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
Machine and Deep Learning Application.
Applying big data learning techniques for a malware classification problem.
Code:
https://gist.github.com/indraneeld/7ffb182fd8eb87d6d463dedc001efad0
Acknowledgments:
Canadian Institute for Cybersecurity (CIC) project in collaboration with Canadian Centre for Cyber Security (CCCS).
This is a talk I gave at a Northwestern University - Complete Genomics Workshop on April 21, 2011 about using clouds to support research in genomics and related areas.
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
Machine and Deep Learning Application.
Applying big data learning techniques for a malware classification problem.
Code:
https://gist.github.com/indraneeld/7ffb182fd8eb87d6d463dedc001efad0
Acknowledgments:
Canadian Institute for Cybersecurity (CIC) project in collaboration with Canadian Centre for Cyber Security (CCCS).
This is a talk I gave at a Northwestern University - Complete Genomics Workshop on April 21, 2011 about using clouds to support research in genomics and related areas.
This talk explains how cloud computing to speed up your research, and provide capabilities that are otherwise out of reach. Big data, data science, machine learning, high-performance computing are all available on-demand using Microsoft Azure.
Researchers around the world can apply for free cloud computing time for their projects at www.Azure4Research.com
This talk describes our experiences from hosting scientific research application in the Microsoft Cloud. Covers an overview of Microsoft Azure capabilities, examples of big data analysis for science, data collections, science gateways and science virtual machine libraries.
A presentation at the NIH Workshop on Advanced Networking for Data-Intensive Biomedical Research. The talk covers our work with the science community on using cloud computing to enhance and improve basic research for data analysis and scientific discovery
A time efficient approach for detecting errors in big sensor data on cloudNexgen Technology
TO GET THIS PROJECT COMPLETE SOURCE CODE PLEASE CALL BEOLOW CONTACT DETAILS
MOBILE: 9791938249, 0413-2211159, WEB: WWW.NEXGENPROJECT.COM ,EMAIL:Praveen@nexgenproject.com
NEXGEN TECHNOLOGY provides total software solutions to its customers. Apsys works closely with the customers to identify their business processes for computerization and help them implement state-of-the-art solutions. By identifying and enhancing their processes through information technology solutions. NEXGEN TECHNOLOGY help it customers optimally use their resources.
The Discovery Cloud: Accelerating Science via Outsourcing and AutomationIan Foster
Director's Colloquium at Los Alamos National Laboratory, September 18, 2014.
We have made much progress over the past decade toward harnessing the collective power of IT resources distributed across the globe. In high-energy physics, astronomy, and climate, thousands work daily within virtual computing systems with global scope. But we now face a far greater challenge: Exploding data volumes and powerful simulation tools mean that many more--ultimately most?--researchers will soon require capabilities not so different from those used by such big-science teams. How are we to meet these needs? Must every lab be filled with computers and every researcher become an IT specialist? Perhaps the solution is rather to move research IT out of the lab entirely: to leverage the “cloud” (whether private or public) to achieve economies of scale and reduce cognitive load. In this talk, I explore the past, current, and potential future of large-scale outsourcing and automation for science.
4 TeraGrid Sites Have Focal Points:
SDSC – The Data Place
Large-scale and high-performance data analysis/handling
Every Cluster Node is Directly Attached to SAN
NCSA – The Compute Place
Large-scale, Large Flops computation
Argonne – The Viz place
Scalable Viz walls
Caltech – The Applications place
Data and flops for applications – Especially some of the GriPhyN Apps
Specific machine configurations reflect this
Accelerating data-intensive science by outsourcing the mundaneIan Foster
Talk at eResearch New Zealand Conference, June 2011 (given remotely from Italy, unfortunately!)
Abstract: Whitehead observed that "civilization advances by extending the number of important operations which we can perform without thinking of them." I propose that cloud computing can allow us to accelerate dramatically the pace of discovery by removing a range of mundane but timeconsuming research data management tasks from our consciousness. I describe the Globus Online system that we are developing to explore these possibilities, and propose milestones for evaluating progress towards smarter science.
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
This talk explains how cloud computing to speed up your research, and provide capabilities that are otherwise out of reach. Big data, data science, machine learning, high-performance computing are all available on-demand using Microsoft Azure.
Researchers around the world can apply for free cloud computing time for their projects at www.Azure4Research.com
This talk describes our experiences from hosting scientific research application in the Microsoft Cloud. Covers an overview of Microsoft Azure capabilities, examples of big data analysis for science, data collections, science gateways and science virtual machine libraries.
A presentation at the NIH Workshop on Advanced Networking for Data-Intensive Biomedical Research. The talk covers our work with the science community on using cloud computing to enhance and improve basic research for data analysis and scientific discovery
A time efficient approach for detecting errors in big sensor data on cloudNexgen Technology
TO GET THIS PROJECT COMPLETE SOURCE CODE PLEASE CALL BEOLOW CONTACT DETAILS
MOBILE: 9791938249, 0413-2211159, WEB: WWW.NEXGENPROJECT.COM ,EMAIL:Praveen@nexgenproject.com
NEXGEN TECHNOLOGY provides total software solutions to its customers. Apsys works closely with the customers to identify their business processes for computerization and help them implement state-of-the-art solutions. By identifying and enhancing their processes through information technology solutions. NEXGEN TECHNOLOGY help it customers optimally use their resources.
The Discovery Cloud: Accelerating Science via Outsourcing and AutomationIan Foster
Director's Colloquium at Los Alamos National Laboratory, September 18, 2014.
We have made much progress over the past decade toward harnessing the collective power of IT resources distributed across the globe. In high-energy physics, astronomy, and climate, thousands work daily within virtual computing systems with global scope. But we now face a far greater challenge: Exploding data volumes and powerful simulation tools mean that many more--ultimately most?--researchers will soon require capabilities not so different from those used by such big-science teams. How are we to meet these needs? Must every lab be filled with computers and every researcher become an IT specialist? Perhaps the solution is rather to move research IT out of the lab entirely: to leverage the “cloud” (whether private or public) to achieve economies of scale and reduce cognitive load. In this talk, I explore the past, current, and potential future of large-scale outsourcing and automation for science.
4 TeraGrid Sites Have Focal Points:
SDSC – The Data Place
Large-scale and high-performance data analysis/handling
Every Cluster Node is Directly Attached to SAN
NCSA – The Compute Place
Large-scale, Large Flops computation
Argonne – The Viz place
Scalable Viz walls
Caltech – The Applications place
Data and flops for applications – Especially some of the GriPhyN Apps
Specific machine configurations reflect this
Accelerating data-intensive science by outsourcing the mundaneIan Foster
Talk at eResearch New Zealand Conference, June 2011 (given remotely from Italy, unfortunately!)
Abstract: Whitehead observed that "civilization advances by extending the number of important operations which we can perform without thinking of them." I propose that cloud computing can allow us to accelerate dramatically the pace of discovery by removing a range of mundane but timeconsuming research data management tasks from our consciousness. I describe the Globus Online system that we are developing to explore these possibilities, and propose milestones for evaluating progress towards smarter science.
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
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
Evolving Cyberinfrastructure, Democratizing Data, and Scaling AI to Catalyze ...inside-BigData.com
In this deck from the Stanford HPC Conference, Nick Nystrom and Paola Buitrago provide an update from the Pittsburgh Supercomputing Center.
Nick Nystrom is Chief Scientist at the Pittsburgh Supercomputing Center (PSC). Nick is architect and PI for Bridges, PSC's flagship system that successfully pioneered the convergence of HPC, AI, and Big Data. He is also PI for the NIH Human Biomolecular Atlas Program’s HIVE Infrastructure Component and co-PI for projects that bring emerging AI technologies to research (Open Compass), apply machine learning to biomedical data for breast and lung cancer (Big Data for Better Health), and identify causal relationships in biomedical big data (the Center for Causal Discovery, an NIH Big Data to Knowledge Center of Excellence). His current research interests include hardware and software architecture, applications of machine learning to multimodal data (particularly for the life sciences) and to enhance simulation, and graph analytics.
Watch the video: https://youtu.be/LWEU1L1o7yY
Learn more: https://www.psc.edu/
and
http://www.hpcadvisorycouncil.com/events/2020/stanford-workshop/
Sign up for our insideHPC Newsletter: http://insidehpc.com/newsletter
06-04-2024 - NYC Tech Week - Discussion on Vector Databases, Unstructured Data and AI
Round table discussion of vector databases, unstructured data, ai, big data, real-time, robots and Milvus.
A lively discussion with NJ Gen AI Meetup Lead, Prasad and Procure.FYI's Co-Found
Adjusting primitives for graph : SHORT REPORT / NOTESSubhajit Sahu
Graph algorithms, like PageRank Compressed Sparse Row (CSR) is an adjacency-list based graph representation that is
Multiply with different modes (map)
1. Performance of sequential execution based vs OpenMP based vector multiply.
2. Comparing various launch configs for CUDA based vector multiply.
Sum with different storage types (reduce)
1. Performance of vector element sum using float vs bfloat16 as the storage type.
Sum with different modes (reduce)
1. Performance of sequential execution based vs OpenMP based vector element sum.
2. Performance of memcpy vs in-place based CUDA based vector element sum.
3. Comparing various launch configs for CUDA based vector element sum (memcpy).
4. Comparing various launch configs for CUDA based vector element sum (in-place).
Sum with in-place strategies of CUDA mode (reduce)
1. Comparing various launch configs for CUDA based vector element sum (in-place).
Learn SQL from basic queries to Advance queriesmanishkhaire30
Dive into the world of data analysis with our comprehensive guide on mastering SQL! This presentation offers a practical approach to learning SQL, focusing on real-world applications and hands-on practice. Whether you're a beginner or looking to sharpen your skills, this guide provides the tools you need to extract, analyze, and interpret data effectively.
Key Highlights:
Foundations of SQL: Understand the basics of SQL, including data retrieval, filtering, and aggregation.
Advanced Queries: Learn to craft complex queries to uncover deep insights from your data.
Data Trends and Patterns: Discover how to identify and interpret trends and patterns in your datasets.
Practical Examples: Follow step-by-step examples to apply SQL techniques in real-world scenarios.
Actionable Insights: Gain the skills to derive actionable insights that drive informed decision-making.
Join us on this journey to enhance your data analysis capabilities and unlock the full potential of SQL. Perfect for data enthusiasts, analysts, and anyone eager to harness the power of data!
#DataAnalysis #SQL #LearningSQL #DataInsights #DataScience #Analytics
Techniques to optimize the pagerank algorithm usually fall in two categories. One is to try reducing the work per iteration, and the other is to try reducing the number of iterations. These goals are often at odds with one another. Skipping computation on vertices which have already converged has the potential to save iteration time. Skipping in-identical vertices, with the same in-links, helps reduce duplicate computations and thus could help reduce iteration time. Road networks often have chains which can be short-circuited before pagerank computation to improve performance. Final ranks of chain nodes can be easily calculated. This could reduce both the iteration time, and the number of iterations. If a graph has no dangling nodes, pagerank of each strongly connected component can be computed in topological order. This could help reduce the iteration time, no. of iterations, and also enable multi-iteration concurrency in pagerank computation. The combination of all of the above methods is the STICD algorithm. [sticd] For dynamic graphs, unchanged components whose ranks are unaffected can be skipped altogether.
The Building Blocks of QuestDB, a Time Series Databasejavier ramirez
Talk Delivered at Valencia Codes Meetup 2024-06.
Traditionally, databases have treated timestamps just as another data type. However, when performing real-time analytics, timestamps should be first class citizens and we need rich time semantics to get the most out of our data. We also need to deal with ever growing datasets while keeping performant, which is as fun as it sounds.
It is no wonder time-series databases are now more popular than ever before. Join me in this session to learn about the internal architecture and building blocks of QuestDB, an open source time-series database designed for speed. We will also review a history of some of the changes we have gone over the past two years to deal with late and unordered data, non-blocking writes, read-replicas, or faster batch ingestion.
The affect of service quality and online reviews on customer loyalty in the E...
Cognitive Hardware and Software Ecosystem Community Infrastructure (CHASE-CI)
1. “Cognitive Hardware and Software Ecosystem
Community Infrastructure (CHASE-CI)”
Panel: AI and the Edge
Internet2 Global Summit
San Diego, CA
May 9, 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. (GDC)
Logical Next Step: The Pacific Research Platform Networks Campus DMZs
to Create a Regional End-to-End Science-Driven “Big Data Superhighway” System
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
3. 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
4. Calit2’s Pattern Recognition Lab is Exploring Mapping
Machine Learning Algorithm Families Onto Novel Architectures
Qualcomm
Institute
• Deep & Recurrent Neural Networks (DNN, RNN)
• Graph Theoretic
• Reinforcement Learning (RL)
• Clustering and other neighborhood-based
• Support Vector Machine (SVM)
• Sparse Signal Processing and Source Localization
• Dimensionality Reduction & Manifold Learning
• Latent Variable Analysis (PCA, ICA)
• Stochastic Sampling, Variational Approximation
• Decision Tree Learning
5. FIONA8: Adding GPUs to FIONAs
Supports Data Science Machine Learning
Multi-Tenant Containerized GPU JupyterHub
Running Kubernetes / CoreOS
Eight Nvidia GTX-1080 Ti GPUs
~$13K
32GB RAM, 3TB SSD, 40G & Dual 10G ports
Source: John Graham, Calit2
6. 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
7. 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
See talk by:
Hurtado Anampa