Towards dynamic reconfigurable load-balancing for hybrid desktop platforms

High-performance platforms are required by applications that use massive calculations. Actually, desktop accelerators (like the GPUs) form a powerful heterogeneous platform in conjunction with multi-core CPUs. To improve application performance on these hybrid platforms, load-balancing plays an important role to distribute workload. However, such scheduling problem faces challenges since the cost of a task at a Processing Unit (PU) is non-deterministic and depends on parameters that cannot be known a priori, like input data, online creation of tasks, scenario changing, etc. Therefore, self-adaptive computing is a potential paradigm as it can provide flexibility to explore computational resources and improve performance on different execution scenarios. This paper presents an ongoing PhD research focused on a dynamic and reconfigurable scheduling strategy based on timing profiling for desktop accelerators. Preliminary results analyze the performance of solvers for SLEs (Systems of Linear Equations) over a hybrid CPU and multi-GPU platform applied to a CFD (Computational Fluid Dynamics) application. The decision of choosing the best solver as well as its scheduling must be performed dynamically considering online parameters in order to achieve a better application performance.