PAR Class 27, Mon 2019-04-22
Table of contents::
1 Final presentations
- Mon
- SP
- AJ
- ER
- ZJ
- Thu
- AG
- JG
- SM
- ZC
- HH
- ZL
- HB
- BM
- KS
- JC
2 Term project
- Details are in the syllabus, except that your talk is 10 minutes, not 15, and you're not required to demo it.
- You may base your paper on my 2018 Fall Workshop on Computational Geometry paper here. Or, you may use any other, equally formal, style.
- Please hand everything in by the end of the weekend. Putting everything on parallel might be easiest, in a dir named project. Then email me.
3 Course recap
- My teaching style is to work from particulars to the general.
- You've seen OpenMP, a tool for shared memory parallelism.
- You've seen the architecture of NVidia's GPU, a widely used parallel system, and CUDA, a tool for programming it.
- You've seen Thrust, a tool on top of CUDA, built in the C++ STL style.
- You've seen quantum computing, both the theory and programming IBM's quantum computer.
- You've seen how widely used numerical tools like BLAS and FFT have versions built on CUDA.
- You've had a chance to program in all of them on parallel.ecse, with dual 14-core Xeons, Pascal NVidia board
- You seen talks by leaders in high performance computing, such as Jack Dongarra.
- You've seen quick references to parallel programming using Matlab, Mathematica, and the cloud.
- Now, you can inductively reason towards general design rules for shared and non-shared parallel computers, and for the SW tools to exploit them.
4 Course survey
If you liked the course, then please officially tell RPI by completing the survey. Thanks.