UCLA’s CI Vision

1. UCLA’s CI Vision Institute for Digital Research and Education (IDRE) Research CI Institute for Informatics (I2) Common Collaboration And Learning Environment (CCLE) CyberLearning DataNet

2. The Institute for Digital Research and Education IDRE HPC IDRE HASIS IDRE STATS

3. IDRE Mission Statement(s) • To accelerate discovery, the design and engineering of molecules, materials, and catalysts, and the understanding of complex processes, through simulations and through knowledge extraction of large digital data generated in simulations, experiments, imaging, DNA sequencing, and historical records. • To support, advance and guide a campus wide program to position UCLA as a world leader in research and education in computational thinking using high performance computation, data visualization, and data analysis of large data sets and databases.

4. An overwhelming new wave of technologyand complexity

5. What is IDRE? • An ACTIVE community of faculty, researchers, and students with: • Common interests and overlapping research and education goals • A growing federation of Centers of Excellencee.g., LONI, FSC, IPAM, • A growing repository of knowledge, resources, and experiences • Components • Members • Mission • Governance and organization • Resources • Events/Activities • Research • Education • THE BENEFITS OF MEMBERSHIP IS THAT THERE IS NO “MEMBERSHIP” • ONLY THE ADDED VALUE OF ACHIEVING YOUR RESEARCH AND EDUCATION GOALS.

6. What is IDRE? • Governance • Director • Executive Committee • Sets policy and priorities • Ad Hoc Committees • Oversight: VCR and VCIT • Organization • IDRE-HPC • IDRE-HASIS • IDRE-STAT

7. What is IDRE? • Events/Activities • Annual retreat sets themes for the year • Speakers/Workshops • Birds of a feather lunches • Training Courses • Initiatives • Projects • Focus ProjectsAnnual call • Funded Projects • Fee for services • Pilot Projects • Directors level • General Projects • Reviewed by resource committee • The areas of active research continually change as faculty members and IDRE research scholars respond to the landscape

8. What is IDRE? • Resources • MOU has been reached between VCIT and Director of IDRE • 15-20 FTE’s within ATS are now IDRE Research Staff who report up to the director of IDRE. These include ~10 PhDs. • Data Center Space, system admin resources, and funds to build out to 1700 node capacity • Hoffman 2 shared cluster • Technology sandbox • Computational lab space: Currently just walls. • Visualization portal • Additional resources to be acquired through grants

9. What is IDRE? • Domain Science Research • Advancing the frontier of high-energy physics • Achieving fusion energy • Advancing an understanding of the brain • Designing new materials: for nanoscale, energy efficiency, extreme conditions • Designing new protein catalysts • Predicting the earth’s weather and climate • Understanding space weather • Determining the source of the most energetic particles in the universe • Determining the most fundamental forces of nature • Bioinformatics • Proteomics • Medical imaging • Preventing heart attacks • Population genetics and evolution • Organizational research and social networks • Other Domain Research • Digital cultural mapping • Historic modeling • Geographic Information Systems

10. What is IDRE? • Applied math, computer, and information science research • Domain Specific Computing: Multi-core, GPUs, FPGAs • Parallel Computing • Code optimization and enhancement • Algorithms • Particle methods (PIC, meshless) • Molecular dynamics • Quantum • Fluid dynamics • Electromagnetics • Automating communication patterns • Software engineering: Developing and maintaining complex code • Imaging • Scientific Workflows • Grid and Cloud Computing • Statistical Modeling and Computing

11. What is IDRE? • Education • IDRE training courses • Developing a post-doctoral mentorship program • Young researchers sub group • Using research codes in education • Developing an digital humanities curriculum • Cataloguing of computational science courses

12. The Shared Cluster Implementation at UCLA Research Virtual Shared Cluster Campus General Purpose Cluster (160 Cores) Application Cluster (80 Cores) Base Cluster (96 Cores) Contributed Clusters (space for ~ 12,000 Cores) Parallel Serial Open to all campus researchers for commercial parallel apps & parallel jobs Open to all campus researchers for commercial serial apps & serial jobs Researchers have guaranteed access to equivalent number of their contributed nodes for serial or parallel jobs with access to additional pooled serial or parallel cycles.

13. UCLA Shared Cluster build out through Faculty purchased nodes and storage • Current – 450 nodes, 2,700 cores, 19 Tflops, 200TB of storage • 20 different research groups – Physics, Neuroimaging, Economics, Astrophysics, Biostatistics, Chemistry, Engineering, Physiological Science. • In the process of migrating 18 existing stand-alone clusters into shared • Equivalent Flop to storage match • Roughly 9Tflops of harvested cycles available to researchers. • Already been as a pipeline for researchers to get access to NSF and DOE Facilities

14. UC Shared Cluster Pilot • 10 campuses, 5 medical centers, SDSC, LBL • High potential for regional and system capability and capacity • UC Shared Cluster Pilot • How to work as a UC system – non-trivial • Build the experience base on a system shared resources • Build the experience based with a shared regional data centers • Build the business model • Build the trust of the faculty researchers

15. Why A Shared Cluster Model:The Operational Perspective • More efficient use of scarce people resources • Standalone clusters have separate “everything”. Storage, head/interactive nodes, network, user space, configuration • Higher overall performance than a standalone Cluster • Harvesting unused compute cycles – in some cases only 30% of cycles are used on standalone systems, • Allocation of unused cycles for important campus initiatives • More efficient data center operations • Better security • Better staff utilization • 1.4 FTE – 7 x 32 node clusters vs. • .5 FTE 1 x 200 node cluster • Better machine utilization • Gain 25 - 50% in unused cycles on stand alone clusters • Better machine performance • Gain 30%+ of cycles lost to I/O wait on GigE vs. Infiniband • Better code performance • Gain factor of 2-20 in utilization with optimized code • Better data center efficiency • Data centers 3 – 4 x more efficient than ad hoc space • Regional data centers more efficient than distributed

16. Many System wide IT Infrastructure Projects Are Interrelated: UCLA is in a leadership position New equipment can be located in shared regional data centers to relieve campus data center space constraints and achieve efficiencies. Systemwide Shared Research Computing Services Program Shared Regional Data Centers Consolidated Campus Data Centers Shared Research Computing Services Pilot Reciprocal Disaster Recovery Sites Mainframe Sharing Once shared space becomes available in regional data centers, implementation of other initiatives that call for sharing infrastructure or services is greatly facilitated.