Reliable mapping and partitioning of performance-constrained openCL applications on CPU-GPU MPSoCs

Heterogeneous Multi-Processor Systems-on-Chips (MPSoCs) containing CPU and GPU cores are typically required to execute applications concurrently. Existing approaches exploit applications executing in CPU and GPU cores at the same time taking into account performance and energy consumption for mapping and partitioning. This paper presents a proposal for mapping and partitioning of applications in CPU-GPU MPSoCs taking into account the temperature behavior of the system. We evaluate the temperature profiling to partition the applications between CPU and GPU. The profiling is done by measuring the temperature of the CPU and GPU cores while executing different applications at different partitions. Results shown up to 13% savings of average temperature of the chip while maintaining performance requirements. A lower thermal behavior represents a better long-term reliability (lifetime) of the SoC.