BSC-Microsoft Research Centre Overview Osman Unsal
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BSC-Microsoft Research Centre Overview Osman Unsal Moscow 11.June.2009 Outline • Barcelona Supercomputing Center • BSC – Microsoft Research Centre Barcelona Supercomputing Center • Mission – Investigate, develop and manage technology to facilitate the advancement of science. • Objectives – Operate national supercomputing facility – R&D in Supercomputing and Computer Architecture. – Collaborate in R&D e-Science • Consortium – the Spanish Government (MEC) – the Catalonian Government (DURSI) – the Technical University of Catalonia (UPC) Location MareNostrum ● MareNostrum 2006 ● 10240 PowerPC 970 cores ● 2560 JS21 2.3 GHz ● 20 TB of Memory ● 8 GB per node ● 380 TB Storage Capacity ● 3 networks ● Myrinet ● Gigabit ● 10/100 Ethernet ● Operating System ● Linux 2.6 (SuSE) Spanish Supercomputing Network Magerit Altamira CaesarAugusta LaPalma Picasso Tirant Access Committee Resource utilization Solicitudes recibidas y concedidas 140 120 • Resource distribution 100 – 20% internal use 80 60 – 80% assigned via Access 40 Committee 20 0 • Unique Access Committee 2006 P2 2006 P3 2006 P4 2007 P1 2007 P2 2007 P3 2008 P1 Total solicitadas, concedidas (clase A) y usadas – 44 Spanish researchers 60000 Solicitadas 50000 Concedidas • Core Team Usadas • 4 panels 40000 30000 – Astrophysics, Space, and Khoras Earth Sciences 20000 – Biomedicine and Life Sciences 10000 – Physics and Engineering 0 2006 P2 2006 P3 2006 P4 2007 P1 2007 P2 2007 P3 2008 P1 – Chemistry and New Materials Uso por áreas de Ciencia – New applications every four months 2500 – Committee renewed every 2 years 2000 – Designated by the Spanish Ministry 1500 QCM FI BCV 1000 – Technical assistance by BSC Khoras AECT staff 500 0 01/02/08 08/02/08 15/02/08 22/02/08 29/02/08 07/03/08 14/03/08 21/03/08 28/03/08 04/04/08 11/04/08 18/04/08 Industry using MareNostrum ● Average utilization of 1000 processors ● Using dedicated network ● Oil prospection using 2000 processors, each execution lasts for 5 weeks • Provide HPC computing resourceswith the following specifications: – 1 Million CPU-hours – Disk space: 2 Terabytes capacity – Disaster Back-up for 20 Terabytes capacity Staff Evolution BSC-CNS has 195 members at October of 2007 and hailed from 21 different countries (Argentina, Belgium, Brazil, Bulgaria, Colombia, China, Cuba, France, Germany, India, Ireland, Italy, Lebanon, Mexico, Poland, Russia, Serbia, Turkey, the United Kingdom, the United States and Spain). BSC Departments • Computer Science – Computer Architecture • Life Sciences • Earth Sciences • Computer Applications Towards Petaflop computing systems Analysis and prediction of: • Applications • Parallel execution environments • OS activity The Grid Distributed-memory programming model - Scalable MPI Shared-memory Future Petaflop programming models systems - Mercurium OpenMP compiler - NthLib - NanosDSM - Transactional memory Small DMM Large cluster systems On-board SMP cc-NUMA Hardware and software technologies for: 10s, 100s, 1000s, … cores, • Grand Challenge applications multithreaded chip • Killer applications (Google, Amazon, eBay, …) CIRI R&D Philosophy • Technology Web Metacomputing Applications •ST-ORM •Steel stamping •Structural analysis MPI •MGPOM •UTE2paraver •MPIRE •OMPITrace Computer OpenMP Performance •OMPTrace Dimemas Architecture •activity Visualization •Collective, mapping •hw counters •Scheduling •Nested Parallelism Paraver •Precedences •Indirect access System scheduling RunTime Scheduling •Self analysis •Performance Driven Proc. Alloc. •Dynamic load balancing •Process and memory control •Page migration Amber Marenostrum 5x CPU? MN prediction 5x CPU Ideal BW? MN prediction Ideal BW BSC Departments • Computer Science – Computer Architecture • Life Sciences • Earth Sciences • Computer Applications Computer Architecture is ..... • About the interface between what technology can provide and what the people/market demand • At that interface we have our design point: – Reliability – Availability – Cost – Power – Performance Yale Patt, University of Austin at Texas Superscalar Processor file Fetch select Bypass Decode Rename Commit Register Window Wakeup+ Instruction Data Cache Register Write Register Fetch of multiple instructions every cycle. Rename of registers to eliminate added dependencies. Instructions wait for source operands and for functional units. Out- of -order execution, but in order graduation. Predict branches and speculative execution J.E. Smith and S.Vajapeyam.¨Trace Processors…¨ IEEE Computer.Sept. 1997. pp68-74. Superscalar Processors • Out of order (IPC <= 3) F D R E E E E W F D R E E E W 1 F D R E E W T = N * * t IPC c F D R E E E E W F D R E E E W F D R E E W Time F D R E E E W F D R E E E E W F D R E E E E W Future High-Performance Processors • Future Applications: Integer, FP, Commercial and Multimedia • Future Technology: Moore´s Law • No New Paradigm for Computing • Pipeline Design and Optimization: – Memory Hierarchy: Cache, Prefetching – Branch Predictors – Wire Delays – Power – Design – Verification • ILP improvements Future Architectures • Superscalar • VLIW (EPIC) • Multithreaded • Multiprocessors on a chip Which is the Architecture that will dominate the future?? Advanced ILP Techniques for Superscalar Processors • Value Prediction • Reuse • Helper Threads • Trace Processor • Control/Data Speculation • Kilo-instruction Processors BSC Departments • Computer Science – Computer Architecture • Life Sciences • Earth Sciences • Computer Applications Life Sciences Department Our goal is to understand Biology from a biomedical, molecular and evolutionary point of view Covering from the atom to the earth Group Research Line Victor Atomic (and electronic) modeling of Guallar protein biochemistry and biophysics Modesto Molecular dynamics: understanding the Orozco motion of proteins Juan Identification of the structural bases of Fernández protein-protein interaction Patrick Protein-protein interaction networks Aloy Josep Ll. Web services Gelpí Analysis of genomes and networks to model David diseases, systems and evolution of organisms Torrents Analysis of protein and function diversity on earth Computational genomics group (collaboration with EMBL, Heidelberg) Identification and classification of new and known proteins from Molecular known genomes and metagenomic data Biology Providing new functional associations and hypothesis for basic research IMPACT + FIELDS FP7 European project BioMedicine Identification of new microorganisms, pathways, proteins and molecules. New therapeutic molecules (antibiotics, protein targets) Evolution Ecology Understanding the mechanisms Identification of new pathways and organisms of evolution of proteins and organisms for new possibilities in biodegradation. Understanding the structural basis of the interaction between proteins and with therapeutic drugs - Drug design Biomedical - Identification of therapeutic protein targets and molecular - Finding functional associations between proteins goals: - Function prediction for new proteins - Structure prediction of proteins Life Sciences Department • Library of protein MD simulations • 1400 at the moment • >900 CPU years. • 10 Tb of data TimeSlice The natura key is the set of these fields CP,CE1 analysisId - version: can allow to have more CP initialSnapshot ENUM(„CATEGORY‟, the one simulation with the same CP lastSnapshot 'FILE','FLOAT', force field for the same protein Analysis ‟STRING‟) - pdbCode reason - programVersion (for example: CP name The web will load the AMBER8) CP,CE3 parentName title of the analysis from and all possible combinations of this field SimulationAnalysis ForceFields initialSnapshot = -1 type The web will show the && CP analysisId title analysis help from this lastSnapshot = -1 description field means ALL Simulation CE1 simulationId CP simulationId CE2 name CE2 parentName AnalysisFragment Fragment floatValue version CP,CE2 analysisId CP fragmentId stringValue CE1 pdbCode UNIQUE restraint CP,CE1 fragmentId programVersion (these fields are the pdbCode date real primary key) chain status statusComment Status values: simulationTime - -1: no information about status ResidueFragment saltConcentration - 0: will be calculated some day boxType - 1: setup finished, ready to be CP,CE1 fragmentId boxX calculated boxY - 2: @ Marenostrum initialResidue boxZ - 3: simulation finished lastResidue @ means volume - 4: analysis in execution NO CHAIN waters - 5: analysis finished: ok totalAtoms - 6: analysis finished: no ok temperature entries integrationTime CP idCode samplingTime -999, -999 Status comment: to be defined, proteinCharge means ALL but probably it will be a text totalCharge field where you will be able to shake put any interesting comment. cutoff GeneOntologyFragment PBC SimulationForceField CP,CE1 fragmentId ewald EnzymeCodeFragment CP,CE1 forceFieldId geneOntologyId CP,CE2 simulationId CP,CE1 fragmentId evidence desctiption enzymeCode SetupOption ForceField ScopFragment CP,CE1 simulationId ActiveSiteFragment CP,CE2 idMon CP forceFieldId CP,CE1 fragmentId CP number CP,CE1 fragmentId CP type name scopId domain description name Possible values CathFragment for type: PfamFragment The web - A (atom) will show Monomer CP,CE1 fragmentId CP,CE1 fragmentId - H (heteroatom) the analysis - W (water) CP idMon help from cathId accession - R (residue) this field domain name sourceData name numberAtoms OtherFragment synonym ENUM('PDB','MoDEL', formula CP,CE1 fragmentId 'OTHER') molecularWeight Version: 200501091241 charge reason Rueda et al., PNAS 2007 BSC: Life Sciences R&D THE ENCODE THE EARTH PROJECT PROTEOME