Creating and Debugging Performance CUDA C

Langdon, William B.

doi:10.1007/978-3-642-28789-3_2

Cited by 5 publications

(2 citation statements)

References 28 publications

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“…Performant parallel programming remains difficult [4]. After several decades of compiler development, it is widely accepted that completely automatic parallelisation using compiler technology is infeasible.…”

Section: Discussionmentioning

confidence: 99%

Improving 3D medical image registration CUDA software with genetic programming

Langdon

Modat

Petke

et al. 2014

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

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Genetic Improvement (GI) is shown to optimise, in some cases by more than 35%, a critical component of healthcare industry software across a diverse range of six nVidia graphics processing units (GPUs). GP and other search based software engineering techniques can automatically optimise the current rate limiting CUDA parallel function in the Nifty Reg open source C++ project used to align or register high resolution nuclear magnetic resonance NMRI and other diagnostic NIfTI images. Future Neurosurgery techniques will require hardware acceleration, such as GPGPU, to enable real time comparison of three dimensional in theatre images with earlier patient images and reference data. With millimetre resolution brain scan measurements comprising more than ten million voxels the modified kernel can process in excess of 3 billion active voxels per second.

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Section: Discussionmentioning

confidence: 99%

Improving 3D medical image registration CUDA software with genetic programming

Langdon

Modat

Petke

et al. 2014

Proceedings of the 2014 Annual Conference on Genetic and Evolutionary Computation

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show abstract

“…At what speed will that data be needed by the GPU processing cores? [Langdon, 2012]. Arithmetic intensity is the ratio of instructions performed per data item moved.…”

Section: Applying Gi To a New Gpu Applicationmentioning

confidence: 99%

Genetic improvement of GPU software

Langdon

Lam

Modat

et al. 2016

Genet Program Evolvable Mach

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We survey Genetic Improvement (GI) of general purpose computing on graphics cards. We summarise several experiments which demonstrate four themes. Experiments with the gzip program show that genetic programming (GP) can automatically port sequential C code to parallel code. Experiments with the StereoCamera program show that GI can upgrade legacy parallel code for new hardware and software. Experiments with NiftyReg and BarraCUDA show that GI can make substantial improvements to current parallel CUDA applications. Finally, experiments with the pknotsRG program show that with semi-automated approaches, enormous speed ups can sometimes be had by growing and grafting new code with genetic programming in combination with human input.

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Genetically Improved Software

Langdon

2015

Handbook of Genetic Programming Applications

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Creating and Debugging Performance CUDA C

Cited by 5 publications

References 28 publications

Improving 3D medical image registration CUDA software with genetic programming

Improving 3D medical image registration CUDA software with genetic programming

Genetic improvement of GPU software

Genetically Improved Software

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