Grid is Dead ? Nimrod on the Cloud
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This document discusses using hybrid cloud and grid infrastructure for high-throughput computational science. It provides an overview of the Nimrod toolkit, which supports parameter sweeps, optimization, and workflows across distributed resources. A recent experiment used Nimrod to complete jobs faster on grid resources than Amazon EC2. It also outlines a potential strawman project called GEMAP to enable grid-enabled microscopy across the Pacific using remote microscopes, compute clusters, storage, and visualization portals.
Grid is Dead ? Nimrod on the Cloud
- 1. Mixing the Grid and Clouds: High-throughput Science using Nimrod or Is the Grid Dead? David Abramson Monash e-Science and Grid Engineering Lab (MESSAGE Lab) Faculty of Information Technology Science Director: Monash e-Research Centre ARC Professorial Fellow 1
- 2. Instructions .. • To highlight their science and its success' so far and how their work utilizes advanced cyberinfrastructure • Identify potential ITC barriers to ongoing success • Paint a picture of the future for their research in the next 3 to 5 years and what demands it may create for cyberinfrastrcture • Identify concrete experiments or demonstrations which will utilize and/or stress the infrastructure within 12-24 months
- 3. What have we been doing over the Pacific?
- 4. PRAGMA A Practical Collaborative Framework IOIT-VN Strengthen Existing and Establish New Collaborations Work with Science Teams to Advance Grid Technologies and Improve the Underlying Infrastructure http://www.pragma-grid.net In the Pacific Rim and Globally
- 5. PRIME @ Monash • Engaged in PRIME since 2004 • Projects range from bio-engineering, theoretical chemistry to computer science • Has underpinned long lasting academic collaborations – Publications – Presentations at conferences • Undergraduate students without research experience!
- 6. MURPA Seminars I’ve participated in numerous video conferences to date but nothing like this. The quality was so high that the experience was almost as if we were all in the same room. The massively increased bandwidth was transformational. Quantity begat quality. Alan Finkel, Chancellor, Monash Univ
- 8. Clouds and Grids and …. A bit about hype …
- 9. Gartner Hype Cycle 2000
- 10. Gartner Hype Cycle 2005
- 11. Gartner Hype Cycle 2007
- 12. Gartner Hype Cycle 2008
- 13. Gartner Hype Cycle 2009
- 15. Introduction • University research groups have used varying sources of infrastructure to perform computational science – Rarely been provided on a strict commercial basis. – Access controlled by the users – High end facilities peer re-viewed grant, usually made in terms of CPU hours • Cloud computing is a major shift in – provisioning – delivery of computing infrastructure and services. • Shift from – distributed, unmanaged resources to – scalable centralised services managed in professional data centres, with rapid elasticity of resource and service provisioning to users. – Commercial cloud services
- 16. Policy and technical challenges • Free resources will not disappear! • Commercial clouds could provide an overflow capability • Potential – perform base-load computations on “free” resources, – pay-as-you-go ser-vices to meet user demand. • To date, very few tools can support both styles of resource provisioning.
- 17. Grid Enabled Elasticity • Resources maintained by home organisation • Distinct administrative domains • Unified compute, instruments and data • Middleware layer • Never solved deployment – See Goscinski, W. and Abramson, D. “An Infrastructure for the Deployment of e-Science Applications”, in “High Performance Computing (HPC) and Grids in Action”, Volume 16 Advances in Parallel Computing, Editor: L. Grandinetti, March 2008, approx. 540 pp., hardcover, ISBN: 978-1-58603- 839-7. • Standards exploded this vision! – Plus a whole load of useless computer scientists! 17
- 18. Cloud Enabled Elasticity • Home resource expands elastically • Cloud providers “join” home resource • Virtual machines deployed on demand • Scalable infrastructure – Compute – Doesn’t address instruments and data • Do we still have a whole load of useless computer scientists? 18
- 19. Hybrid solutions • Grid (Wide Area) – Wide area computing – Instruments, data – Security – File transport • Cloud (Local Area) – Elastic resources – Virtual machines (deployment) • Underpinned by a computational economy! – Abramson, D., Giddy, J. and Kotler, L. “High Performance Parametric Modeling with Nimrod/G: Killer Application for the Global Grid?”, International Parallel and Distributed Processing Symposium (IPDPS), pp 520- 528, Cancun, Mexico, May 2000
- 20. High throughput science with Nimrod
- 21. Nimrod supporting “real” science • A full parameter sweep is the cross product of all the parameters (Nimrod/G) • An optimization run minimizes some output metric and returns Nimrod/O Results Results Results parameter combinations that do this (Nimrod/O) • Design of experiments limits number of combinations (Nimrod/E) • Workflows (Nimrod/K)
- 22. Antenna Design Aerofoil Design Aerofoil Design Drug Docking 22
- 23. Nimrod/K Workflows • Nimrod/K integrates Kepler with – Massivly parallel execution mechanism – Special purpose function of Nimrod/G/O/E – General purpose workflows from Kepler – Flexible IO model: Streams to files Authentication GUI …Kepler GUI Extensions… Vergil Documentation Kepler Smart SMS Type Re-run / Provenance Object System Failure Actor&Data Ext Framework Manager SEARCH Recovery Kepler Core Ptolemy Extensions
- 24. Parameter Sweep Actors • Using a MATLAB actor provided by Kepler • Local spawn • Multiple thread ran concurrently on a computer with 8 cores (2 x quads) • Workflow execution was just under 8 times faster • Remote Spawn • 100’s of remote processes
- 25. Nimrod/EK Actors • Actors for generating and analyzing designs • Leverage concurrent infrastructure
- 26. Nimrod/OK Workflows • Nimrod/K supports parallel execution • General template for search – Built from key components • Can mix and match optimization algorithms 26
- 27. A recent experiment Resource #jobs completed Total job time μ / σ Job runtime (h:m:s) (mins) East 818 1245:37:23 91/5.7 EC2 613 683:34:05 67/14.2
- 28. A Grid exemplar
- 29. Grid Architecture for Microscopy Microscopes Clusters Storage Grid Middleware Visualization
- 30. ARC Linkage Grant with Leica Remote control of Leica Microscope from Kepler Nov 2008 First OptiPortal/Kepler link Feb 2009 First remote control of Leica Microscope in Germany to Opti-portal in Australia using Kepler March 2009.
- 31. Bird’s eye capture and display
- 32. Zooming into area of interest
- 33. Image cleanup and rendering
- 34. Image cleanup and rendering Parallelism for free!
- 35. Strawman Project: Grid Enabled Microscopy Across the Pacific (GEMAP)? • Remote microscopes • Cloud time – Currently Leica – Which cloud? • Mix of Compute Clusters – Who pays? – University Clusters (Monash) • Network – NCRIS (APAC grid) – Reservation? – Rocks Virtual Clusters (UCSD) – Who pays? – Commercial services • Project funding (Amazon) – Who pays? • Distributed display devices – OptIPortals
- 36. • Faculty Members • Funding & Support – Jeff Tan – Axceleon – Maria Indrawan – Australian Partnership for Advanced • Research Fellows Computing (APAC) – Blair Bethwaite – Australian Research Council – Slavisa Garic – Cray Inc – Donny Kurniawan – Tom Peachy – CRC for Enterprise Distributed Systems (DSTC) • Admin – GrangeNet (DCITA) – Rob Gray – Hewlett Packard • Current PhD Students – IBM – Shahaan Ayyub – Philip Chan – Microsoft – Colin Enticott – Sun Microsystems – ABM Russell – US Department of Energy – Steve Quinette – Ngoc Dinh (Minh) • Completed PhD Students – Greg Watson – Rajkumar Buyya – Andrew Lewis – Nam Tran – Wojtek Goscinski – Aaron Searle – Tim Ho – Donny Kurniawan 37
- 37. Questions? • More information: http://messagelab.monash.edu.au