Cg: a system for programming graphics hardware in a C-like language
ACM SIGGRAPH 2003 Papers
Extended-precision floating-point numbers for GPU computation
ACM SIGGRAPH 2006 Research posters
Linear algebra operators for GPU implementation of numerical algorithms
SIGGRAPH '05 ACM SIGGRAPH 2005 Courses
GPU Computing: Programming a Massively Parallel Processor
Proceedings of the International Symposium on Code Generation and Optimization
Fast parametric elastic image registration
IEEE Transactions on Image Processing
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In this article we discuss the accuracy issues that arise, when implementing cubic B-spline interpolation on the Graphics Processing Unit (GPU). Imprecision is inherent to the discrete nature of digital computing, especially when using floating point numbers. However, there are special accuracy issues to deal with, when employing the GPU. The GPU is more and more being regarded as a general purpose parallel co-processor, and currently it is also finding its way in environments, where the algorithmic outcome has great impact, such as in biomedical image analysis. For such applications it is eminent that the accuracy of the results is documented, and properly taken into account. Next to analyzing the accuracy of the cubic B-spline interpolation, we propose a modification to the GPU algorithm, in order to increase its precision, without sacrificing any significant performance.