Challenges in Automatic Software Optimisation: the energy efficiency case

ith the advent of the Exascale capability allowing supercomputers to perform at least 1018 IEEE 754 Double Precision (64 bits) operations per second, many concerns have been raised regarding the energy consumption of high-performance computing code. Recently, Frontier operated by the Oak Ridge National Laboratory, has become the first supercomputer to break the exascale barrier
[1]. In total, Frontier contains 9,408 CPUs, 37,632 GPUs, and 8,730,112 cores. This world-leading supercomputer consumes about 21 megawatts which is truly remarkable as Frontier was also ranked first on the Green500 list before being recently replaced. The previous top Green500 machine, MN-3 in Japan, provided 39.38 gigaflops per watt, while the Frontier delivered 62.68 gigaflops per watt.
All these infrastructure and hardware improvements are just the tip of the Iceberg. Energy-aware code is now required to minimize the energy consumption of distributed and/or multi-threaded software. For example, the data movement bottleneck is responsible for 35 − 60% of a system’s energy consumption during intra-node communication. In an HPC environment, additional energy
is consumed through inter-node communication. This position paper aims to introduce future research directions to enter now in the age of energy-aware software. The paper is organized as follows. First, we introduce related works regarding measurement and energy optimisation. Then we propose to focus on the two different levels of granularity in energy optimisation.