Abstract
Hybrid multicore processors (HMPs) are poised to dominate the landscape of the next generation of computing on the desktop as well as on exascale systems. HMPs consist of general purpose CPU cores along with specialized coprocessors and can provide high performance for a wide spectrum of applications at significantly lower energy requirements per floating-point operations per second (FLOP). In this article, we develop parallel algorithms and software for constructing multiresolution synthetic aperture radar images on HMPs. We develop several load balancing algorithms for optimizing time performance and energy on HMPs. We also present a systematic approach for deriving the energy-time performance tradeoffs on HMPs in the presence of dynamic voltage frequency scaling. Pareto-optimal curves are presented on a system consisting of 24 traditional cores and a GPU.
Highlights
S YNTHETIC aperture radar (SAR) image formation utilizes tensor product-based transformation of radar return pulse histories to yield a spatial representation containing possible target objects
We show that, depending upon whether time performance or energy optimization is critical, separate load balancing strategies should be used on Hybrid multicore processors (HMPs)
1) DVFS CPU Results: Figs. 11– 13 present SAR reconstruction runtime, power, and energy when tile-based partitioning was employed. 3000, 4000, and 5000 pulses were used with 2048 × 2048 pixel and 4096 × 4096 pixel image sizes
Summary
S YNTHETIC aperture radar (SAR) image formation utilizes tensor product-based transformation of radar return pulse histories to yield a spatial representation containing possible target objects Algorithms such as backprojection have been employed in reconstructing images from SAR pulse data to produce better quality reconstructions than frequency domain algorithms [1]–[3] due to support for higher resolution and fewer assumptions about the image, albeit with high computation time [4], [5]. Mulitresolution approaches for improving the time performance of sequential SAR algorithms were proposed in [6] This is beneficial, for example, in change detection applied to reconstructed SAR video, where reduced resolution (and lower computational time) may be appropriate for background regions, while candidate target regions are rendered at higher resolution.
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