRP uses high-speed networking infrastructure like the CENIC network to connect data generators and consumers across 15+ campuses, creating an integrated "big data freeway system".
- It deploys specialized data transfer nodes called FIONAs to enable high-speed transfer of large datasets between sites at near the full network speed.
- Recent additions include using Kubernetes to orchestrate containers across the PRP infrastructure and integrating machine learning resources through the CHASE-CI grant to support data-intensive AI applications.
Berkeley cloud computing meetup may 2020Larry Smarr
The Pacific Research Platform (PRP) is a high-bandwidth global private "cloud" connected to commercial clouds that provides researchers with distributed computing resources. It links Science DMZs at universities across California and beyond using a high-performance network. The PRP utilizes Data Transfer Nodes called FIONAs to transfer data at near full network speeds. It has adopted Kubernetes to orchestrate software containers across its resources. The PRP provides petabytes of distributed storage and hundreds of GPUs for machine learning. It allows researchers to perform data-intensive science across multiple universities much faster than possible individually.
The Pacific Research Platform: a Science-Driven Big-Data Freeway SystemLarry Smarr
The Pacific Research Platform (PRP) is a multi-institutional partnership that establishes a high-capacity "big data freeway system" spanning the University of California campuses and other research universities in California to facilitate rapid data access and sharing between researchers and institutions. Fifteen multi-campus application teams in fields like particle physics, astronomy, earth sciences, biomedicine, and visualization drive the technical design of the PRP over five years. The goal of the PRP is to extend campus "Science DMZ" networks to allow high-speed data movement between research labs, supercomputer centers, and data repositories across campus, regional
Towards a High-Performance National Research Platform Enabling Digital ResearchLarry Smarr
The document summarizes Dr. Larry Smarr's keynote presentation on enabling a high-performance national research platform. It describes how multi-institutional research increasingly relies on access to large datasets, requiring new cyberinfrastructure. The Pacific Research Platform provides high-bandwidth networking between universities to support research collaborations across disciplines. The next steps involve scaling this model into a national and global platform. The presentation highlights how the PRP enables various scientific applications and drives innovation through improved data transfer capabilities and distributed computing resources.
The document provides an overview of the Pacific Research Platform (PRP) and discusses its role in connecting researchers across institutions and enabling new applications. It summarizes the PRP's key components like Science DMZs, Data Transfer Nodes (FIONAs), and use of Kubernetes for container management. Several examples are given of how the PRP facilitates high-performance distributed data analysis, access to remote supercomputers, and sensor networks coupled to real-time computing. Upcoming work on machine learning applications and expanding the PRP internationally is also outlined.
The Pacific Research Platform: Building a Distributed Big-Data Machine-Learni...Larry Smarr
The document summarizes the Pacific Research Platform (PRP) which connects researchers across multiple universities with high-speed networks and computing resources for big data and machine learning applications. Key points:
- PRP connects 15 universities with optical networks, distributed storage devices (FIONAs), and over 350 GPUs for data analysis and AI training.
- It allows researchers to rapidly share and analyze large datasets, with one example reducing a workflow from 19 days to 52 minutes.
- Other projects using PRP resources include climate modeling, astrophysics simulations, and machine learning courses involving thousands of students.
Looking Back, Looking Forward NSF CI Funding 1985-2025Larry Smarr
This document provides an overview of the development of national research platforms (NRPs) from 1985 to the present, with a focus on the Pacific Research Platform (PRP). It describes the evolution of the PRP from early NSF-funded supercomputing centers to today's distributed cyberinfrastructure utilizing optical networking, containers, Kubernetes, and distributed storage. The PRP now connects over 15 universities across the US and internationally to enable data-intensive science and machine learning applications across multiple domains. Going forward, the document discusses plans to further integrate regional networks and partner with new NSF-funded initiatives to develop the next generation of NRPs through 2025.
The Pacific Research Platform (PRP) is a multi-institutional cyberinfrastructure project that connects researchers across California and beyond to share large datasets. It spans the 10 University of California campuses, major private research universities, supercomputer centers, and some out-of-state universities. Fifteen multi-campus research teams in fields like physics, astronomy, earth sciences, biomedicine, and multimedia will drive the technical needs of the PRP over five years. The goal is to create a "big data freeway" to allow high-speed sharing of data between research labs, supercomputers, and repositories across multiple networks without performance loss over long distances.
The Pacific Research Platform: A Regional-Scale Big Data Analytics Cyberinfra...Larry Smarr
The document discusses the Pacific Research Platform (PRP), a regional big data cyberinfrastructure connecting researchers across California universities. PRP provides high-speed networks and data transfer nodes to enable sharing of large datasets for projects like medical imaging, cryo-electron microscopy, and machine learning. Recent grants are expanding PRP to add GPUs and non-von Neumann processors to support these computationally intensive applications.
Pacific Research Platform Science DriversLarry Smarr
The document discusses the vision and progress of the Pacific Research Platform (PRP) in creating a "big data freeway" across the West Coast to enable data-intensive science. It outlines how the PRP builds on previous NSF and DOE networking investments to provide dedicated high-performance computing resources, like GPU clusters and Jupyter hubs, connected by high-speed networks at multiple universities. Several science driver teams are highlighted, including particle physics, astronomy, microbiology, earth sciences, and visualization, that will leverage PRP resources for large-scale collaborative data analysis projects.
CENIC: Pacific Wave and PRP Update Big News for Big DataLarry Smarr
The document discusses the Pacific Wave exchange and Pacific Research Platform (PRP). It provides an overview of Pacific Wave, including its history and connectivity across the Pacific and western US. It then discusses how the PRP will build on infrastructure projects to create a high-speed "big data freeway" for science across California universities. This will allow researchers to more easily share and analyze large datasets for projects in areas like climate modeling, cancer genomics, astronomy and particle physics. Details are provided on specific science applications and datasets that will benefit from the enhanced connectivity of the PRP.
Internet & Climate Change: Cyberinfrastructure for a Carbon-Constrained WorldLarry Smarr
- Internet and information technologies (ICT) can play a key role in addressing climate change by enabling efficiency gains across multiple sectors that could reduce greenhouse gas emissions up to 5 times more than ICT's own carbon footprint.
- University campuses can serve as living laboratories for testing green ICT solutions and infrastructure to reduce emissions from buildings, transportation, electricity generation and distribution.
- Advances in machine learning and brain-inspired computing will be necessary to develop low-power exascale supercomputers needed to fully model and simulate climate systems.
The document summarizes Dr. Larry Smarr's presentation on the Pacific Research Platform (PRP) and its role in working toward a national research platform. It describes how PRP has connected research teams and devices across multiple UC campuses for over 15 years. It also details PRP's innovations like Flash I/O Network Appliances (FIONAs) and use of Kubernetes to manage distributed resources. Finally, it outlines opportunities to further integrate PRP with the Open Science Grid and expand the platform internationally through partnerships.
Machine Learning in Healthcare DiagnosticsLarry Smarr
Machine learning and artificial intelligence are rapidly transforming healthcare and medicine. Advances in genetic sequencing have enabled the mapping of human and microbial genomes at low costs. Researchers are using machine learning to analyze genomic and microbiome data to better understand health and disease. Non-von Neumann brain-inspired computing architectures are being developed for machine learning applications and could accelerate medical research and diagnostics. These technologies may help create personalized health coaching and move medicine from reactive sickcare to proactive healthcare.
National Federated Compute Platforms: The Pacific Research PlatformLarry Smarr
The Pacific Research Platform (PRP) is a multi-institution hypercluster that connects science DMZs across 25 partner campuses using FIONA data transfer nodes and 10-100Gbps networks. PRP adopted Kubernetes and Rook to orchestrate petabytes of distributed storage and GPUs for data science applications. A CHASE-CI grant added machine learning capabilities. PRP is working to federate with the Open Science Grid and become a prototype for a future National Research Platform connecting regional networks.
Positioning University of California Information Technology for the Future: S...Larry Smarr
05.02.15
Invited Talk
The Vice Chancellor of Research and Chief Information Officer Summit
“Information Technology Enabling Research at the University of California”
Title: Positioning University of California Information Technology for the Future: State, National, and International IT Infrastructure Trends and Directions
Oakland, CA
Analyzing Large Earth Data Sets: New Tools from the OptiPuter and LOOKING Pro...Larry Smarr
The document discusses two projects, OptIPuter and LOOKING, that aim to analyze large earth data sets using optical networking and grid technologies. OptIPuter extends grid middleware to dedicated optical circuits for earth and medical sciences. LOOKING builds on OptIPuter to provide real-time control of ocean observatories through web and grid services integrated over optical networks. Both projects represent efforts to develop cyberinfrastructure for interactive analysis of remote earth science data and instruments.
An Integrated Science Cyberinfrastructure for Data-Intensive ResearchLarry Smarr
This document summarizes Dr. Larry Smarr's vision for an integrated science cyberinfrastructure to support data-intensive research. It discusses the exponential growth of digital data and need for dedicated high-bandwidth networks and data repositories. Specific examples are provided of initiatives at UCSD, regional optical networks connecting research institutions, and national projects like the Open Science Grid and Cancer Genomics Hub that are creating cyberinfrastructure to enable data-intensive scientific discovery.
Cognitive Hardware and Software Ecosystem Community Infrastructure (CHASE-CI)Larry Smarr
This document summarizes Dr. Larry Smarr's presentation on the Cognitive Hardware and Software Ecosystem Community Infrastructure (CHASE-CI) project. The project received two NSF grants totaling over $5 million to create a regional cyberinfrastructure linking multiple universities. This includes a "Big Data Superhighway" linking campus networks and a machine learning layer with 256 GPUs for faculty and students. The goal is to map machine learning algorithms to novel architectures like GPUs, FPGAs, and neuromorphic chips to support data science and AI applications.
CHASE-CI: A Distributed Big Data Machine Learning PlatformLarry Smarr
This document summarizes a talk given by Professor Ken Kreutz-Delgado on distributed machine learning platforms and brain-inspired computing. It discusses the Pacific Research Platform (PRP) which connects multiple universities and research institutions. The PRP uses FIONA appliances and Kubernetes to distribute storage and processing. A new NSF grant will add GPUs across 10 campuses for training AI algorithms on big data. The talk envisions connecting the PRP with clouds of GPUs and non-von Neumann processors like IBM's TrueNorth chip. Calit2's Pattern Recognition Lab uses different processors including TrueNorth to explore machine learning algorithms.
A California-Wide Cyberinfrastructure for Data-Intensive ResearchLarry Smarr
The document discusses creating a California-wide cyberinfrastructure for data-intensive research. It outlines efforts to connect all UC campuses and other research institutions across California with high-speed optical networks. This would create a "big data plane" to share large datasets. Several campuses have received NSF grants to upgrade their networks and implement Science DMZ architectures with 10-100Gbps connections to CENIC. Connecting these resources would provide researchers access to high-performance computing, large scientific instruments, and datasets. This would support collaborative big data science across disciplines like physics, climate modeling, genomics and microscopy.
Opening Keynote Lecture
15th Annual ON*VECTOR International Photonics Workshop
Calit2’s Qualcomm Institute
University of California, San Diego
February 29, 2016
Creating a Big Data Machine Learning Platform in CaliforniaLarry Smarr
Big Data Tech Forum: Big Data Enabling Technologies and Applications
San Diego Chinese American Science and Engineering Association (SDCASEA)
Sanford Consortium
La Jolla, CA
December 2, 2017
The Pacific Research Platform:a Science-Driven Big-Data Freeway SystemLarry Smarr
The Pacific Research Platform will create a regional "Big Data Freeway System" along the West Coast to support science. It will connect major research institutions with high-speed optical networks, allowing them to share vast amounts of data and computational resources. This will enable new forms of collaborative, data-intensive research for fields like particle physics, astronomy, biomedicine, and earth sciences. The first phase aims to establish a basic networked infrastructure, with later phases advancing capabilities to 100Gbps and beyond with security and distributed technologies.
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The Rise of Supernetwork Data Intensive ComputingLarry Smarr
Invited Remote Lecture to SC21
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St. Louis, Missouri
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My Remembrances of Mike Norman Over The Last 45 YearsLarry Smarr
Mike Norman has been a leader in computational astrophysics for over 45 years. Some of his influential work includes:
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Larry Smarr discusses quantifying his body and health over time through extensive self-tracking. He measures various biomarkers through regular blood tests and analyzes his gut microbiome by sequencing stool samples. This revealed issues like chronic inflammation and an unhealthy microbiome. Smarr then took steps like a restricted eating window and increasing plant diversity in his diet, which reversed metabolic syndrome issues and correlated with shifts in his microbiome ecology. His goal is to continue precisely measuring factors like toxins, hormones, gut permeability and food/supplement impacts to further optimize his health.
Panel: Reaching More Minority Serving InstitutionsLarry Smarr
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This document summarizes a presentation on global petascale to exascale workflows for data intensive sciences. It discusses a partnership convened by the GNA-G Data Intensive Sciences Working Group with the mission of meeting challenges faced by data-intensive science programs. Cornerstone concepts that will be demonstrated include integrated network and site resource management, model-driven frameworks for resource orchestration, end-to-end monitoring with machine learning-optimized data transfers, and integrating Qualcomm's GradientGraph with network services to optimize applications and science workflows.
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Distributed Cyberinfrastructure to Support Big Data Machine Learning
1. “Distributed Cyberinfrastructure
to Support Big Data Machine Learning”
Panel on the Future of Machine Learning
California Institute for Telecommunications and Information Technology
University of California, Irvine
May 24, 2018
Dr. Larry Smarr
Director, California Institute for Telecommunications and Information Technology
Harry E. Gruber Professor,
Dept. of Computer Science and Engineering
Jacobs School of Engineering, UCSD
http://lsmarr.calit2.net
1
2. Based on Community Input and on ESnet’s Science DMZ Concept,
NSF Has Made Over 200 Campus-Level Awards in 44 States
Source: Kevin Thompson, NSF
3. How UCSD DMZ Network Transforms Big Data Microbiome Science:
Preparing for Knight/Smarr 1 Million Core-Hour Analysis
Knight Lab
FIONA
10Gbps
Gordon
Prism@UCSD
Data Oasis
7.5PB,
200GB/s
Knight 1024 Cluster
In SDSC Co-Lo
CHERuB
100Gbps
Emperor & Other Vis Tools
64Mpixel Data Analysis Wall
120Gbps
40Gbps
1.3Tbps
4. • FIONAs PCs [a.k.a ESnet DTNs]:
– ~$8,000 Big Data PC with:
– 1 CPU
– 10/40 Gbps Network Interface Cards
– 3 TB SSDs or 100+ TB Disk Drive
– Extensible for Higher Performance to:
– +Up to 38 Intel CPUs
– +Up to 8 GPUs [4M GPU Core Hours/Week]
– +NVMe SSDs for 100Gbps Disk-to-Disk
– +Up to 160 TB Disks for Data Posting
– $700 10Gpbs FIONAs Being Tested
• FIONettes are $250 FIONAs
– 1Gbps NIC With USB-3 for Flash Storage or SSD
Big Data Science Data Transfer Nodes (DTNs)-
Flash I/O Network Appliances (FIONAs)
Phil Papadopoulos, SDSC &
Tom DeFanti, Joe Keefe & John Graham, Calit2
Key Innovation: UCSD Designed Flash I/O Network Appliances (FIONAs)
To Provide Disk-to-Disk Data Transfer at Full Speed on 10/40/100G Networks
FIONAS—10/40G, $8,000
FIONette—1G, $250
5. Logical Next Step: The Pacific Research Platform Networks Campus DMZs
to Create a Regional End-to-End Science-Driven “Big Data Superhighway” System
(GDC)
NSF CC*DNI Grant
$5M 10/2015-10/2020
PI: Larry Smarr, UC San Diego Calit2
Co-PIs:
• Camille Crittenden, UC Berkeley CITRIS,
• Tom DeFanti, UC San Diego Calit2/QI,
• Philip Papadopoulos, UCSD SDSC,
• Frank Wuerthwein, UCSD Physics and SDSC
Letters of Commitment from:
• 50 Researchers from 15 Campuses
• 32 IT/Network Organization Leaders
NSF Program Officer: Amy Walton
Source: John Hess, CENIC
6. PRP National-Scale Experimental Distributed Testbed:
Using Internet2 to Connect Early-Adopter Quilt Regional R&E Networks
Original PRP
Extended PRP
Testbed
Announced at Internet2 Global Summit May 8, 2018
7. PRP’s First 2.5 Years:
Connecting Multi-Campus Application Teams and Devices
Earth
Sciences
8. Data Transfer Rates From 40 Gbps DTN in UCSD Physics Building,
Across Campus on PRISM DMZ, Then to Chicago’s Fermilab Over CENIC/ESnet
Based on This Success,
Würthwein Will Upgrade 40G DTN to 100G
For Bandwidth Tests & Kubernetes Integration
With OSG, Caltech, and UCSC
Source: Frank Würthwein, OSG, UCSD/SDSC, PRP
9. FIONA8
FIONA8
100G Epyc NVMe
40G 160TB
100G NVMe 6.4T
SDSU
100G Gold NVMe
March 2018 John Graham, UCSD
100G NVMe 6.4T
Caltech
40G 160TB
UCAR
FIONA8
UCI
FIONA8
FIONA8
FIONA8
FIONA8
FIONA8
FIONA8
FIONA8
FIONA8
sdx-controller
controller-0
Calit2
100G Gold FIONA8
SDSC
40G 160TB
UCR 40G 160TB
USC
40G 160TB
UCLA
40G 160TB
Stanford
40G 160TB
UCSB
100G NVMe 6.4T
40G 160TB
UCSC
40G 160TB
Hawaii
Running Kubernetes/Rook/Ceph On PRP
Allows Us to Deploy a Distributed PB+ of Storage for Posting Science Data
Rook/Ceph - Block/Object/FS
Swift API compatible with
SDSC, AWS, and Rackspace
Kubernetes
Centos7
10. UC San Diego Jaffe Lab (SIO) Scripps Plankton Camera
Off the SIO Pier with Fiber Optic Network
11. Over 1 Billion Images So Far!
Requires Machine Learning for Automated Image Analysis and Classification
Phytoplankton: Diatoms
Zooplankton: Copepods
Zooplankton: Larvaceans
Source: Jules Jaffe, SIO
”We are using the FIONAs for image processing...
this includes doing Particle Tracking Velocimetry
that is very computationally intense.”-Jules Jaffe
12. New NSF CHASE-CI Grant Creates a Community Cyberinfrastructure:
Adding a Machine Learning Layer Built on Top of the Pacific Research Platform
Caltech
UCB
UCI UCR
UCSD
UCSC
Stanford
MSU
UCM
SDSU
NSF Grant for High Speed “Cloud” of 256 GPUs
For 30 ML Faculty & Their Students at 10 Campuses
for Training AI Algorithms on Big Data
NSF Program Officer: Mimi McClure
13. FIONA8: Adding GPUs to FIONAs
Supports Data Science Machine Learning
Multi-Tenant Containerized GPU JupyterHub
Running Kubernetes / CoreOS
Eight Nvidia GTX-1080 Ti GPUs
32GB RAM, 3TB SSD, 40G & Dual 10G ports
Source: John Graham, Calit2
14. 48 GPUs for
OSG Applications
UCSD Adding >350 Game GPUs to Data Sciences Cyberinfrastructure -
Devoted to Data Analytics and Machine Learning
SunCAVE 70 GPUs
WAVE + Vroom 48 GPUs
FIONA with
8-Game GPUs
95 GPUs
for Students
CHASE-CI Grant Provides
96 GPUs at UCSD
for Training AI Algorithms on Big Data
Plus 288 64-bit GPUs
On SDSC’s Comet
15. Next Step: Surrounding the PRP Machine Learning Platform
With Clouds of GPUs and Non-Von Neumann Processors
Microsoft Installs Altera FPGAs
into Bing Servers &
384 into TACC for Academic Access
CHASE-CI
64-TrueNorth
Cluster
64-bit GPUs
4352x NVIDIA Tesla V100 GPUs
16. Pattern Computer Was Just Announced -
We Will Provide Access Through CHASE-CI
HE
UC
CCD
ICD
May 23, 2018
Mark Anderson, CEO Announcing Pattern Computer
Reduction of 10,000 Variables to 39
For Microbiome Protein Families
Smarr, et. al (2018)
www.patterncomputer.com/img/pdf/KEGGs_5.22_final.pdf
17. Calit2 Has Established Labs On Both UC San Diego and UC Irvine Campuses
For Machine Learning on von Neumann and NvN Processors
Charless Fowlkes, Director
Ken Kreutz Delgado, Director
18. CHASE-CI’s ML Researchers Are Exploring Mapping
Machine Learning Algorithm Families Onto Novel Architectures
Qualcomm
Institute
1. Deep & Recurrent Neural Networks (DNN, RNN)
2. Reinforcement Learning (RL)
3. Variational Autoencoder (VAE) and Markov Chain Monte Carlo (MCMC)
4. Support Vector Machine (SVM)
5. Sparse Signal Processing (SSP) and Sparse Baysian Learning (SBL)
6. Latent Variable Analysis (PCA, ICA)