Using Dynamic Duty Cycle Modulation to Improve Energy Efficiency in High Performance Computing

Bhalachandra, Sridutt; Porterfield, Allan; Prins, Jan F.

doi:10.1109/ipdpsw.2015.144

Cited by 24 publications

(8 citation statements)

References 19 publications

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“…Zhang et al (2009) use duty cycling to efficiently manage on-chip shared resources. Bhalachandra et al (2015) employed this technique in MPI applications, while Cicotti et al (2014) designed a library to save energy with duty cycling and DVFS. Wang et al (2015) used DCT and duty cycling on OpenMP parallel loops.…”

Section: Related Workmentioning

confidence: 99%

Performance and energy analysis of OpenMP runtime systems with dense linear algebra algorithms

Lima

Raïs

Lefèvre

et al. 2018

The International Journal of High Performance Computing Applica

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In this paper, we analyse performance and energy consumption of five OpenMP runtime systems over a NUMA platform. We also selected three CPU level optimizations, or techniques, to evaluate their impact on the runtime systems: processors features Turbo Boost and C-States, and CPU DVFS through Linux CPUFreq governors. We present an experimental study to characterize OpenMP runtime systems on the three main kernels in dense linear algebra algorithms (Cholesky, LU and QR) in terms of performance and energy consumption. Our experimental results suggest that OpenMP runtime systems can be considered as a new energy leverage, and Turbo Boost, as well as C-States, impacted significantly performance and energy. CPUFreq governors had more impact with Turbo Boost disabled, since both optimizations reduced performance due to CPU thermal limits. A LU factorization with concurrent write extension from libKOMP achieved up to 63% of performance gain and 29% of energy decrease.

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Section: Related Workmentioning

confidence: 99%

Performance and energy analysis of OpenMP runtime systems with dense linear algebra algorithms

Lima

Raïs

Lefèvre

et al. 2018

The International Journal of High Performance Computing Applica

View full text Add to dashboard Cite

show abstract

“…Some approaches use static analysis and may not always be flexible to adapt to dynamic changes. Our previous works [5,6] attempt to go beyond chip-wide DVFS and propose dynamic policies that use core-specific power controls. However, these works target scenarios with computational workload imbalances.…”

Section: Related Workmentioning

confidence: 99%

Improving Energy Efficiency in Memory-constrained Applications Using Core-specific Power Control

Bhalachandra

Porterfield

Olivier

et al. 2017

Proceedings of the 5th International Workshop on Energy Efficient Supercomputing

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Power is increasingly the limiting factor in High Performance Computing (HPC) at Exascale and will continue to influence future advancements in supercomputing. Recent processors equipped with on-board hardware counters allow real time monitoring of operating conditions such as energy and temperature, in addition to performance measures such as instructions retired and memory accesses. An experimental memory study presented on modern CPU architectures, Intel Sandybridge and Haswell, identifies a metric, TORo core, that detects bandwidth saturation and increased latency. TORo core is used to construct a dynamic policy applied at coarse and fine-grained levels to modulate per-core power controls on Haswell machines. The coarse and fine-grained application of dynamic policy shows best energy savings of 32.1% and 19.5% with a 2% slowdown in both cases. On average for six MPI applications, the fine-grained dynamic policy speeds execution by 1% while the coarse-grained application results in a 3% slowdown. Energy savings through frequency reduction not only provide cost advantages, they also reduce resource contention and create additional thermal headroom for non-throttled cores improving performance.

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“…A simple adaptive runtime model constructed in our previous work [3] is applied here to real HPC applications. The model automates the process of picking the clock duty cycle for the next application region by comparing the computing and waiting times of each thread.…”

Section: Energy Modelmentioning

confidence: 99%

Application Runtime Variability and Power Optimization for Exascale Computers

Porterfield

Fowler

Bhalachandra

et al. 2015

Proceedings of the 5th International Workshop on Runtime and Operating Systems for Supercomputers

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Exascale computing will be as much about solving problems with the least power/energy as about solving them quickly. In the future, systems are likely to be over-provisioned with processors and will rely on hardware-enforced power bounds to allow operation within power budget/thermal design limits.Variability in High Performance Computing (HPC) makes scheduling and application optimization difficult. For three HPC applications -ADCIRC, WRF and LQCD, we show the effects of heterogeneity on run-to-run execution consistency (with and without a power limit applied). A 4% hardware run-to-run variation is seen in case of a perfectly balanced compute-bound application, while up to 6% variation was observed for an application with minor load imbalances.A simple model based on Dynamic Duty Cycle Modulation (DDCM) is introduced. The model was implemented within the MPI profiling interface for collective operations and used in conjunction with the three applications without source code changes. Energy savings of over 10% are obtained for ADCIRC with only a 1-3% slowdown. With a power limit in place, the energy saving is partially translated to performance improvement. With a power limit of 50W, one version of the model executes 3% faster while saving 6% in energy, and a second version executes 1% faster while saving over 10% energy.

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Using Dynamic Duty Cycle Modulation to Improve Energy Efficiency in High Performance Computing

Cited by 24 publications

References 19 publications

Performance and energy analysis of OpenMP runtime systems with dense linear algebra algorithms

Performance and energy analysis of OpenMP runtime systems with dense linear algebra algorithms

Improving Energy Efficiency in Memory-constrained Applications Using Core-specific Power Control

Application Runtime Variability and Power Optimization for Exascale Computers

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