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), and Super 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 study and Genetic Algorithm as a method to solve TSP. Travelling Salesman Problem is belong often the case in real life and has a high computational complexity. While the Genetic Algorithm (GA) is belong a reliable algorithm to solve complex cases, but has the disadvantage that the time complexity level is very high. In some research related to HPC infrastructure comparison, the performance of multi-core CPU single node for data computation has not been done. Whereas 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 solving high 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 deliver 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.