As modern GPUs rely partly on their on-chip memories

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Abstract. The Hough transform is a commonly used algorithm to detect lines and other features in images. It is robust to noise and occlusion, but has a large computational cost. This paper introduces two new implementations of the Hough transform for lines on a GPU. One focuses on minimizing processing time, while the other has an input-data independent processing time. Our results show that optimizing the GPU code for speed can achieve a speed-up over naive GPU code of about 10×. The implementation which focuses on processing speed is the faster one for most images, but the implementation which achieves a constant processing time is quicker for about 20% of the images.

The last decade has witnessed the blooming emergence of many-core platforms, especially the graphic processing units (GPUs). With the exponential growth of cores in GPUs, utilizing them efficiently becomes a challenge. The dataparallel programming model assumes a single instruction stream for multiple concurrent threads (SIMT); therefore little support is offered to enforce thread ordering and finegrained synchronizations. This becomes an obstacle when migrating algorithms which exploit fine-grained parallelism, to GPUs, such as the dataflow algorithms.In this paper, we propose a novel approach for fine-grained inter-thread synchronizations on the shared memory of modern GPUs. We demonstrate its performance and compare it with other fine-grained and medium-grained synchronization approaches. Our method achieves 1.5x speedup over the warp-barrier based approach and 4.0x speedup over the atomic spin-lock based approach on average. To further explore the possibility of realizing fine-grained dataflow algorithms on GPUs, we apply the proposed synchronization scheme to Needleman-Wunsch -a 2D wavefront application involving massive cross-loop data dependencies. Our implementation achieves 3.56x speedup over the atomic spin-lock implementation and 1.15x speedup over the conventional data-parallel implementation for a basic sub-grid, which implies that the fine-grained, lock-based programming pattern could be an alternative choice for designing general-purpose GPU applications (GPGPU).

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