Session

DescriptionEnergy and power challenges increase as High-Performance Computing and AI scale to meet rapid industry and research demands. These challenges include higher CO2 emissions, increased energy costs, and strain on the power infrastructure. HPC centers are looking to reduce energy consumption and enhance energy efficiency by optimizing resource utilization and managing their workloads more efficiently. Efforts to improve energy efficiency often focus on hardware advancements, such as microarchitectures, intra-core parallelism, vectorization, and accelerators for critical workloads. These innovations reduced idle power and improved execution but have also introduced challenges like swift power variations. Data center infrastructure, rack design, and cooling techniques have also progressed. Liquid cooling, especially direct hot-water cooling, has gained traction for its cost-saving potential. Although such hardware improvements are impressive, they cannot fully address energy challenges due to their limited adaptability to workloads. Complementary software solutions provide a global view of system status and energy usage, support dynamic adaptation across the stack, enable long-term predictions of resource use, and deliver actionable insights on workload optimizations to users. Research on power-steering runtimes and monitoring tools has contributed to user-facing analytics tools. The rapid progress of AI techniques opens additional opportunities for energy efficiency and optimization in HPC systems.