Improving Global Performance on GPU for Algorithms with Main Loop Containing a Reduction Operation: Case of Dijkstra’s Algorithm

Abstract

In this paper, we study the impact of copying data in GPU computing. GPU computing allows implementing parallel computations at low cost: a GPU can be purchased at under USD 500. Many studies have shown that GPU can be used to speed up the calculations. But for algorithms requiring doing a part of the calculations on GPU and another part on CPU, alternately, latency due to the copy of the data is a performance degradation factor. To illustrate this, we consider the Dijkstra's algorithm on the shortest path used in solving optimization problems. This algorithm is very heavy to run on sequential machine. So, we are considering a parallel approach on GPU. Note that Dijkstra's algorithm has been subject of many implementations on GPU. In the present work, we use two platforms with external GPU. Graphs are represented in adjacency matrix. During the computation of this algorithm, intermediates results are copied from GPU to CPU or from CPU to GPU. The purpose of this work is to measure the impact of these copies in the overall performance of the algorithm. For that we calculate time due to the copying data's implementation; then we compare results with implementation computing only on CPU memory (zero-copy). The real impact shown by experiments demonstrates the interest of this study. GP-GPU programmers have to think that they will use either memory zero-copy or GPU memory. The challenge for GPU's manufacturers is how to reduce this impact.

Locality-Based Relaxation: An Efficient Method for GPU-Based Computation of Shortest Paths

“…In addition to our algorithm and Harish et al's [12,13], Singh et al [26], Chaibou et al [6] and Ortega et al [20,21] present algorithms with just one atomic statement. Chaibou et al [6] evaluate the cost of memory copy between CPU and GPU. Ortega et al [20,21] propose an algorithm based on Dijkstra's algorithm to find SSSP.…”