Super Calculator using Compute Unified Device Architecture (CUDA)

Scientific computation requires a great amount of computing power especially
in floating-point operation but a high-end multi-cores processor is currently limited in
terms of floating point operation performance and parallelization. Recent
technological advancement has made parallel computing technically and financially
feasible using Compute Unified Device Architecture (CUDA) developed by NVIDIA.
This research focuses on measuring the performance of CUDA and implementing
CUDA for a scientific computation involving the process of porting the source code
from CPU to GPU using direct integration technique. The ported source code is then
optimized by managing the resources to achieve performance gain over CPU. It is
found that CUDA is able to boost the performance of the system up to 69 times in
Parboil Benchmark Suite. Successful attempt at porting Serpent encryption algorithm
and Lattice Boltzmann Method provided up to 7 times throughput performance gain
and up to 10 times execution time performance gain respectively over the CPU. Direct
integration guideline for porting the source code is then produced based on the two
implementations.