Performing with CUDA

Recently a GPGPU application had to be redesigned to overcome performance problems. A number of software engineering lessons were learnt from this and other projects. We describe those about obtaining high performance from nVidia GPUs and practical aspects of CUDA C software development.

[1]  Wolfgang Banzhaf,et al.  Fast Genetic Programming on GPUs , 2007, EuroGP.

[2]  William B. Langdon Debugging CUDA , 2011, GECCO '11.

[3]  Henry Wong,et al.  Analyzing CUDA workloads using a detailed GPU simulator , 2009, 2009 IEEE International Symposium on Performance Analysis of Systems and Software.

[4]  G.E. Moore,et al.  Cramming More Components Onto Integrated Circuits , 1998, Proceedings of the IEEE.

[5]  William B. Langdon,et al.  A fast high quality pseudo random number generator for nVidia CUDA , 2009, GECCO '09.

[6]  John D. Owens,et al.  GPU Computing , 2008, Proceedings of the IEEE.

[7]  Joachim Stender,et al.  Parallel Genetic Algorithms: Theory and Applications , 1993 .