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- W2014311169 abstract "We present a parallel multi-hypothesis template tracking algorithm on heterogeneous platforms using a layered dispatch programming model. The contributions of this work are: an architecture-specific optimised solution for vasculature structure enhancement, an approach to segment the vascular lumen network from volumetric CTA images and a layered dispatch programming model to free the developers from hand-crafting mappings to particularly constrained execution domains on high throughput architecture. This abstraction is demonstrated through a vasculature segmentation application and can also be applied in other real-world applications.Current GPGPU programming models define a grouping concept which may lead to poorly scoped lo cal/ shared memory regions and an inconvenient approach to projecting complicated iterations spaces. To improve on this situation, we propose a simpler and more flexible programming model that leads to easier computation projections and hence a more convenient mapping of the same algorithm to a wide range of architectures.We first present an optimised image enhancement solution step- by-step, then solve a separable nonlinear least squares problem using a parallel Levenberg-Marquardt algorithm for template matching, and perform the energy efficiency analysis and performance comparison on a variety of platforms, including multi-core CPUs, discrete GPUs and APUs. We propose and discuss the efficiency of a layered-dispatch programming abstraction for mapping algorithms onto heterogeneous architectures." @default.
- W2014311169 created "2016-06-24" @default.
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- W2014311169 date "2013-02-19" @default.
- W2014311169 modified "2023-09-23" @default.
- W2014311169 title "Vasculature segmentation using parallel multi-hypothesis template tracking on heterogeneous platforms" @default.
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- W2014311169 doi "https://doi.org/10.1117/12.2002698" @default.
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