Good Practices for Reproducible Research

Multi-core architectures comprising several GPUs have become mainstream in the eld of High-Performance Computing. However, obtaining the maximum performance of such heterogeneous machines is challenging as it requires to carefully o oad computations and manage data movements between the di erent processing units. The most promising and successful approaches so far rely on task-based runtimes that abstract the machine and rely on opportunistic scheduling algorithms. As a consequence, the problem gets shifted to choosing the task granularity, task graph structure, and optimizing the scheduling strategies. Trying di erent combinations of these di erent alternatives is also itself a challenge. Indeed, getting accurate measurements requires reserving the target system for the whole duration of experiments. Furthermore, observations are limited to the few available systems at hand and may be di cult to generalize. In this article, we show how we crafted a coarsegrain hybrid simulation/emulation of StarPU, a dynamic runtime for hybrid architectures, over SimGrid, a versatile simulator for distributed systems. This approach allows to obtain performance predictions accurate within a few percents on classical dense linear algebra kernels in a matter of seconds, which allows both runtime and application designers to quickly decide which optimization to enable or whether it is worth investing in higher-end GPUs or not.