The ICARUS White Paper: A Scalable, Energy-Efficient, Solar-Powered HPC Center Based on Low Power GPUs

Geveler, Markus; Ribbrock, Dirk; Donner, D.; Ruelmann, Hannes; Höppke, Christoph; Schneider, D. P.; Tomaschewski, Daniel; Turek, Stefan

doi:10.1007/978-3-319-58943-5_59

Cited by 7 publications

(4 citation statements)

References 12 publications

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“…Various attempts to take advantage of mobile technology for increasing energy efficiency of HPC systems have also been taken in the recent past. The closest to our work are the EU Mont-Blanc project [16,17] and the COSA project [18,19], but several other examples can be found in the literature [20][21][22][23].…”

Section: Introduction and Related Workmentioning

confidence: 93%

Performance and Power Analysis of HPC Workloads on Heterogeneous Multi-Node Clusters

Mantovani

Calore

2018

JLPEA

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Performance analysis tools allow application developers to identify and characterize the inefficiencies that cause performance degradation in their codes, allowing for application optimizations. Due to the increasing interest in the High Performance Computing (HPC) community towards energy-efficiency issues, it is of paramount importance to be able to correlate performance and power figures within the same profiling and analysis tools. For this reason, we present a performance and energy-efficiency study aimed at demonstrating how a single tool can be used to collect most of the relevant metrics. In particular, we show how the same analysis techniques can be applicable on different architectures, analyzing the same HPC application on a high-end and a low-power cluster. The former cluster embeds Intel Haswell CPUs and NVIDIA K80 GPUs, while the latter is made up of NVIDIA Jetson TX1 boards, each hosting an Arm Cortex-A57 CPU and an NVIDIA Tegra X1 Maxwell GPU.

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Section: Introduction and Related Workmentioning

confidence: 93%

Performance and Power Analysis of HPC Workloads on Heterogeneous Multi-Node Clusters

Mantovani

Calore

2018

JLPEA

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“…Energy-Efficient Computing. A large body of early studies focused on reducing power consumption of a single server by applying the dynamic voltage and frequency scaling technique (i.e., DVFS) [16], low-power chipsets [17], and advanced cooling techniques [18]. Emerging energy-management schemes aim to optimize energy efficiency of servers equipped with multi-core processors [19], GPUs [20], and smart memory cubes [21].…”

Section: Background and Related Workmentioning

confidence: 99%

Exploiting Renewable Energy and UPS Systems to Reduce Power Consumption in Data Centers

et al. 2022

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To develop environmental friendly and energy-efficient data centers, it is prudent to leverage on-site renewable sources like solar and wind. Data centers deploy distributed UPS systems to improve efficiency, scalability, and reliability of UPS systems, thereby handling the intermittent nature of renewable energy. We propose a renewableenergy manager called REDUX to (1) offer a smart way of managing energy supply of data centers powered by grid and renewable energy and (2) maintain a desirable balance between energy cost and system performance. To achieve this overarching objective, REDUX judiciously orchestrates distribute UPS devices (i.e., recharge or discharge) to allocate energy resources when (1) grid price is at low or high states or (2) renewable energy generation is at a low or fluctuate level. REDUX not only guarantees the stable operation of daily workload conditions, but also cuts back the energy cost of data centers by improving power resource utilization. Compared with the existing strategies, REDUX demonstrates a prominent capability of mitigating average peak workload and boosting renewable-energy utilization.Index Terms-Renewable energy, uninterruptible power supply (UPS), distributed UPS systems, resource management, energy cost, data centers.• Energy cost of large-scale data centers is skyrocketing.• Consuming renewable energy in data centers brings economical and environmental benefits.

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“…One of such projects, particularly interesting for adopting off-the-shelf boards, is the ICARUS project [10], aiming to build and study the performance of a mobile solar-powered cluster of NVIDIA Jetson boards.…”

Section: Introduction and Related Workmentioning

confidence: 99%

Power-Efficient Computing: Experiences from the COSA Project

Cesini¹,

Corni²,

Falabella³

et al. 2017

Scientific Programming

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Energy consumption is today one of the most relevant issues in operating HPC systems for scientific applications. The use of unconventional computing systems is therefore of great interest for several scientific communities looking for a better tradeoff between time-to-solution and energy-to-solution. In this context, the performance assessment of processors with a high ratio of performance per watt is necessary to understand how to realize energy-efficient computing systems for scientific applications, using this class of processors. Computing On SOC Architecture (COSA) is a three-year project (2015)(2016)(2017) funded by the Scientific Commission V of the Italian Institute for Nuclear Physics (INFN), which aims to investigate the performance and the total cost of ownership offered by computing systems based on commodity low-power Systems on Chip (SoCs) and high energyefficient systems based on GP-GPUs. In this work, we present the results of the project analyzing the performance of several scientific applications on several GPU-and SoC-based systems. We also describe the methodology we have used to measure energy performance and the tools we have implemented to monitor the power drained by applications while running.

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The ICARUS White Paper: A Scalable, Energy-Efficient, Solar-Powered HPC Center Based on Low Power GPUs

Cited by 7 publications

References 12 publications

Performance and Power Analysis of HPC Workloads on Heterogeneous Multi-Node Clusters

Performance and Power Analysis of HPC Workloads on Heterogeneous Multi-Node Clusters

Exploiting Renewable Energy and UPS Systems to Reduce Power Consumption in Data Centers

Power-Efficient Computing: Experiences from the COSA Project

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