Shuaiwen "Leon" Song
Colloq: Speaker Institution:
Colloq: Date and Time:
Thu, 2012-12-06 10:00
Building 5100, Room 128 (JICS Auditorium)
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Massive parallelism combined with complex memory hierarchies and heterogeneity in high-performance computing (HPC) systems form a barrier to efficient application and architecture design. The performance achievements of the past must continue over the next decade to address the needs of scientific simulations. However, building an exascale system by 2022 that uses less than 20 megawatts will require significant innovations in power and performance efficiency. Prior to this work, the fundamental relationships between power and performance were not well understood. Our analytical modeling approach allows users to quantify the relationship between power and performance at scale by enabling study of the effects of machine and application dependent characteristics on system energy efficiency. Our model helps users isolate root causes of energy or performance inefficiencies and develop strategies for scaling systems to maintain or improve efficiency. I will also show how this methodology can be extended and applied to model power and performance in heterogeneous GPU-based architectures.
Colloq: Speaker Bio:
Shuaiwen "Leon" Song is a PhD candidate in the Computer Science department of Virginia Tech. His primary research interests fall broadly within the area of High Performance Computing (HPC) with a focus on power and performance analysis and modeling for large scale homogeneous and heterogeneous parallel architectures and runtime systems. He is a recipient of the 2011 Paul E. Torgersen Award for Graduate Student Research Excellence and in 2011 was an Institute for Scientific Computing Research (ISCR) Scholar at Lawrence Livermore National Laboratory. His work has been published in conferences and journals including IPDPS, IEEE Cluster, PACT, MASCOTS, IEEE TPDS, and IJHPCA.