Power efficiency for variation-tolerant multicore processors
Proceedings of the 2006 international symposium on Low power electronics and design
Impact of process variations on multicore performance symmetry
Proceedings of the conference on Design, automation and test in Europe
Variation-Aware Application Scheduling and Power Management for Chip Multiprocessors
ISCA '08 Proceedings of the 35th Annual International Symposium on Computer Architecture
PowerPack: Energy Profiling and Analysis of High-Performance Systems and Applications
IEEE Transactions on Parallel and Distributed Systems
Auto-tuning full applications: A case study
International Journal of High Performance Computing Applications
Online Adaptive Code Generation and Tuning
IPDPS '11 Proceedings of the 2011 IEEE International Parallel & Distributed Processing Symposium
A ROSE-Based OpenMP 3.0 research compiler supporting multiple runtime libraries
IWOMP'10 Proceedings of the 6th international conference on Beyond Loop Level Parallelism in OpenMP: accelerators, Tasking and more
Proceedings of the 9th conference on Computing Frontiers
Auto-tuning for energy usage in scientific applications
Euro-Par'11 Proceedings of the 2011 international conference on Parallel Processing - Volume 2
Beyond DVFS: A First Look at Performance under a Hardware-Enforced Power Bound
IPDPSW '12 Proceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum
Modeling Power and Energy Usage of HPC Kernels
IPDPSW '12 Proceedings of the 2012 IEEE 26th International Parallel and Distributed Processing Symposium Workshops & PhD Forum
Energy Efficient Parallel Matrix-Matrix Multiplication for DVFS-enabled Clusters
ICPPW '12 Proceedings of the 2012 41st International Conference on Parallel Processing Workshops
Exploiting process variability in voltage/frequency control
IEEE Transactions on Very Large Scale Integration (VLSI) Systems
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Power, energy, and compute time are all important metrics that can act as either objectives or constraints in program or system optimization. Recent microprocessors include sensors (counters) for monitoring these metrics as well as on-chip system controllers that may use this information. Code optimization is relatively straightforward if the measurements are stable and repeatable over time on nominally identical hardware, if there is a lot of variance it becomes very difficult. This paper describes experiments that expose the variability of performance and energy usage on recent Intel processors for some parallel benchmarks using shared memory (OpenMP) and message passing (MPI) programming models. During the start up phase going from a quiescent to a "hot" steady state temperature differences of greater than 26°C were seen resulting in run-to-run energy differences as large as 10%. Even in steady state, run-to-run variability in execution time and energy usage were problematic. The patterns of variability found in execution time and energy consumption pose a challenge to simple strategies for running performance experiments as part of a tuning framework.