Jeffrey Vetter

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Another dimension: Several Factors Motivate Heterogeneity � Sacrifice generality and compatibility to address specific algorithms � Computational power > Asymmetric, dynamic cores can provide performance advantages [1] > Use transistors differently � Tukwila – First 2 billion transistor chip > 80486 had ~1.2M transistors, ~50MHz, 1989 > Specialization can be free � Power > Combination of these features provide more ops per watt for your targeted application � Memory > Include different cache or memory organization tuned for data access patterns � Performance of general purpose processors de-accelerating ISSCC 2008. [1] M.D. Hill and M.R. Marty, “Amdahl's Law in the Multicore Era,” IEEE Computer, to appear, 2008. 20
ENABLING HETEROGENEOUS COMPUTING FOR THE OPEN SCIENCE COMMUNITY 19 November 2009 21
Page 1 and 2: Jeffrey Vetter Presented to SC09 NV
Page 3 and 4: Overview • Predictive scientific
Page 5 and 6: Leadership computing is advancing s
Page 7 and 8: Highly visible science output Physi
Page 9 and 10: Application Complexity � Today’
Page 11 and 12: HPC Landscape Today � 2.3 PF Peak
Page 13 and 14: THE ROAD AHEAD 19 November 2009 13
Page 15 and 16: … facilities and cost issue ??? 5
Page 17 and 18: 17 Power, cooling, and floorspace a
Page 19: Scaling dimension seems straightfor
Page 23 and 24: Large Scale Computational Science D
Page 25 and 26: Alternatives analysis concluded GPU
Page 27 and 28: GPU Rationale - Power Efficiency! F
Page 29 and 30: Keeneland Partners Georgia Institut
Page 31 and 32: ID system will use NVIDIA’s Fermi
Page 33 and 34: Computational Materials - Case Stud
Page 35 and 36: Biomolecular systems from NAMD Team
Page 37 and 38: Keeneland Software Environment •
Page 39 and 40: Workload Analysis: Examples Branch
Page 41 and 42: Scalable Heterogeneous Computing (S
Page 43: Thank You! Thanks to contributors,

Jeffrey Vetter

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