Performance Scalability of a Remote Sensing Application on High Performance Reconfigurable Platforms

The trend for remote sensing satellite missions has always been towards smaller size, lower cost, more flexibility, and higher computational power. Reconfigurable Computers (RCs) combine the flexibility of traditional microprocessors with the power of Field Programmable Gate Arrays (FPGAs). Multi-node systems of High-Performance RCs (HPRCs) are becoming more popular recently as dictated by the requirements of many large-scale applications. Therefore, HPRCs are a promising candidate for onboard preprocessing. However, in order to meet the aggressive demands of those large-scale applications such as remote sensing, scalability of such applications on HPRCs may become an issue that can negatively affect the overall performance. In this paper, we investigate the potential of using HPRCs for on-board preprocessing by studying and characterizing the scalability of the Landsat 7 ETM+ ACCA algorithm on two of the stateof-the-art reconfigurable platforms, SRC-6 and CrayXD1.