HiPerCH 12

HiPerCH 12

High Performance Computing in Hessen (HiPerCH)

The 12th installment of the HiPerCH workshop series will be held online. It advances HKHLR's previous lectures on Deep Learning on high performance computing clusters. In addition, we hold a course on Research Software Engineering in cooperation with DLR, and one course on CUDA programming in cooperation with NAG.

The HiPerCH workshops are conducted by the Competence Center for High Performance Computing in Hessen (HKHLR). They are primarily targeted at students and scientists from Hessen, Mainz, and Kaiserslautern with interest in programming modern HPC hardware.

This year, due to the Corona pandemic, we announce HiPerCH 12 nationwide in Germany.

The modules can be booked separately. Please note that there is a limited number of participants for each module. The modules are free for all students and members of universities and academic institutions.

All talks and lectures will be held in English.

Update August 24: Module 1 is fully booked! Further registrations will be placed on the waiting list!

Update August 24: Module 2 has been postponed to be held on Friday, September 25, 2020.

Update August 31: Module 2 is fully booked! Further registrations will be placed on the waiting list!

Update September 21: The registration is now closed.

High Performance Computing in Hessen (HiPerCH)

Module 1: Deep Learning on HPC Systems

See: Module 1 detailed information

Date: Monday, September 21

Module 2: Introduction to Research Software Engineering

See: Module 2 detailed information

Date: Friday, September 25 (Attention! This module was scheduled originally for Tuesday, September 22.)

Module 3: Introduction to CUDA Programming

See: Module 3 detailed information

Date: Wednesday, September 23, and Thursday, September 24


Event Registration Remark

For module 3 (Introduction to CUDA Programming): Part of the registration data will be shared with NAG and NAG may use the data of participants after the workshop to get feedback and to communicate relative information.