Parallelizing Graph Algorithms on GPU for Optimization

Abstract

Many practical applications include image processing, space searching, network analysis, graph partitioning etc. in that large graphs having a millions of vertices are commonly used and to process on that vertices is difficult task. Using high-end computers practical-time implementations are reported but are accessible only to a few. Efficient performance of those applications requires fast implementation of graph processing and hence Graphics Processing Units (GPUs) of today having a high computational power of accelerating capacity are deployed. The NVIDIA GPU can be treated as a SIMD processor array using the CUDA programming model. In this paper Breadth-First Search and All Pair shortest path and traveling salesmen problem graph algorithms are performed on GPU capabilities. The algorithms are introduced to optimize such that they can efficiently adopt GPU. Also an optimization technique that reduce data transfer rate CPU to GPU and reduce access of global memory is designed to reduce latency. Analysis of All pair shortest path algorithm by performing on different memories of GPU which shows that using shared memory can reduce execution time and increase speedup over CPU than global memory and coalescing access of data. TSP algorithm shows that increasing number of blocks and iteration obtained optimized tour length. Keyword: Graphics Processing unit, CUDA, BFS, All Pair Shortest path, TSP, Graph processing, optimization.