Computer Science, Machine Learning, and Applied Mathematics
TimeWednesday, June 2814:00 - 16:00 CEST
DescriptionHigh-Performance Computing (HPC) data centers are facing a significant challenge in terms of energy efficiency as they strive to operate within stringent power budgets and reduce environmental pollution. The computing facilities that will be built for the Square Kilometre Array Telescope (SKA) are a prime example of this challenge, as they must process massive quantities of data from thousands of antennas with a limited power budget. In addition, new hardware technologies with increasing power footprints result in power-hungry supercomputers. In light of this, new strategies, tools, and architectures are being explored to address these issues. This mini-symposium aims to gather experts from diverse research environments to present solutions and methods being investigated in state-of-the-art research. The mini-symposium will dive into the energy efficiency and carbon footprint of SPH-EXA, a smoothed particle hydrodynamics. Then, it will focus on an improved Kernel Tuner, a generic autotuning tool for GPU applications that takes into account energy efficiency to find the best operational frequency for GPUs for SKA workloads. Finally, the mini-symposium will address lower lever optimization by presenting "exotic" solutions such as approximate computing design of Artificial Intelligence accelerators and domain-specific accelerators for biomedical workloads.