Real-time remote sensing applications like search and rescue missions, military target detection, environmental monitoring, hazard prevention and other time-critical applications require onboard real time processing capabilities or autonomous decision making. Some unmanned remote systems like satellites are physically remote from their operators, and all control of the spacecraft and data returned by the spacecraft must be transmitted over a wireless radio link. This link may not be available for extended periods when the satellite is out of line of sight of its ground station. Therefore, lightweight, small size and low power consumption hardware is essential for onboard real time processing systems. With increasing dimensionality, size and resolution of recent hyperspectral imaging sensors, additional challenges are posed upon remote sensing processing systems and more capable computing architectures are needed. Graphical Processing Units (GPUs) emerged as promising architecture for light weight high performance computing that can address these computational requirements for onboard systems. The goal of this study is to build high performance methods for onboard hyperspectral analysis. We propose accelerated methods for the well-known recursive hierarchical segmentation (RHSEG) clustering method, using GPUs, hybrid multicore CPU with a GPU and hybrid multi-core CPU/GPU clusters. RHSEG is a method developed by the National Aeronautics and Space Administration (NASA), which is designed to provide rich classification information with several output levels. The achieved speedups by parallel solutions compared to CPU sequential implementations are 21x for parallel single GPU and 240x for hybrid multi-node computer clusters with 16 computing nodes. The energy consumption is reduced to 74% using a single GPU compared to the equivalent parallel CPU cluster.
翻译:实时遥感应用,如搜索和救援任务、军事目标探测、环境监测、危险预防和其他时间紧迫应用,需要实时实时处理能力或自主决策。卫星等一些无人遥感系统在物理上远离其操作者,航天器和航天器返回的数据的所有控制都必须通过无线无线电链路传输。当卫星在地面站视线之外时,这种链接可能无法长期使用。因此,轻量、小型和低功率消耗硬件对于机载实时处理系统至关重要。随着最近超光谱成像传感器的日益广度、大小和分辨率的提高,遥感处理系统以及更有能力的计算结构也面临更多的挑战。图形处理单位(GPU)作为光重高性能计算结构出现,能够满足机载系统的这些计算要求。本研究的目标是在卫星超光谱分析方面建立高性能方法。我们建议对众所周知的循环级级分解(RHSEGEGEG)采用GPU和混合多重的平行计算方法,使用GPUS-CO的混合混合多重的平行计算方法,由GPU-C-C-C-C-C-C-C-C-C-C-C-CHR-IL-C-C-C-C-C-C-C-C-ILVAL-SAL-C-C-C-C-C-C-C-C-C-C-C-I-I-I-S-S-SAL-S-S-S-S-S-S-S-S-S-S-SL-SL-S-S-S-SLV-SL-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-S-SL-SL-SAL-SL-SL-SL-SL-SL-SL-SL-SL-I-S-S-S-S-S-S-S-S-S-S-S-S-SL-S-S-S-S-S-S-S-S-SL-