CS198 y 199 (Special problems I and II) AY 2015 to 2016

Under F. Cabarle

AY 2015-2016 s1 and s2

• • Introductory slides uploaded, i.e. cs198-199-ago-2015.pdf.

  • For your references (bibliography), I recommend using BibTeX for ease of typsetting, modularity, and flexibility later on.

  • Set 1 readings:

• 1. ACM magazine Communications of the ACM (2008) article ``The many facets of natural computing'' by Lila Kari and Gzegorz Rozenberg

     1. ``From Cells to (Silicon) Computers, and Back'' book chapter, by Gheorghe Paun, in New Computational Paradigms (2008)

  2. ``Membrane Computing'' by Gheorghe Paun (chapter 40) in the Handbook of Natural Computing (2009)

  3. Spiking Neural P Systems - A Tutorial by Gheorghe Paun

• • Set 2 readings:

    1. Asynchronous SNP systems for sorting, by S. Aoki and A. Fujiwara, 3rd International Conference on Networking and Computing, IEEE (2012)

    2. Matrix representation of spiking neural P systems, by X. Zeng et al, CMC2010, LNCS vol 6501, pp. 377-391 (2010)

    3. Implementing sorting networks with spiking neural P systems, by R. Ceterchi and A. Tomescu, Fundamenta Informaticae, vol 87(1), pp. 35-48 (2008)

    4. Some applications of spiking neural P systems, by M. Ionescu and D. Sburlan, Computing and Informatics, Vol. 27, pp. 515-528 (2008)

  • Set 3 readings:

    1. A P-Lingua based simulator for spiking neural P systems, by L.F. Macias-Ramos et al, CMC2011, LNCS vol 7184, pp. 257-281 (2012)

    2. Improving GPU simulations of spiking neural P systems, F.G.C. Cabarle et al, ROMJIST, vol 15(1), pp. 5-20 (2012)

    3. Parallel simulation of PDP systems: updates and roadmap, by M.A. Martinez-del-Amor et al, 13th Brainstorming Week on Membrane Computing (to appear), Seville, Spain (2015)

    4. Simulating P systems on GPU: a survey, by M.A. Martinez-del-Amor et al, Fundamante Informaticae, vol 136(3), pp. 269-284 (2015)

  • Set 4 readings:

    1. A parallel algorithm for skeletonizing images by using spiking neural P systems, Daniel Díaz-Pernil, Francisco Peña-Cantillana, Miguel A. Gutiérrez-Naranjo, Neurocomputing 115, pp. 81-91 (2013)

    2. Simulating a P system based efficient solution to SAT by using GPUs, J.M. Cecilia et al, J. Logic and Algebraic Programming vol 79(6), pp. 317-325 (2010)

    3. The GPU on the simulation of cellular computing models, J.M. Cecilia et al. Soft Computing vol 16(2), pp. 231-246 (2012)

    4. Characterizing the parallel simulation of P systems on the GPU, by M.A. Martinez-del-Amor et al, International Journal of Unconventional Computing, vol 9(5-6) pp. 405-424 (2013)

  • Since we will do massively parallel programming with GPUs, some resources:

• • • My CS 239 (parallel programming) class page is here.

    • Check good and easy to follow videos from Udacity in Youtube, regarding theory and implementation of parallel programming. Since we will implement (parallel) sorting networks later on, a good starting video is here.

• • Set 5 readings:

• • • A Performance Comparison of CUDA and OpenCL. Kamran Karimi, Neil G. Dickson, Firas Hamze. http://arxiv.org/pdf/1005.2581.pdf

    • A Comprehensive Performance Comparison of CUDA and OpenCL. Jianbin Fang, Varbanescu, A.L., Sips, H. DOI:10.1109/ICPP.2011.45

    • Overview and comparison of OpenCL and CUDA technology for GPGPU. Ching-Lung Su, Po-Yu Chen, Chun-Chieh Lan, Long-Sheng Huang, Kuo-Hsuan Wu. DOI:10.1109/APCCAS.2012.6419068

• • Set 6:

  • A uniform solution to integer factorization using time-free spiking neural P system, Xiangrong Liu, Ziming Li, Juan Suo, Juan Liu, Xiaoping Min. Neural Computing and Applications July 2015, Volume 26, Issue 5, pp 1241-1247

  • If you point out (a) mistake(s) in my slides, please email me at fccabarle at dot up dot edu dot ph. You get credit (also my thanks, among other things) for pointing out the mistake(s) correctly!