Power and energy profiling of scientific applications on distributed systems

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 high-performance 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 nodes), and by system scale (2, 4, 8, 16, and 32 nodes). Our results indicate power profiles are often regular corresponding to application characteristics and for fixed problem size increasing the number of nodes always increases energy consumption but does not always improve performance. This finding suggests smart schedulers could be used to optimize for energy while maintaining performance.