Available courses

This course covers the basics of high performance computing (HPC). This includes an introduction to processor and HPC system architectures, optimization and performance modeling, and parallel programming on shared and distributed memory parallel computers. Necessary prerequisites are a working knowledge about UNIX/Linux environments and at least one programming language out of the set {C, C++, Fortran}. We do not have the time to teach UNIX or programming basics in this course.



This course, a collaboration of the Erlangen National High Performance Computing Center (NHR@FAU) and the Leibniz Supercomputing Center (LRZ), is targeted at students and scientists with interest in programming modern HPC hardware, specifically the large scale parallel computing systems available in Jülich, Stuttgart and Munich but also smaller clusters.

This course teaches performance engineering approaches on the compute node level. "Performance engineering" as we define it is more than employing tools to identify hotspots and bottlenecks. It is about developing a thorough understanding of the interactions between software and hardware. This process must start at the core, socket, and node level, where the code gets executed that does the actual computational work. Once the architectural requirements of a code are understood and correlated with performance measurements, the potential benefit of optimizations can often be predicted. We introduce a "holistic" node-level performance engineering strategy and apply it to different algorithms from computational science.

Most HPC systems are clusters of shared memory nodes. To use such systems efficiently both memory consumption and communication time has to be optimized. Therefore, hybrid programming may combine the distributed memory parallelization on the node interconnect (e.g., with MPI) with the shared memory parallelization inside of each node (e.g., with OpenMP or MPI-3.0 shared memory). This course analyzes the strengths and weaknesses of several parallel programming models on clusters of SMP nodes. Multi-socket-multi-core systems in highly parallel environments are given special consideration. MPI-3.0 has introduced a new shared memory programming interface, which can be combined with inter-node MPI communication. 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 with various hybrid MPI+OpenMP approaches and pure MPI. Numerous case studies and micro-benchmarks demonstrate the performance-related aspects of hybrid programming.

Hands-on sessions are included on both days. Tools for hybrid programming such as thread/process placement support and performance analysis are presented in a "how-to" section. This course provides scientific training in Computational Science, and in addition, the scientific exchange of the participants among themselves. This course is organized by VSC (Vienna Scientific Cluster) in cooperation with HLRS and RRZE.


This is the material (slides, example code) for the NHR@FAU hackathon organized for OOKAMI users.

Lecturers

Location: The seminar will be conducted hybrid (in person and online)

Room: 11501.01.019 (01.019 Seminarraum) Elektrotechnik - Cauerstraße 7-9, 1. OG

Zoom Link: TBA

Time: Tuesday 14:15 - 15:45, First lecture: October 18th, 2022 (also see Campo)

Credits: 5 ECTS credits. This requires two talks and a written seminar report.

Possible topics can be found in the intro talk (see below).

Tutors:



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