Energy-Optimal Configurations for Single-Node HPC Applications

Silva, Vitor R. G.; Furtunato, Alex F. A.; Georgiou, Kyriakos; Valderrama, Carlos; Eder, Kerstin; Xavier‐de‐Souza, Samuel

doi:10.1109/hpcs48598.2019.9188110

Cited by 5 publications

(2 citation statements)

References 25 publications

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“…These approximations have been demonstrated to be very precise. In the work of Silva et al, the mean percentage error was calculated to be 0.75% [47].…”

Section: Power Modelmentioning

confidence: 99%

Analytical Energy Model Parametrized by Workload, Clock Frequency and Number of Active Cores for Share-Memory High-Performance Computing Applications

et al. 2022

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Energy consumption is crucial in high-performance computing (HPC), especially to enable the next exascale generation. Hence, modern systems implement various hardware and software features for power management. Nonetheless, due to numerous different implementations, we can always push the limits of software to achieve the most efficient use of our hardware. To be energy efficient, the software relies on dynamic voltage and frequency scaling (DVFS), as well as dynamic power management (DPM). Yet, none have privileged information on the hardware architecture and application behavior, which may lead to energy-inefficient software operation. This study proposes analytical modeling for architecture and application behavior that can be used to estimate energy-optimal software configurations and provide knowledgeable hints to improve DVFS and DPM techniques for single-node HPC applications. Additionally, model parameters, such as the level of parallelism and dynamic power, provide insights into how the modeled application consumes energy, which can be helpful for energy-efficient software development and operation. This novel analytical model takes the number of active cores, the operating frequencies, and the input size as inputs to provide energy consumption estimation. We present the modeling of 13 parallel applications employed to determine energy-optimal configurations for several different input sizes. The results show that up to 70% of energy could be saved in the best scenario compared to the default Linux choice and 14% on average. We also compare the proposed model with standard machine-learning modeling concerning training overhead and accuracy. The results show that our approach generates about 10 times less energy overhead for the same level of accuracy.

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“…These approximations have been demonstrated to be very precise. In the work of Silva et al, the mean percentage error was calculated to be 0.75% [47].…”

Section: Power Modelmentioning

confidence: 99%

Analytical Energy Model Parametrized by Workload, Clock Frequency and Number of Active Cores for Share-Memory High-Performance Computing Applications

et al. 2022

Self Cite

View full text Add to dashboard Cite

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“…the CPU [4]). A linear growth in the CPU frequency f causes the dynamic power dissipation to increase cubically, leading to an energy consumption as E dyn ∝ f 2 [5]. Therefore reducing the frequency leads to a dramatic reduction in energy consumption, which also holds true for modern processors with nanoscale features with non-negligible static power consumption [5].…”

Section: Introductionmentioning

confidence: 99%

Energy-Efficient Computation Offloading in Delay-Constrained Massive MIMO Enabled Edge Network Using Data Partitioning

Malik

2020

IEEE Trans. Wireless Commun.

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We study a wireless edge-computing system which allows multiple users to simultaneously offload computation-intensive tasks to multiple massive-MIMO access points, each with a collocated multiaccess edge computing (MEC) server. Massive-MIMO enables simultaneous uplink transmissions from all users, significantly shortening the data offloading time compared to sequential protocols, and makes the three phases of data offloading, computing, and downloading have comparable durations. Based on this three-phase structure, we formulate a novel problem to minimize a weighted sum of the energy consumption at both the users and the MEC server under a round-trip latency constraint, using a combination of data partitioning, transmit power control and CPU frequency scaling at both the user and server ends. We design a novel nested primal-dual algorithm using two different methods to solve this problem efficiently. Optimized solutions show that for larger requests, more data is offloaded to the MECs to reduce local computation time in order to meet the latency constraint, despite higher energy cost of wireless transmissions. Massive-MIMO channel estimation errors under pilot contamination also causes more data to be offloaded to the MECs. Compared to binary offloading, partial offloading with data partitioning is superior and leads to significant reduction in the overall energy consumption.

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