Energy, Memory, and Runtime Tradeoffs for Implementing Collective Communication Operations

doi:10.14529/jsfi140204

Cited by 15 publications

(1 citation statement)

References 51 publications

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“…The approaches that target the Message Passing Interface (MPI) applications mainly involve mitigation of workload imbalance between the process (slack) [4,16,29,41]. Other MPI-centric solutions address cases where the processor cores wait on the memory or network [5,22,26,43,48,49]. Concurrency throttling has been widely used by adapting the thread count in OpenMP programs that are memory-constrained to reduce power consumption [12,13,31,37].…”

Section: Related Workmentioning

confidence: 99%

Cuttlefish: Library for Achieving Energy Efficiency in Multicore Parallel Programs

Kumar¹,

Gupta²,

Kumar³

et al. 2021

Preprint

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A low-cap power budget is challenging for exascale computing. Dynamic Voltage and Frequency Scaling (DVFS) and Uncore Frequency Scaling (UFS) are the two widely used techniques for limiting the HPC application's energy footprint. However, existing approaches fail to provide a unified solution that can work with different types of parallel programming models and applications.This paper proposes Cuttlefish, a programming model oblivious C/C++ library for achieving energy efficiency in multicore parallel programs running over Intel processors. An online profiler periodically profiles modelspecific registers to discover a running application's memory access pattern. Using a combination of DVFS and UFS, Cuttlefish then dynamically adapts the processor's core and uncore frequencies, thereby improving its energy efficiency. The evaluation on a 20-core Intel Xeon processor using a set of widely used OpenMP benchmarks, consisting of several irregular-tasking and work-sharing pragmas, achieves geometric mean energy savings of 19.4% with a 3.6% slowdown. CCS Concepts: • Software and its engineering → Power management.

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