Reducing overheads of dynamic scheduling on heterogeneous chips

In recent processor development, we have witnessed the integration of GPU and CPUs into a single chip. The result of this integration is a reduction of the data communication overheads. This enables an efficient collaboration of both devices in the execution of parallel workloads. In this work, we focus on the problem of efficiently scheduling chunks of iterations of parallel loops among the computing devices on the chip (the GPU and the CPU cores) in the context of irregular applications. In particular, we analyze the sources of overhead that the host thread experiments when a chunk of iterations is offloaded to the GPU while other threads are executing concurrently other chunks on the CPU cores. We carefully study these overheads on different processor architectures and operating systems using Barnes Hut as a study case representative of irregular applications. We also propose a set of optimizations to mitigate the overheads that arise in presence of oversubscription and take advantage of the different features of the heterogeneous architectures. Thanks to these optimizations we reduce Energy-Delay Product (EDP) by 18% and 84% on Intel Ivy Bridge and Haswell architectures, respectively, and by 57% on the Exynos big.LITTLE.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Asynchronous In Situ Processing with Gromacs: Taking Advantage of GPUs

International audienceNumerical simulations using supercomputers are producing an ever growing amount of data. Efficient production and analysis of these data are the key to future discoveries. The in situ paradigm is emerging as a promising solution to avoid the I/O bottleneck encountered in the file system for both the simulation and the analytics by treating the data as soon as they are produced in memory. Various strategies and implementations have been proposed in the last years to support in situ treatments with a low impact on the simulation performance. Yet, little efforts have been made when it comes to perform in situ analytics with hybrid simulations supporting accelerators like GPUs. In this article, we propose a study of the in situ strategies with Gromacs, a molecular dynamic simulation code supporting multi-GPUs, as our application target. We specifically focus on the computational resources usage of the machine by the simulation and the in situ analytics. We finally extend the usual in situ placement strategies to the case of in situ analytics running on a GPU and study their impact on both Gromacs performance and the resource usage of the machine. We show in particular that running in situ analytics on the GPU can be a more efficient solution than on the CPU especially when the CPU is the bottleneck of the simulation