Efficient Multi-Workload Execution for Sustainable GPU Performance

• Matheus M. Costa UFRGS
• Philippe O. A. Navaux UFRGS
• Silvio Rizzi Argonne National Laboratory
• Bronson Messer Oak Ridge National Laboratory
• Arthur F. Lorenzon UFRGS

Resumo

Modern scientific research often relies on powerful computing systems that use graphics processing units (GPUs) to run complex applications. However, running these systems requires a large amount of energy, which contributes to carbon emissions and raises concerns about environmental impact. Given this scenario, we explore how sharing a single GPU between multiple applications can improve both performance and sustainability when running scientific workflows. We consider three execution strategies: running applications one after another, running two at the same time, and replacing finished tasks with new ones right away, using eighteen widely used scientific applications on three different GPUs (AMD MI250X, AMD RX 7900XT, and NVIDIA RTX 4090). To demonstrate that finding the best co-execution combination of applications improves resource efficiency, we use a mathematical approach based on linear programming to schedule which applications run together. Our results show that this approach can reduce total execution time by up to 47% and lower carbon emissions by as much as 34%, with minimal impact on the performance of individual applications. Additionally, when optimal combinations of parallel applications are used, the overall performance of a complete scientific workflow can improve by 36%, while carbon emissions are reduced by 25%.

Palavras-chave: Schedules, Runtime, High performance computing, Graphics processing units, Carbon dioxide, Computer architecture, Linear programming, Hardware, Energy efficiency, Sustainable development, GPU-Sharing, Carbon Emissions, Scientific Workflow, Energy-efficiency