Parallel Fast Walsh Transform Algorithm and Its Implementation with CUDA on GPUs

1. Alvarez-Cubero, J., P. Zufiria. A C++ Class for Analysing Vector Boolean Functions from a Cryptographic Perspective. – In: Proc. of International Conference on Security and Cryptography (SECRYPT’10), 2010, pp. 512-520.Search in Google Scholar

2. Andrade, J., G. Falcao, V. Silva. Optimized Fast Walsh-Hadamard Transform on GPUs for Non-Binary LDPC Decoding. – Parallel Computing, Vol. 40, 2014, No 9, pp. 449-453.10.1016/j.parco.2014.07.001Open DOISearch in Google Scholar

3. Bouyukliev, I., D. Bikov. Applications of the Binary Representation of Integers in Algorithms for Boolean Functions. – In: Proc. of 44th Spring Conference of the Union of Bulgarian Mathematicians, Mathematics and Education in Mathematics, 2015, pp. 161-166.Search in Google Scholar

4. Carlet, C. Boolean Functions for Cryptography and Error Correcting Codes. – In: C. Crama and P. Hammer, Eds. Boolean Models and Methods in Mathematics, Computer Science, and Engineering. Cambridge University Press, 2010, pp. 257-397.10.1017/CBO9780511780448.011Search in Google Scholar

5. Copeland, A. D., N. B. Chang, S. Lung. GPU Accelerated Decoding of High Performance Error Correcting Codes. – In: Proc. of 14th Annual Workshop on HPEC, Lexington, Massachusetts, USA, 2010.Search in Google Scholar

6. CUDA C Programming Guide. https://docs.nvidia.com/cuda/cuda-c-programming-guide/Search in Google Scholar

7. CUDA Homepage. http://www.nvidia.com/object/cudahomenew.htmlSearch in Google Scholar

8. Demouth, J. Kepler’s Shuffle: Tips and Tricks. – GPU Technology Conference, 2013. http://on-demand.gputechconf.com/gtc/2013/presentations/S3174-Kepler-Shuffle-Tips-Tricks.pdfSearch in Google Scholar

9. Good, I. J. The Interaction Algorithm and Practical Fourier Analysis. – Journal of the Royal Statistical Society, Vol. 20, 1958, No 2, pp. 361-372.10.1111/j.2517-6161.1958.tb00300.xSearch in Google Scholar

10. Joux, A. Algorithmic Cryptanalysis. Chapman & Hall/CRC Cryptography and Network Security Series, 2009.Search in Google Scholar

11. Karpovsky, M. G., R. S. Stankovic, J. T. Astola. Spectral Logic and Its Applications for the Design of Digital Devices. Wiley, 2008.10.1002/9780470289228Search in Google Scholar

12. Kirk, D. B., We n-me i W. Hw u. Programming Massively Parallel Processors: A Hands-on Approach. Elsevier, 2013.Search in Google Scholar

13. Kurzak, J., D. A. Bader, J. Dongarra. Scientific Computing with Multicore and Accelerators. CRC Press, 2010.10.1201/b10376Search in Google Scholar

14. Lindholm, E., J. Nickolls, S. Oberman, J. Montrym. NVIDIA Tesla: A Unied Graphics and Computing Architecture. – IEEE Micro, Vol. 28, 2008, Issue 2.10.1109/MM.2008.31Search in Google Scholar

15. Lobeiras, J., M. Amor, R. Doallo. BPLG: A Tuned Buttery Processing Library for GPU Architectures. – International Journal of Parallel Programming, Vol. 43, 2015, No 6, pp. 1078-1102.10.1007/s10766-014-0323-8Search in Google Scholar

16. Maciol, P., K. Banas. Testing Tesla Architecture for Scientific Computing: The Performance of Matrix-Vector Product. – In: Computer Science and Information Technology, IMCSIT 2008, pp. 285-291.10.1109/IMCSIT.2008.4747253Search in Google Scholar

17. MATLAB Platform for Solving Engineering and Scientific Problems. https://www.mathworks.com/products/matlab/Search in Google Scholar

18. NVIDIA GeForce GT 740M Specification. http://www.geforce.com/hardware/notebook-gpus/geforce-gt-740mSearch in Google Scholar

19. NVIDIA GeForce GTX TITAN Specification. http://http://www.geforce.com/hardware/desktop-gpus/geforce-gtx-titan/specificationsSearch in Google Scholar

20. NVIDIA: CUDA cuFFT Library. http://docs.nvidia.com/cuda/cufft/Search in Google Scholar

21. Owens, J.D., M. Houston, D. Luebke, S. Green, J. E. Stone, J. C. Phillips. GPU Computing. – Proc. of IEEE, Vol. 96, 2008, No 5, pp. 879-899.10.1109/JPROC.2008.917757Open DOISearch in Google Scholar

22. Picek, S., L. Batina, D. Jakobovic, B. Ege, M. Golub. S-Box, SET, Match: A Toolbox for S-Box Analysis. – In: Information Security Theory and Practice. Securing the Internet of Things, Lecture Notes in Computer Science, Vol. 8501, 2014, pp. 140-149.10.1007/978-3-662-43826-8_10Search in Google Scholar

23. Sage Mathematics Software. http://www.sagemath.org/Search in Google Scholar

24. Shucai, Xiao, Wu-chun Feng. Inter-Block GPU Communication via Fast Barrier Synchronization. – In: IEEE International Symposium on Parallel & Distributed Processing (IPDPS’10), 2010.10.1109/IPDPS.2010.5470477Search in Google Scholar