Cache-aware GPU memory scheduling scheme for CT back-projection

Zheng, Zhaoqiang; Mueller, Klaus

doi:10.1109/nssmic.2010.5874183

Cited by 3 publications

(1 citation statement)

References 6 publications

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“…Zheng et al [22] presented a cache-aware memory-scheduling scheme for cone beam backprojection [23], [24], which is an inverse volume rendering problem. In other words, a series of 2-D projections are backprojected into 3-D space to reconstruct volume data.…”

Section: Related Workmentioning

confidence: 99%

Cache-Aware, In-Place Rotation Method for Texture-Based Volume Rendering

Misaki

Ino

Hagihara

2017

IEICE Trans. Inf. & Syst.

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SUMMARYWe propose a cache-aware method to accelerate texturebased volume rendering on a graphics processing unit (GPU) that is compatible with the compute unified device architecture. The proposed method extends a previous method such that it can maximize the average rendering performance while rotating the viewing direction around a volume. To realize this, the proposed method performs in-place rotation of volume data, which rearranges the order of voxels to allow consecutive threads (warps) to refer to voxels with the minimum access strides. Experiments indicate that the proposed method replaces the worst texture cache (TC) hit rate of 42% with the best TC hit rate of 93% for a 1024 3 -voxel volume. Thus, the average frame rate increases by a factor of 1.6 in the proposed method compared with that in the previous method. Although the overhead of inplace rotation slightly decreases the frame rate from 2.0 frames per second (fps) to 1.9 fps, this slowdown occurs only with a few viewing directions.

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Section: Related Workmentioning

confidence: 99%

Cache-Aware, In-Place Rotation Method for Texture-Based Volume Rendering

Misaki

Ino

Hagihara

2017

IEICE Trans. Inf. & Syst.

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Creating optimal code for GPU‐accelerated CT reconstruction using ant colony optimization

2013

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Cache-Aware GPU Optimization for Out-of-Core Cone Beam CT Reconstruction of High-Resolution Volumes

Ino

Hagihara

2016

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper proposes a cache-aware optimization method to accelerate out-of-core cone beam computed tomography reconstruction on a graphics processing unit (GPU) device. Our proposed method extends a previous method by increasing the cache hit rate so as to speed up the reconstruction of high-resolution volumes that exceed the capacity of device memory. More specifically, our approach accelerates the well-known Feldkamp-Davis-Kress algorithm by utilizing the following three strategies: (1) a loop organization strategy that identifies the best tradeoff point between the cache hit rate and the number of off-chip memory accesses; (2) a data structure that exploits high locality within a layered texture; and (3) a fully pipelined strategy for hiding file input/output (I/O) time with GPU execution and data transfer times. We implement our proposed method on NVIDIA's latest Maxwell architecture and provide tuning guidelines for adjusting the execution parameters, which include the granularity and shape of thread blocks as well as the granularity of I/O data to be streamed through the pipeline, which maximizes reconstruction performance. Our experimental results show that it took less than three minutes to reconstruct a 2048 3 -voxel volume from 1200 2048 2 -pixel projection images on a single GPU; this translates to a speedup of approximately 1.47 as compared to the previous method.

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Cache-aware GPU memory scheduling scheme for CT back-projection

Cited by 3 publications

References 6 publications

Cache-Aware, In-Place Rotation Method for Texture-Based Volume Rendering

Cache-Aware, In-Place Rotation Method for Texture-Based Volume Rendering

Creating optimal code for GPU‐accelerated CT reconstruction using ant colony optimization

Cache-Aware GPU Optimization for Out-of-Core Cone Beam CT Reconstruction of High-Resolution Volumes

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