High Performance Computing (HPC) platforms allow scientists to model
computationally intensive algorithms. HPC clusters increasingly use
General-Purpose Graphics Processing Units (GPGPUs) as accelerators; FPGAs
provide an attractive alternative to GPGPUs for use as co-processors, but they
are still far from being mainstream due to a number of challenges faced when
using FPGA-based platforms. Our research aims to make FPGA-based high
performance computing more accessible to the scientific community. In this work
we present the results of investigating the acceleration of a particular
atmospheric model, Flexpart, on FPGAs. We focus on accelerating the most
computationally intensive kernel from this model. The key contribution of our
work is the architectural exploration we undertook to arrive at a solution that
best exploits the parallelism available in the legacy code, and is also
convenient to program, so that eventually the compilation of high-level legacy
code to our architecture can be fully automated. We present the three different
types of architecture, comparing their resource utilization and performance,
and propose that an architecture where there are a number of computational
cores, each built along the lines of a vector instruction processor, works best
in this particular scenario, and is a promising candidate for a generic
FPGA-based platform for scientific computation. We also present the results of
experiments done with various configuration parameters of the proposed
architecture, to show its utility in adapting to a range of scientific
applications.Comment: This is an extended pre-print version of work that was presented at
the international symposium on Highly Efficient Accelerators and
Reconfigurable Technologies (HEART2014), Sendai, Japan, June 911, 201
