Computational Science – ICCS 2006

Volume 3991 of the series Lecture Notes in Computer Science pp 242-249

Characterizing the Performance and Energy Attributes of Scientific Simulations

  • Sayaka AkiokaAffiliated withThe Pennsylvania State University
  • , Konrad MalkowskiAffiliated withThe Pennsylvania State University
  • , Padma RaghavanAffiliated withThe Pennsylvania State University
  • , Mary Jane IrwinAffiliated withThe Pennsylvania State University
  • , Lois Curfman McInnesAffiliated withArgonne National Laboratory
  • , Boyana NorrisAffiliated withArgonne National Laboratory

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We characterize the performance and energy attributes of scientific applications based on nonlinear partial differential equations (PDEs). where the dominant cost is that of sparse linear system solution. We obtain performance and energy metrics using cycle-accurate emulations on a processor and memory system derived from the PowerPC RISC architecture with extensions to resemble the processor in the BlueGene/L. These results indicate that low-power modes of CPUs such as Dynamic Voltage Scaling (DVS) can indeed result in energy savings at the expense of performance degradation. We then consider the impact of certain memory subsystem optimizations to demonstrate that these optimizations in conjunction with DVS can provide faster execution time and lower energy consumption. For example, on the optimized architecture, if DVS is used to scale down the processor to 600MHz, execution times are faster by 45% with energy reductions of 75% compared to the original architecture at 1GHz. The insights gained from this study can help scientific applications better utilize the low-power modes of processors as well as guide the selection of hardware optimizations in future power-aware, high-performance computers.