Power consumption is a troublesome design constraint for emergent systems such as IBM’s BlueGene /L. If current trends continue, future petaflop systems will require 100 megawatts of power to maintain high-performance. To address this problem the power and energy characteristics of highperformance systems must be characterized. To date, power-performance profiles for distributed systems have been limited to interactive commercial workloads. However, scientific workloads are typically non-interactive (batched) processes riddled with interprocess dependences and communication. We present a framework for direct, automatic profiling of power consumption for non-interactive, parallel scientific applications on high-performance distributed systems. Though our approach is general, we use our framework to study the power-performance efficiency of the NAS parallel benchmarks on a 32-node Beowulf cluster. We provide profiles by component (CPU, memory, disk, and NIC), by node (for each of 32 n...
Xizhou Feng, Rong Ge, Kirk W. Cameron