This thesis offers contributions to improve performance and energy efficiency in these massively parallel systems. However, programmers must handle all device management, workload distribution and code portability between systems, significantly complicating their programming. Co-execution allows all devices to simultaneously compute the same problem, cooperating to consume less time and energy. This strongly penalizes accelerator utilization and system energy consumption, as well as making it difficult to adapt applications. Their complexity implies that they are usually used under the task paradigm and the host-device programming model. Heterogeneous systems are becoming increasingly relevant, due to their performance and energy efficiency capabilities, being present in all types of computing platforms, from embedded devices and servers to HPC nodes in large data centers.
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