Supporting scientists in re-engineering sequential programs to parallel using model-driven engineering

Developing complex computational-intensive
and data-intensive scientific applications requires effective
utilization of the computational power of the available
computing platforms including grids, clouds, clusters, multicore
and many-core processors, and graphical processing
units (GPUs). However, scientists who need to leverage such
platforms are usually not parallel or distributed programming
experts. Thus, they face numerous challenges when
implementing and porting their software-based experimental
tools to such platforms. In this paper, we introduce a
sequential-to-parallel engineering approach to help scientists
in engineering their scientific applications. Our approach is
based on capturing sequential program details, planned
parallelization aspects, and program deployment details using
a set of domain-specific visual languages (DSVLs). Then, using
code generation, we generate the corresponding parallel
program using necessary parallel and distributed
programming models (MPI, OpenCL, or OpenMP). We
summarize three case studies (matrix multiplication, N-Body
simulation, and signal processing) to evaluate our approach.