Solutions For Optimizing The Stream Compaction Algorithmic Function Using The Compute Unified Device Architecture
Alexandru Pîrjan · RePEc: Research Papers in Economics · 2012
In this paper, I have researched and developed solutions for optimizing the stream compaction algorithmic function using the Compute Unified Device Architecture (CUDA). The stream compaction is a common parallel primitive, an essential building block for many data processing algorithms, whose optimization improves the performance of a wide class of parallel algorithms useful in data processing. A particular interest in this research was to develop solutions for optimizing the stream compaction algorithmic function that offers optimal solutions over an entire range of CUDA enabled GPUs: Tesla GT200, Fermi GF100 and the latest Kepler GK104 architecture, released on 22 March 2012. In order to confirm the utility of the developed optimization solutions, I have extensively benchmarked and evaluated the performance of the stream compaction algorithmic function in CUDA.