Parallel Algorithm of CPU and GPU oriented Heterogeneous Computation in SIFT Feature Matching

Abstract:

According to the basis of features about graphic processing unit(GPU) computation and tasks division, the study tries to bring forward a method of Master/Slave CPU+GPU heterogeneous computation. This paper presents an analysis and definition of the parallel data structures, and a description of the mapping mechanism for computing tasks on compute unified device architecture(CUDA). A logical scheduling algorithm is proposed to divide an issue or algorithm into many sub tasks. The result shows that the speed of SIFT parallel algorithm in the Geforce GTX 285 is about 30 time of the serial algorithm running in the CPU.

Key words:remote sensing image; feature matching; graphic processing unit (GPU); compute unified device architecture (CUDA); scale invariant feature transform(SIFT)