Università Roma Tre

Short introduction to Julia for scientific programming. Introduction to parallel aechitectures. Designi principles of parallel algorithms. Prallel and distributed programming with Julia. Comunication and sincronization primitives: MPI paradigm. Directive-based languages: OpenMP. Performance metrics of parallel programs. Matrix operations and dense linear systems: mentions to BLAS, LAPACK, scaLAPACK. Sparse linear systems. Mentions to CombBLAS and GraphBLAS.

1. [Lecture slides and diary](https://github.com/cvdlab-courses/pdc/blob/master/schedule.md)

2. [Learning Julia](https://www.manning.com/books/julia-in-action)

3. Blaise N. Barney, [HPC Training Materials](https://computing.llnl.gov/tutorials/parallel_comp/), per gentile concessione del Lawrence Livermore National Laboratory's Computational Training Center

4. J. Dongarra, J. Kurzak, J. Demmel, M. Heroux, [Linear Algebra Libraries for High- Performance Computing: Scientific Computing with Multicore and Accelerators](http://www.netlib.org/utk/people/JackDongarra/SLIDES/sc2011-tutorial.pdf), SuperComputing 2011 (SC11)