link_collection.rst

Links

The following is a link collection that contains different links that were useful for this project.

• Timing tools:

  http://en.wikipedia.org/wiki/Time\_(Unix)

• Profiling tools:

  http://software.intel.com/en-us/intel-parallel-studio-xe

  http://en.wikipedia.org/wiki/LTTng

  http://code.google.com/p/gperftools/

  https://wiki.engr.illinois.edu/download/attachments/114688007/amplifier\_xe\_linux.pdf

• Tutorials:

  http://www.ibm.com/developerworks/library/l-gnuprof.html

  http://en.wikipedia.org/wiki/Performance\_analysis

  http://en.wikipedia.org/wiki/Profiling\_(computer\_programming)

  http://nbviewer.ipython.org/urls/raw.github.com/jrjohansson/scientific-python-lectures/master/Lecture-6B-HPC.ipynb

  http://www.softeng.rl.ac.uk/media/uploads/publications/2010/07/ProfilingTutorial.pdf

• Articles:

  http://www.gotw.ca/publications/concurrency-ddj.htm

  http://www.extremetech.com/computing/116561-the-death-of-cpu-scaling-from-one-core-to-many-and-why-were-still-stuck/

  http://web.mit.edu/newsoffice/topic/the-multicore-future.html

  http://www.karlrupp.net/2013/06/cpu-gpu-and-mic-hardware-characteristics-over-time/

• Fitting:

  http://jarrodmillman.com/scipy2011/pdfs/brefsdal.pdf

  http://adsabs.harvard.edu/abs/1979ApJ...228..939C

• General links:

  http://www.debugging-guide.com/

  http://spiff.rit.edu/classes/phys445/lectures/radec/radec.html

• Google Performance Tools:

  http://code.google.com/p/gperftools/?redir=1

• Simple Python Profiling tutorial:

  http://nematodes.org/martin/teaching/programming-topics/profiling-python-for-cpu-and-memory-usage/

• Online courses

  http://www-users.york.ac.uk/~mijp1/teaching/4th\_year\_HPC/notes.shtml

  http://www.citutor.org

• Books:

  Quinn, "Parallel Programming in C with MPI and OpenMP", Amazon

  Grötker, "Developer's Guide to debugging", Amazon