thesisRecent advancements in High Performance Computing (HPC) infrastructure with tradi- tional computing systems augmented with accelerators like graphic processing units (GPUs) and coprocessors like Intel Xeon Phi have successfully enabled predictive simulations specifi- cally Computational Fluid Dynamics (CFD) with more accuracy and speed. One of the most significant challenges in high-performance computing is to provide a software framework that can scale efficiently and minimize rewriting code to support diverse hardware configurations. Algorithms and framework support have been developed to deal with complexities and provide abstractions for a task to be compatible with various hardware targets. Software is written in C++ and represented as a Directed Acyclic Graph (DAG) with nodes that implement actual mathematical calculations. This thesis will present an improved approach for scheduling and execution of computational tasks within a heterogeneous CPU-GPU com- puting system insulting application developers with the inherent complexity in parallelism. The details will be presented within a context to facilitate the solution of partial differential equations on large clusters using graph theory
