Cabaleiro Domínguez, José CarlosFernández Pena, Anselmo TomásLaso Rodríguez, Rubén2023-06-222023-06-222023http://hdl.handle.net/10347/30753This thesis faces the challenges of dynamic workload optimisation and workload balancing in two different problems: in conventional systems using heterogeneous (CPU and GPU) parallelism, and in NUMA systems. On one hand, a library named IHP is proposed. Dynamically, CPU and GPU performance are evaluated so computational workload is divided accordingly. Results show that execution times can be improved between 3% and 55% depending on the code and the performance of the computing units. On the other hand, a tool for migrating threads and memory pages in NUMA systems has been developed. This tool incorporates several algorithms that, considering performance measurements, decide whether migrations are required. Experiments show that performance can be improved by up to 47%, particularly in multi-tasking scenarios.engAttribution-NonCommercial-NoDerivatives 4.0 Internacionalhttp://creativecommons.org/licenses/by-nc-nd/4.0/High Performance ComputingNUMAHardware Countersheterogeneous parallelismschedulingworkload balancing330406 Arquitectura de ordenadoresdoctoral thesisopen access