This course offers hands-on training from 0 to 100, i.e., from the beginning of parallel programming up to the high end needed for efficient parallelization on current clusters of shared memory and ccNUMA nodes.

Distributed memory parallelization with the Message Passing Interface MPI (Mon, for beginners):
On clusters and distributed memory architectures, parallel programming with the Message Passing Interface (MPI) is the dominating programming model. The course gives an introduction into MPI-1. Hands-on sessions (in C and Fortran) will allow users to immediately test and understand the basic constructs of the Message Passing Interface (MPI).

Shared memory parallelization with OpenMP (Tue, for beginners):
The focus is on shared memory parallelization with OpenMP, the key concept on hyper-threading, dual-core, multi-core, shared memory, and ccNUMA platforms. This course teaches shared memory OpenMP parallelization. Hands-on sessions (in C and Fortran) will allow users to immediately test and understand the directives and other interfaces of OpenMP. Race-condition debugging tools are also presented.

Intermediate and advanced topics in parallel programming (Wed-Fri):
Topics are advanced usage of communicators and virtual topologies, one-sided communication, derived datatypes,  MPI-2 parallel file I/O, hybrid mixed model MPI+OpenMP parallelization, parallelization of explicit and implicit solvers and of particle based applications, parallel numerics and libraries, and parallelization with PETSc. MPI-3.0 introduced a new shared memory programming interface, which can be combined with MPI message passing and remote memory access on the cluster interconnect. It can be used for direct neighbor accesses similar to OpenMP or for direct halo copies, and enables new hybrid programming models. These models are compared in the hybrid mixed model MPI+OpenMP parallelization session with various hybrid MPI+OpenMP approaches and pure MPI. Further aspects are domain decomposition, load balancing, and debugging.