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.
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 document discusses the growing carbon footprint of information and communication technologies (ICT) and efforts to make cyberinfrastructure more energy efficient and environmentally sustainable. Specifically, it mentions that (1) ICT energy usage is growing rapidly and accounts for 2% of global greenhouse gas emissions, (2) universities are working on initiatives like the GreenLight project to reduce ICT energy usage through techniques like dynamic power management, and (3) further research is needed to develop more energy-efficient computing technologies, data center designs, and videoconferencing solutions to reduce the need for travel.
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.
Toward a Global Interactive Earth Observing CyberinfrastructureLarry Smarr
The document discusses the need for a new generation of cyberinfrastructure to support interactive global earth observation. It outlines several prototyping projects that are building examples of systems enabling real-time control of remote instruments, remote data access and analysis. These projects are driving the development of an emerging cyber-architecture using web and grid services to link distributed data repositories and simulations.
High Performance Cyberinfrastructure for Data-Intensive ResearchLarry Smarr
This document summarizes a lecture given by Dr. Larry Smarr on high performance cyberinfrastructure for data-intensive research. The summary discusses:
1) The need for dedicated high-bandwidth networks separate from the shared internet to enable big data research due to the increasing volume of digital scientific data.
2) Extensions being made to networks like CENIC in California to provide campus "Big Data Freeways" connecting instruments, computing resources, and remote facilities.
3) The use of networks like HPWREN to provide high-performance wireless access for data-intensive applications in rural areas like astronomy, wildfire detection, and more.
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.
The Jump to Light Speed - Data Intensive Earth Sciences are Leading the Way t...Larry Smarr
05.06.14
Keynote to the 15th Federation of Earth Science Information Partners Assembly Meeting: Linking Data and Information to Decision Makers
Title: The Jump to Light Speed - Data Intensive Earth Sciences are Leading the Way to the International LambdaGrid
San Diego, CA
08.04.14
Invited Talk
National Astrobiology Institute Executive Council Meeting
Astrobiology Science Conference 2008
Santa Clara Convention Center
Title: High Performance Collaboration
Santa Clara, CA
Calit2 as a Model for Collaborative InnovationLarry Smarr
- Calit2 was established in 2000 as a collaborative research institute between UCSD and UCI to bring together faculty from different disciplines to work on emerging technologies through multidisciplinary teams.
- It has over 1000 researchers working across both campuses in fields like nanotechnology, biomedicine, digital arts and more.
- Calit2 has established numerous partnerships internationally and in industry, and has facilities like clean rooms, virtual reality labs and more that enable cutting edge research.
- One example is how Calit2 worked with NASA to reduce the time to receive satellite images during wildfires, and has since used VR to help plan fire response.
The Pacific Research Platform Enables Distributed Big-Data Machine-LearningLarry Smarr
The Pacific Research Platform enables distributed big data machine learning by connecting scientific instruments, sensors, and supercomputers across California and the United States with high-speed optical networks. Key components include FIONA data transfer nodes that allow fast disk-to-disk transfers near the theoretical maximum, Kubernetes to orchestrate distributed computing resources, and the Nautilus hypercluster which aggregates thousands of CPU cores and GPUs into a unified platform. This infrastructure has accelerated many scientific workflows and supported cutting-edge research in fields such as astronomy, oceanography, climate science, and particle physics.
Peering The Pacific Research Platform With The Great Plains NetworkLarry Smarr
The Pacific Research Platform (PRP) connects research institutions across the western United States with high-speed networks to enable data-intensive science collaborations. Key points:
- The PRP connects 15 campuses across California and links to the Great Plains Network, allowing researchers to access remote supercomputers, share large datasets, and collaborate on projects like analyzing data from the Large Hadron Collider.
- The PRP utilizes Science DMZ architectures with dedicated data transfer nodes called FIONAs to achieve high-speed transfer of large files. Kubernetes is used to manage distributed storage and computing resources.
- Early applications include distributed climate modeling, wildfire science, plankton imaging, and cancer genomics. The PR
Why Researchers are Using Advanced NetworksLarry Smarr
07.07.03
Remote Talk from Calit2 to:
Building KAREN Communities for Collaboration Forum
KIWI Advanced Research and Education Network
University of Auckland, Auckland City, New Zealand
Title: Why Researchers are Using Advanced Networks
La Jolla, CA
The document summarizes the creation and evolution of Calit2, the California Institute for Telecommunications and Information Technology, a partnership between UC San Diego and UC Irvine. It describes how Calit2 was established in 2001 with a mission to explore how emerging technologies could transform applications through interdisciplinary research. With support from the state and industry partners, Calit2 has grown facilities and research projects in areas like networking, virtual reality, biomedicine, and more recently brain-inspired computing and machine learning.
The Pacific Research Platform: Building a Distributed Big Data Machine Learni...Larry Smarr
This document summarizes Dr. Larry Smarr's invited talk about the Pacific Research Platform (PRP) given at the San Diego Supercomputer Center in April 2019. The PRP is building a distributed big data machine learning supercomputer by connecting high-performance computing and data resources across multiple universities in California and beyond using high-speed networks. It provides researchers with petascale computing power, distributed storage, and tools like Kubernetes to enable collaborative data-intensive science across institutions.
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 (PRP) interconnects campus DMZs across multiple institutions to provide high-speed connectivity for data-intensive research.
- The PRP utilizes specialized data transfer nodes called FIONAs that provide disk-to-disk transfer speeds of 10-100Gbps.
- Early applications of the PRP include distributing telescope data between UC campuses, connecting particle physics experiments to computing resources, and enabling real-time wildfire sensor data analysis.
Panel Presentation - Tom DeFanti with Larry Smarr and Frank Wuerthwein - Naut...Larry Smarr
The document discusses the Nautilus distributed community cyberinfrastructure cluster which provides over 1,100 GPUs and 5 petabytes of storage distributed across research institutions in the US. It is supported by NSF grants and aims to enhance and sustain its resources while expanding its user community and supporting research in areas like machine learning, computational media, and science. The cluster sees heavy usage from projects in fields such as high energy physics, astronomy, wildfires and more.
Distributed Cyberinfrastructure to Support Big Data Machine LearningLarry Smarr
Panel on the Future of Machine Learning
California Institute for Telecommunications and Information Technology
University of California, Irvine
May 24, 2018
Distributed Cyberinfrastructure to Support Big Data Machine LearningLarry Smarr
Panel on the Future of Machine Learning
California Institute for Telecommunications and Information Technology
University of California, Irvine
May 24, 2018
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.
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.
Opening Keynote Lecture
15th Annual ON*VECTOR International Photonics Workshop
Calit2’s Qualcomm Institute
University of California, San Diego
February 29, 2016
The Rise of Supernetwork Data Intensive ComputingLarry Smarr
Invited Remote Lecture to SC21
The International Conference for High Performance Computing, Networking, Storage, and Analysis
St. Louis, Missouri
November 18, 2021
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:
- Cosmic jet simulations in the early 1980s which helped explain phenomena from galactic centers.
- Pioneering the use of adaptive mesh refinement in the 1990s to achieve dynamic load balancing on supercomputers.
- Massive cosmology simulations in the late 2000s with over 100 trillion particles using thousands of processors across multiple supercomputing sites, producing petabytes of data.
- Developing end-to-end workflows in the 2000s to couple supercomputers, high-speed networks, and large visualization systems to enable real-time analysis of extremely large astrophysics simulations.
Metagenics How Do I Quantify My Body and Try to Improve its Health? June 18 2019Larry Smarr
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.
Email Marketing in Odoo 17 - Odoo 17 SlidesCeline George
Email marketing is used to send advertisements or commercial messages to specific groups of people by using email. Email Marketing also helps to track the campaign’s overall effectiveness. This slide will show the features of odoo 17 email marketing.
Description:
Welcome to the comprehensive guide on Relational Database Management System (RDBMS) concepts, tailored for final year B.Sc. Computer Science students affiliated with Alagappa University. This document covers fundamental principles and advanced topics in RDBMS, offering a structured approach to understanding databases in the context of modern computing. PDF content is prepared from the text book Learn Oracle 8I by JOSE A RAMALHO.
Key Topics Covered:
Main Topic : PL/SQL
Sub-Topic :
Structure of PL/SQL Block, Declaration Section, Variable, Constant, Execution Section, Exception, How PL/SQL works, Control Structures, If then Command,
Loop Command, Loop with IF, Loop with When, For Loop Command, While Command, Integrating SQL in PL/SQL program.
Target Audience:
Final year B.Sc. Computer Science students at Alagappa University seeking a solid foundation in RDBMS principles for academic and practical applications.
URL for previous slides
Unit V
Chapter 15
Unit IV
Chapter 14 Synonym : https://www.slideshare.net/slideshow/lecture_notes_unit4_chapter14_synonyms-pdf/270327685
Chapter 13 Users, Privileges : https://www.slideshare.net/slideshow/lecture-notes-unit4-chapter13-users-roles-and-privileges/270304806
Chapter 12 View : https://www.slideshare.net/slideshow/rdbms-lecture-notes-unit4-chapter12-view/270199683
Chapter 11 Sequence: https://www.slideshare.net/slideshow/sequnces-lecture_notes_unit4_chapter11_sequence/270134792
chapter 8,9 and 10 : https://www.slideshare.net/slideshow/lecture_notes_unit4_chapter_8_9_10_rdbms-for-the-students-affiliated-by-alagappa-university/270123800
About the Author:
Dr. S. Murugan is Associate Professor at Alagappa Government Arts College, Karaikudi. With 23 years of teaching experience in the field of Computer Science, Dr. S. Murugan has a passion for simplifying complex concepts in database management.
Disclaimer:
This document is intended for educational purposes only. The content presented here reflects the author’s understanding in the field of RDBMS as of 2024.
Lecture Notes Unit4 Chapter13 users , roles and privilegesMurugan146644
Description:
Welcome to the comprehensive guide on Relational Database Management System (RDBMS) concepts, tailored for final year B.Sc. Computer Science students affiliated with Alagappa University. This document covers fundamental principles and advanced topics in RDBMS, offering a structured approach to understanding databases in the context of modern computing. PDF content is prepared from the text book Learn Oracle 8I by JOSE A RAMALHO.
Key Topics Covered:
Main Topic : USERS, Roles and Privileges
In Oracle databases, users are individuals or applications that interact with the database. Each user is assigned specific roles, which are collections of privileges that define their access levels and capabilities. Privileges are permissions granted to users or roles, allowing actions like creating tables, executing procedures, or querying data. Properly managing users, roles, and privileges is essential for maintaining security and ensuring that users have appropriate access to database resources, thus supporting effective data management and integrity within the Oracle environment.
Sub-Topic :
Definition of User, User Creation Commands, Grant Command, Deleting a user, Privileges, System privileges and object privileges, Grant Object Privileges, Viewing a users, Revoke Object Privileges, Creation of Role, Granting privileges and roles to role, View the roles of a user , Deleting a role
Target Audience:
Final year B.Sc. Computer Science students at Alagappa University seeking a solid foundation in RDBMS principles for academic and practical applications.
URL for previous slides
chapter 8,9 and 10 : https://www.slideshare.net/slideshow/lecture_notes_unit4_chapter_8_9_10_rdbms-for-the-students-affiliated-by-alagappa-university/270123800
Chapter 11 Sequence: https://www.slideshare.net/slideshow/sequnces-lecture_notes_unit4_chapter11_sequence/270134792
Chapter 12 View : https://www.slideshare.net/slideshow/rdbms-lecture-notes-unit4-chapter12-view/270199683
About the Author:
Dr. S. Murugan is Associate Professor at Alagappa Government Arts College, Karaikudi. With 23 years of teaching experience in the field of Computer Science, Dr. S. Murugan has a passion for simplifying complex concepts in database management.
Disclaimer:
This document is intended for educational purposes only. The content presented here reflects the author’s understanding in the field of RDBMS as of 2024.
How to Fix Field Does Not Exist Error in Odoo 17Celine George
This slide will represent how to fix the error field does not exist in a model in Odoo 17. So if you got an error field does not exist it typically means that you are trying to refer a field that doesn’t exist in the model or view.
Dear Sakthi Thiru Dr. G. B. Senthil Kumar,
It is with great honor and respect that we extend this formal invitation to you. As a distinguished leader whose presence commands admiration and reverence, we cordially invite you to join us in celebrating the 25th anniversary of our graduation from Adhiparasakthi Engineering College on 27th July, 2024. we would be honored to have you by our side as we reflect on the achievements and memories of the past 25 years.
Life of Ah Gong and Ah Kim ~ A Story with Life Lessons (Hokkien, English & Ch...OH TEIK BIN
A PowerPoint Presentation of a fictitious story that imparts Life Lessons on loving-kindness, virtue, compassion and wisdom.
The texts are in Romanized Hokkien, English and Chinese.
For the Video Presentation with audio narration in Hokkien, please check out the Link:
https://vimeo.com/manage/videos/987932748
How to install python packages from PycharmCeline George
In this slide, let's discuss how to install Python packages from PyCharm. In case we do any customization in our Odoo environment, sometimes it will be necessary to install some additional Python packages. Let’s check how we can do this from PyCharm.
C++ Interview Questions and Answers PDF By ScholarHat
The Pacific Research Platform: Building a Distributed Big-Data Machine-Learning Cyberinfrastructure
1. “The Pacific Research Platform:
Building a Distributed Big-Data Machine-Learning
Cyberinfrastructure”
Briefing
Jacobs School of Engineering
University of California San Diego
July 18, 2019
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
2. UC San Diego’s Calit2 & SDSC Have Pioneered Big-Data Cyberinfrastructure for 17 Years
with NSF Grants: OptIPuter, Quartzite, Prism, CHERuB, PRP, CHASE-CI, TNRP
OptIPuter
PI Smarr,
Co-PI DeFanti
Co-PI Papadopoulos, Ellisman
2002-2009
Quartzite
PI Papadopoulos,
Co-PI Smarr, Ford,
Fainman
3. 2013-2015: Creating a “Big Data” Backplane on Campus:
NSF CC-NIE Funded Prism@UCSD and CHERuB
Prism@UCSD, Phil Papadopoulos, SDSC, Calit2, PI; Smarr co-PI
CHERuB, Mike Norman, SDSC PI
CHERuB
4. (GDC)
2015-2020: The Pacific Research Platform Connects Campus “Big Data Freeways”
to Create a Regional End-to-End Science-Driven “Big Data Superhighway” System
NSF CC*DNI Grant
$6M 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
Source: John Hess, CENIC
UCOP CIO Tom Andriola
Provided Funds and ITLC Support
for Using Ten UC Campuses
For Advanced Technology Testing
5. 2017-2020: CHASE-CI Adds
Machine-Learning to the Data-Science Community Cyberinfrastructure
Caltech
UCB
UCI UCR
UCSD
UCSC
Stanford
MSU
UCM
SDSU
NSF Grant for 256 High Speed “Cloud” GPUs
For 32 ML Faculty & Their Students at 10 Campuses
To Train AI Algorithms on Big Data
6. PRP Engineers Designed and Built Several Generations
of Optical-Fiber Big-Data Flash I/O Network Appliances (FIONAs)
UCSD-Designed FIONAs Solved the Disk-to-Disk Data Transfer Problem
at Near Full Speed on Best-Effort 10G, 40G and 100G Networks
FIONAs Designed by UCSD’s Phil Papadopoulos, John Graham,
Joe Keefe, and Tom DeFanti
FIONette—
1G, $250
Used for
Training 50
Engineers in
2018-2019
Two FIONA DTNs at UC Santa Cruz: 40G & 100G
Up to 200 TeraByte Rotating Storage
Add Up to 8 Nvidia GPUs Per FIONA
To Add Machine Learning Capability
Over 100 Now Deployed on PRP
7. 48 GPUs for
OSG Applications
UCSD Has Added >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
104 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
8. UCSD’s ITS Adapted PRP FIONA8s
To Support Data Science Courses
Instructional Data Science
Machine Learning Platform:
Instead of Spending
~$20,000/Quarter/Course on
Commercial Clouds:
97 Courses over 6 Quarters
$4M vs. $240K over 12 Quarters
At least 20,000 Students
Adam Tilghman, ITS
Source: UCSD ITS
9. The Student GPUs
Have Supported a Broad Set of Courses Across Campus
Source: UCSD ITS
11. Student GPU Demand Is Variable
Allowing for Other Student Uses
Available to Support:
Independent Study,
For-Credit Research,
External Barter
Source: UCSD ITS
12. 2018-2019: PRP Game Changer!
Using Kubernetes to Orchestrate Containers Across the PRP
“Kubernetes is a way of stitching together
a collection of machines into,
basically, a big computer,”
--Craig Mcluckie, Google
and now CEO and Founder of Heptio
"Everything at Google runs in a container."
--Joe Beda,Google
14. Major CHASE-CI Usage by UCI
Over PRP to UCSD CPUs/GPUs
Cognitive Anteater
Robotics Laboratory
(CARL) supervised
by
Prof. Jeff Krichmar
UCICompVis Group
supervised by
Prof. Charless Fowlkes
#ofCores
Demo
Last Night
From
Data Think Tank Lab
2 Months
15. Very Cost-Effective for Academic Machine Learning and Data Sharing
• Data science researchers need DTNs with lots of storage, encryption and lots of GPUS
• One UC spends $40,000 in cloud GPU per published grad student paper
• Another spends $20,000 for undergrad ML AWS access in just one course
• Instead, add to our Nautilus hypercluster (or clone it & federate):
– UCSD ECE Department bought 4 FIONA8s, buying 4 more
– UCSD Physics Department. bought 3 FIONA8s, buying 3 more
– UCSD CSE researchers bought/are buying FIONA8s to add to Nautilus
– UCSD Instructional IT has 13 FIONA8s for Machine Learning/AI class labs
• Working Storage on Nautilus FIONAs is
– very inexpensive (12TB drives are ~$430 each—16 per FIONA. FISA encrypted drives @ same cost)
– and very high speed (most FIONAs are 40/100G and are located in ScienceDMZs)
Clemson’s Alex Feltus: “I cannot wait to add a node to the
Nautilus compute fabric!” 5/22/2019
19. Original PRP
CENIC/PW Link
2018-2019: National-Scale Pilot -
Using CENIC & Internet2 to Connect Quilt Regional R&E Networks
Announced May 8, 2018
Internet2 Global Summit
“Towards
The NRP”
3-Year Grant
Funded
by NSF
$2.5M
OAC-1826967
PI Smarr
Co-PIs Altintas
Papadopoulos
Wuerthwein
Rosing
Mgr: DeFanti
NRP Pilot
NSF CENIC Link
20. CENIC/PW Link
40G 3TB
U Hawaii
40G 160TB
NCAR-WY
40G 192TB
UWashington
100G FIONA
I2 Chicago
100G FIONA
I2 Kansas City
10G FIONA1
40G FIONA
UIC
100G FIONA
I2 NYC
40G 3TB
StarLight
United States PRP Nautilus Hypercluster FIONAs
We Now Connect 3 More Regionals and 3 Internet2 sites
21. Global PRP Nautilus Hypercluster Is Rapidly Increasing
Partners Beyond Our Original Partner in Amsterdam—May 2019
PRP
PRPv2
Nautilus
Transoceanic
Nodes
Guam
Asian Pacific RP
Transoceanic
Nodes
Australia
Korea
Singapore
Netherlands
10G 35TB
UvA
40G FIONA6
40G 28TB
KISTI
10G (coming)
U of Guam
100G 35TB
U of Queensland
Transoceanic Nodes Show Distance is Not the Barrier
to Above 5Gb/s Disk-to-Disk Performance
23. Director: F. Martin Ralph
Big Data Collaboration with:
Source: Scott Sellers, PhD CHRS; Postdoc CW3E
Collaboration on Atmospheric Water in the West
Between UC San Diego and UC Irvine
Director, Soroosh Sorooshian, UCSD
24. Calit2’s FIONA
SDSC’s COMET
Calit2’s FIONA
Pacific Research Platform (10-100 Gb/s)
GPUsGPUs
Complete Workflow Time: 19.2 Days52 Minutes!
UC, Irvine UC, San Diego
PRP Shortened Scott Sellar’s Workflow From 19.2 Days to 52 Minutes -
532 Times Faster!
Source: Scott Sellers, US State Dept.
25. OSG IceCube Usage on PRP (Purple Segment) 3/9/19:
Using 126 GPUs + 142 CPUs + 49 GB RAM
GPU Simulations Needed to Improve Ice Model.
=> Results in Significant Improvement in Pointing Resolution
for Multi-Messenger Astrophysics
IceCube
26. PRP Actively Develops Diversity
• Grants
– 3 Female co-PIs
– 1 Hispanic co-PI
• Campuses
– 8 Minority-Serving Institutions in PRP/Nautilus
• Workshops
– NRP’18 Workshop Program Committee 80% Female
– Multiple MSI, EPSCoR Focused Workshops Jackson State University
PRP MSI Workshop
Presenting
FIONettes
27. Installing FIONAs Across California in Late 2018 and Early 2019
To Enhance User’s CPU and GPU Computing, Data Posting, and Data Transfers
UC Merced
Stanford UC Santa Barbara
UC Riverside
UC Santa Cruz
UC Irvine