The Optimal High Performance Computing Infrastructure for Solving High Complexity Problem
Abstract: The high complexity
of the problems today requires increasingly powerful hardware performance. Corresponding
economic laws, the more reliable the performance of the hardware, it will be
comparable to the higher price. Associated with the high-performance computing
(HPC) infrastructures, there are three hardware architecture that can be used,
i.e. Computer Cluster, Graphical Processing Unit (GPU), andSuper Computer. The
goal of this research is to determine the most optimal of HPC infrastructure to
solve high complexity problem. For this reason, we chose Travelling Salesman
Problem (TSP) as a case studyand Genetic Algorithm as a method to solve TSP.
Travelling Salesman Problem is belonging often thecase in real life and has a
high computational complexity. While the Genetic Algorithm (GA) belongs areliable
algorithm to solve complex cases but has the disadvantage that the time
complexity level is veryhigh. In some research related to HPC infrastructure
comparison, the performance of multi-core CPUsingle node for data computation
has not been done. The current development trend leads to the development of
PCs with higher specifications like this. Based on the experiments results, we
conclude that the use of GA is very effective to solve TSP. the use of
multi-core single-node in parallel for solvinghigh complexity problems as far
as this is still better than the two other infrastructure but slightly below compare
to multi-core single-node serially, while GPU delivers the worst performance
compared to others infrastructure. The utilization of a super computer PC for
data computation is still quite promising considering the ease of
implementation, while the GPU utilization for the purposes of data computing is
profitable if we only utilize GPU to support CPU for data computing.
Author: Yuliant Sibaroni,
Fitriyani, Fhira Nhita
Journal Code: jptkomputergg160303
