A High-Performance Multi-Element Processing Framework on GPUs

Many computational engineering problems ranging from finite element methods to image processing involve the batch processing on a large number of data items. While multielement processing has the potential to harness computational power of parallel systems, current techniques often concentrate on maximizing elemental performance. Frameworks that take this greedy optimization approach often fail to extract the maximum processing power of the system for multi-element processing problems. By ultilizing the knowledge that the same operation will be accomplished on a large number of items, we can organize the computation to maximize the computational throughput available in parallel streaming hardware. In this paper, we analyzed weaknesses of existing methods and we proposed efficient parallel programming patterns implemented in a high performance multi-element processing framework to harness the processing power of GPUs. Our approach is capable of levering out the performance curve even on the range of small element size. A High-Performance Multi-Element Processing Framework on GPUs Linh Ha, James King, Zhisong Fu and Robert M. Kirby Scientific Computing and Imaging Institute University of Utah Email: {lha, jsking, zhisong, kirby}@sci.utah.edu Abstract—Many computational engineering problems ranging from finite element methods to image processing involve the batch processing on a large number of data items. While multielement processing has the potential to harness computational power of parallel systems, current techniques often concentrate on maximizing elemental performance. Frameworks that take this greedy optimization approach often fail to extract the maximum processing power of the system for multi-element processing problems. By ultilizing the knowledge that the same operation will be accomplished on a large number of items, we can organize the computation to maximize the computational throughput available in parallel streaming hardware. In this paper, we analyzed weaknesses of existing methods and we proposed efficient parallel programming patterns implemented in a high performance multi-element processing framework to harness the processing power of GPUs. Our approach is capable of levering out the performance curve even on the range of small element size.Many computational engineering problems ranging from finite element methods to image processing involve the batch processing on a large number of data items. While multielement processing has the potential to harness computational power of parallel systems, current techniques often concentrate on maximizing elemental performance. Frameworks that take this greedy optimization approach often fail to extract the maximum processing power of the system for multi-element processing problems. By ultilizing the knowledge that the same operation will be accomplished on a large number of items, we can organize the computation to maximize the computational throughput available in parallel streaming hardware. In this paper, we analyzed weaknesses of existing methods and we proposed efficient parallel programming patterns implemented in a high performance multi-element processing framework to harness the processing power of GPUs. Our approach is capable of levering out the performance curve even on the range of small element size.