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A study on a computational model for GPGPU algorithms and its application to medical image processing

GPGPU算法计算模型及其在医学图像处理中的应用研究

基本信息

批准号：
18300009
负责人：
HAGIHARA Kenichi
金额：
$ 10.72万
依托单位：
Osaka University
依托单位国家：
日本
项目类别：
Grant-in-Aid for Scientific Research (B)
财政年份：
2006
资助国家：
日本
起止时间：
2006 至 2007
项目状态：
已结题

来源：
https://kaken.nii.ac.jp/grant/KAKENHI-PROJECT-18300009/
关键词：
GPGPU OpenGL CUDA cone-beam CT (computed tomography) image reconstru array alignment real-time processing all pairs shortest path memory bottleneck 性能モデルストリームプログラミング命令移動データ転送データ圧縮

项目摘要

The purpose of this research is to investigate to what extent medical image processing can be sped up on a single PC with a GPU as opposed to a PC cluster. For this, we developed two programs for cone-beam CT (computed tomography) image reconstruction-one using OpenGL/Cg and one using CUDA. For 360 images of 512 x 512 pixels each, we reconstructed a 512x512x512 voxel volume. Execution time for the OpenGL/Cg version was 8.3 seconds, and for the CUDA version was 5.7 seconds. The OpenGL/Cg version was 23.7 times faster than the CPU version (in other words, it has equivalent power of at least a 24 PC cluster). Therefore we confirmed that a single PC with GPU can provide enough computing power for real-time operation of a CT machine. Starting in April 2008, a CT machine using this algorithm was being sold by the Shimadzu Corporation.Programs that requires much CPU time are said to have a "calculation bottleneck", and the current impression is that CPU execution speed is the main bottleneck in computation. However, even among these types of programs there is a not-insignificant number which have a "memory bottleneck". Based on last years success and results from the GPGPU performance model tested last year, we are confident that GPGPU speed improvements will also be applicable to such "memory bottleneck" programs.In addition to cone-beam CT image reconstruction, we also used CUDA to implement a biology oriented array alignment program, as well as a shortest-path-between-all-points implementation for graph theory. Run time of the array alignment program was substantially decreased on GPU compared to CPU, with a 1024 length query taking about 20 seconds on the GPU compared to 677 seconds on the CPU. A query length of 1024 is suitable for work with amino acids and we feel this will contribute to pharmaceutical research.

本研究的目的是调查在配备 GPU 的单个 PC 上（相对于 PC 集群）可以在多大程度上加速医学图像处理。为此，我们开发了两个用于锥束 CT（计算机断层扫描）图像重建的程序，一个使用 OpenGL/Cg，另一个使用 CUDA。对于每张 512 x 512 像素的 360 幅图像，我们重建了 512x512x512 体素体积。 OpenGL/Cg 版本的执行时间为 8.3 秒，CUDA 版本的执行时间为 5.7 秒。 OpenGL/Cg 版本比 CPU 版本快 23.7 倍（换句话说，它具有至少相当于 24 个 PC 集群的能力）。因此我们确认单台带有GPU的PC可以为CT机的实时操作提供足够的计算能力。从2008年4月开始，岛津公司开始销售使用该算法的CT机。需要大量CPU时间的程序被称为“计算瓶颈”，目前的印象是CPU执行速度是计算的主要瓶颈。然而，即使在这些类型的程序中，也有不少存在“内存瓶颈”。基于去年的成功和去年测试的 GPGPU 性能模型的结果，我们相信 GPGPU 速度的改进也将适用于此类“内存瓶颈”程序。除了锥束 CT 图像重建之外，我们还使用 CUDA 实现了面向生物学的阵列对齐程序，以及图论的所有点之间的最短路径实现。与 CPU 相比，GPU 上的数组对齐程序的运行时间显着减少，1024 长度的查询在 GPU 上花费约 20 秒，而在 CPU 上花费 677 秒。 1024 的查询长度适合处理氨基酸，我们认为这将有助于药物研究。