Optimization of Scaling and Node-level Performance on Hawk

By means of this workshop, you can tweak compiler flags, environment settings, etc. in order to make your code run faster. According to our experience gathered in prior workshops, these “low-hanging fruits” can give you a significant speedup but require only little effort.

Furthermore, you will analyze the runtime behavior of your code, locate bottlenecks, design and discuss potential solutions as well as implement them. All categories of bottlenecks (CPU, memory subsystem, communication and I/O) will be addressed, according to the respective requirements. Ideally, the above steps will be repeated multiple times in order to address several bottlenecks.

Every attending group will be supported during the entire workshop by a dedicated member of the HLRS user support group. In addition, HPE and AMD specialists will be available to help with issues specific to e.g. MPI and the processor.

Due to the Covid-19 pandemic, we decided to provide this workshop in an online fashion. In order to do so, we will setup breakout rooms in a Zoom session which enable participants to communicate as well as share screens and remote control applications with support staff, hence providing the same options of interaction as meeting in person.

Schedule

(it's possible to book individual days only)

9:00 - 17:30 Workshop daily

Prerequisites

In order to attend the workshop, you should already have an account on Hawk and your application should already be running on the system. Furthermore, we require that you bring your own code including a test case which is set up according to the following rules:

• use case selection:
  • When processing the test case, your code should have a behavior and profile which is close to that of current and maybe future production runs.
  • If possible, the test case should be representative for those production runs of your group which consume the largest part of your compute time budget.
• number of cores:
  • In order to be representative, the test case should be in size comparable to the respective current and maybe future production runs.
  • In order to save valuable resources and to allow for a productive workflow, it should however be as small as possible.
  • So take into account to reduce the size of your test case as long as the profile stays almost constant. This can often be achieved by reducing the simulated domain and keep the computational load per core constant ("weak down scaling").
• wall time:
  • In order to allow for a productive workflow, the wall time should be a few minutes only.
  • At the same time, it should cover all important parts of the code, i.e. computation, communication and I/O.
  • So take into account to reduce the number of simulated time steps.

Content levels

• Intermediate: 13 hours 30 minutes
• Advanced: 13 hours 30 minutes

Learn more about course curricula and content levels.

German (in English, if required)

HLRS, HPE and AMD user support staff

Handouts will be available to participants in terms of PDFs.

Registration is closed.

Fee

Members of German universities and public research institutes: none
Members of universities and public research institutes within EU or PRACE-member countries: none
Members of other universities and public research institutes: 120 EUR
Others: 400 EUR
Our course fee includes coffee breaks (classroom courses only)

HLRS is part of the Gauss Centre for Supercomputing (GCS), which is one of the six PRACE Advanced Training Centres (PATCs) that started in Feb. 2012.
HLRS is also member of the Baden-Württemberg initiative bwHPC-C5.
This course is provided within the framework of the bwHPC-C5 user Support. This course is not part of the PATC curriculum and is not sponsored by the PATC program.

Björn Dick phone 0711 - 685 87189, dick(at)hlrs.de
Tobias Haas phone 0711 685 87223, tobias.haas(at)hlrs.de
Khatuna Kakhiani phone 0711 685 65796, kakhiani(at)hlrs.de
Lorenzo Zanon phone 0711 685 63824, zanon(at)hlrs.de

http://www.hlrs.de/training/2021/HPE1

https://www.hlrs.de/training/ and https://www.hlrs.de/training/overview/