Energy Aware HPC Revisited: AI Concerns, Architectures and Optimization Strategies

Dear Colleagues,

This Special Issue invites original research papers that report on the state-of-the-art and recent advancements in energy-aware high-performance computing (HPC) and optimization strategies in applications supporting artificial intelligence (AI).

Energy efficiency is one of the main concerns of today’s computer systems, particularly in HPC, where energy consumption has a significant impact on both operational and environmental costs. There are several approaches to this problem at different levels, such as developing more efficient software, hardware, and architectures, as well as improving power management software or even the associated cooling system.

Although the only measures that were considered in the past were flops, with the advent of artificial intelligence and big data, the HPC community has realized that this form of design contributes to supercomputers, which consume a lot of electricity. Indeed, the growing demand on power processing shows that high energy consumption HPC systems need to evolve, and the attempt to achieve energy-efficient HPC architectures has increased in recent years.

These features expose the tradeoffs between performance and power consumption, leaving room for the production of more energy-efficient algorithms and operating systems. Indeed, adequate computation is an emerging paradigm, in which the accuracy of computational results can be achieved, for example, for energy savings and performance improvement at run time. This concept explores how IT systems (from embedded devices to HPC) can be improved to be "more energy-efficient, faster, and less complex", by relaxing the requirement that they should be exactly correct. For example, several recent approaches attempt to design CNN models and hardware architectures to jointly maximize accuracy throughput, while minimizing energy and costs. Approaches like this are sometimes based on an approximate calculation of the aforementioned AI algorithms, by reducing the precision of the arithmetic operation, by reducing the number of operands or by building adequate functional units. 

This Special Issue is focused on sharing and showing new proposals regarding energy aware HPC optimization strategies in the context of novel applications supporting AI. Prospective authors are invited to submit high-quality original contributions and reviews to this Special Issue. Potential topics include, but are not limited to, the following:

• Cost efficient HPC architectures, topologies, and strategies
• Energy aware HPC algorithm and architecture adequacy
• Approximate computing
• Low-power reconfigurable accelerators for heterogeneous HPC
• Configurable, custom, optimized CNNs topologies for energy aware HPC

Prof. Dr. Carlos Valderrama
Prof. Dr. Fan YANG
Asst. Prof. Dr. Khaled Ben Khalifa
Assoc. Prof. Dr. Marcelo A. C. Fernandes
Prof. Dr. Chokri Souani
Dr. Eva Dokladalova
Dr. Arvind R Yadav
Guest Editor

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