Binotto, AlecioAlecioBinottoPereira, Carlos EduardoCarlos EduardoPereiraKuijper, ArjanArjanKuijperStork, AndréAndréStorkFellner, Dieter W.Dieter W.Fellner2022-03-112022-03-112011https://publica.fraunhofer.de/handle/publica/37298210.1109/HPCC.2011.20A personal computer can be considered as a one-node heterogeneous cluster that simultaneously processes several application tasks. It can be composed by, for example, asymmetric CPU and GPUs. This way, a high-performance heterogeneous platform is built on a desktop for data intensive engineering calculations. In our perspective, a workload distribution over the Processing Units (PUs) plays a key role in such systems. This issue presents challenges since the cost of a task at a PU is non-deterministic and can be affected by parameters not known a priori. This paper presents a context-aware runtime and tuning system based on a compromise between reducing the execution time of engineering applications - due to appropriate dynamic scheduling - and the cost of computing such scheduling applied on a platform composed of CPU and GPUs. Results obtained in experimental case studies are encouraging and a performance gain of 21.77% was achieved in comparison to the static assignment of all tasks to the GPU.enhigh performance computingheterogeneous systemGraphics Processing Unit (GPU)Forschungsgruppe Semantic Models, Immersive Systems (SMIS)006conference paper