Algorithms and Data Structures for HPC
- Department of Computer Systems
The most computation intensive tasks (or memory complex tasks) are solved by large HPC systems.
Seven so called „dwarfs“ were identified as typical tasks computed by HPC systems.
In this subject these „dwarfs“ are described (including their variants).
Also typical algortihms and advanced data structures for solution will be discussed.
Baic knowledge of principles of parallel systems
- Syllabus of lectures:
1) Overview of nowadays technologies I (OpenMP and MPI)
2) Overview of nowadays technologies II (OpenACC and CUDA)
3) Single-node optimization
4) Dense linear algebra: data stuctures, operations and optimizations
5) Sparse linear algebra: data stuctures, operations and optimizations
6) 7 dwarfs: Spectral methods, N-body methods, Monte Carlo methods
7) 7 dwarfs: Structured and unstructured grids
8) More dwarfs: Graph Traversal, etc.
9) HPC debugging and profiling tools
10) Performance limits given by architecture
11) Survey of scientific libraries for HPC
12) HPC benchmarks: High Performance LINPACK vs HPCG
- Syllabus of tutorials:
- Study Objective:
- Study materials:
Krste Asanovic, Ras Bodik, Bryan Christopher Catanzaro,Joseph James Gebis, Parry Husbands, Kurt Keutzer, David A. Patterson, William Lester Plishker, John Shalf,Samuel Webb Williams, Katherine A. Yelick The Landscape of Parallel Computing Research: A View from Berkeley
Prof. Dr. Michael Bader: HPC - Algorithms and Applications
- Further information:
- No time-table has been prepared for this course
- The course is a part of the following study plans: