Abstract:

Heterogeneous multiprocessors are increasingly important in the multi-core era due to their potential for high performance and energy
efficiency. In order for software to fully realize this potential, the step that maps computations to processing elements must be as
automated as possible. However, the state-of-the-art approach is to rely on the programmer to specify this mapping manually and
statically. This approach is not only labor intensive but also not adaptable to changes in runtime environments like problem sizes
and hardware/software configurations. In this study, we propose adaptive mapping, a fully automatic technique to map computations
to processing elements on a CPU+GPU machine. We have implemented it in our experimental heterogeneous programming
system called Qilin. Our results show that, by judiciously distributing works over the CPU and GPU, automatic adaptive mapping
achieves a 25% reduction in execution time and a 20% reduction in energy consumption than static mappings on average for a set of
important computation benchmarks. We also demonstrate that our technique is able to adapt to changes in the input problem size and
system configuration.