High-Performance Computing

The research and teaching in High-Performance Computing focuses on different aspects of optimizing (or “tuning”) existing applications or applications under development, such that results can be obtained faster. Those applications are typically large-scale simulations, that can run for days and weeks, and that consume lots of computational resources, e.g. memory, CPU cycles, disk space, etc. There are several different approaches to “speed-up” calculations:

Serial tuning

This area focuses on finding the bottlenecks in sequential algorithms and applications, and to implement solutions that are optimized for modern cache-based CPU architectures.

Parallelization

This work involves both the design and implementation of new parallel algorithms, as well as to parallelize existing implementations of algorithms efficiently. The major part of work in this area focuses on the development of parallel codes for multi-core (CPU) and many-core (GPU, accelerators) types of architectures, using e.g. OpenMP, OpenCL or CUDA.

Run-time optimization

Large applications can very often benefit from changing or optimizing the runtime environment, e.g. access to local disks, adapting the memory page sizes, replacing of runtime-libraries, etc. This can be a good way to improve performance for applications that are not available in source code.