uppmax and uppnex: enabling high performance bioinformatics ola spjuth, uppmax...
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High-performance bioinformatics
• Trivial/embarrassingly parallelizable– Mass of individual tasks (or divide up problems),
run in parallel– E.g. analyze several sequences
• Non-trivial parallelism– Single task on many processors (data partitioning)– Example: Molecular dynamics
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Resources for high-performance computing (HPC)
• Supercomputers– “a computer at the frontline of current processing
capacity, particularly speed of calculation”
• Clusters– Processors in close proximity
• GRID computing– Distributed systems, (joined clusters)
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UPPMAX
• Uppsala university’s resource for high performance computing (HPC) and related know-how– Computational clusters
• 6000 cores
– Storage• 1.4 PB parallel storage
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• A project at UPPMAX• 13,152 MSEK from KAW/SNIC (2008-12-30)
• ~1 M cpuh/month on a shared cluster (kalkyl)• ~1 PB cluster-attached parallel storage (bubo)• Long term storage on SweStore (>1 PB)• SMP machine, 64 core, 2TB RAM (halvan)
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The cluster kalkyl
• 348 nodes with 8 cores each– 324 nodes with 24 GB– 16 nodes with 48 GB– 16 nodes with 72 GB– Total: 2784 cores
• SLURM queuing system
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UPPNEX data flow
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Knowledge Base / Community website
www.uppnex.uu.se
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UPPNEX Application Experts
• Assist with NGS Analysis
• Available via
mailing-list or by
direct contact
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Project growth
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UPPNEX storage usage
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Used CPU core h / month
1 week maintenance stop for move to new computer hall
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A typical day at UPPMAX
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UPPNEX software used
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Conclusions:
Community needs (storage)
• Access to high-availability storage
• Access to long term storage
• Sustainable file infrastructure
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• Support new types of HPC users and usage
• Keep up with the bioinformatics software flood
• Managing data growth (previously only computations)
Conclusions:
UPPNEX main challenges