SC Conference - Activity Details

Alexander Szalay  (Johns Hopkins University)

Andreas Terzis  (Johns Hopkins University)

Alainna White  (Johns Hopkins University)

Howie Huang  (George Washington University)

Jan Vandenberg  (Johns Hopkins University)

Tamas Budavari  (Johns Hopkins University)

Sam Carliles  (Johns Hopkins University)

Alireza Mortazavi  (Johns Hopkins University)

Gordon Bell  (Microsoft Research)

Jose Blakeley  (Microsoft Corporation)

David Luebke  (NVIDIA)

Michael Schuette  (OCZ Technology)

Ani Thakar  (Johns Hopkins University)

Tuesday,  10:30AM - 10:52AM

Room PB251

Enterprise and scientific data sets double every year, forcing similar growths in storage size and power consumption. As a consequence, current system architectures used to build data warehouses are hitting a power consumption wall. We propose a novel alternative architecture comprising a large number of so-called “Amdahl blades” that combine energy-efficient CPUs and GPUs with solid state disks to increase sequential I/O throughput by an order of magnitude while keeping power consumption constant. Offloading throughput-intensive analysis to integrated GPUs in the chipset increases the relative performance per Watt. We also show that while keeping the total cost of ownership constant, Amdahl blades offer five times the throughput of a state-of-the-art computing cluster for data-intensive applications. Finally, using the scaling laws origi-nally postulated by Amdahl, we show that systems for data-intensive computing must maintain a balance between low power consumption and per-server throughput to opti-mize performance per Watt.