Paralleles Rechnen Seminar
Instructors: Univ-Prof. Dr. Bertil SchmidtShortname: 08.079.590
Course No.: 08.079.590
Course Type: Seminar
Requirements / organisational issues
Prerequisites:Successful partitication in PAA or HPC.
Contents
Topic Selection:E-Mail your two preferred topics to Prof. Dr. Bertil Schmidt by 12th July 2019 latest.
I will then assign a topic to you.
Note: There is also the otion of suggesting a topic by yourself (needs my approval)
Topics:
- Accelerating Reduction and Scan Using Tensor Core Units
- diBELLA: Distributed Long Read to Long Read Alignment
- Performance extraction and suitability analysis of multi- and many-core architectures for next generation sequencing secondary analysis
- Adaptive Sparse Matrix-Matrix Multiplication on the GPU
- GPU-Accelerated Large-Scale Genome Assembly
- Hardware-conscious Hash-Joins on GPUs
- Harnessing GPU tensor cores for fast FP16 arithmetic to speed up mixed-precision iterative refinement solvers
- GRASShopPER—An algorithm for de novo assembly based on GPU alignments
- Exascale Deep Learning for Climate Analytics
- Efficient Architecture-Aware Acceleration of BWA-MEM for Multicore Systems
- A Modular Benchmarking Infrastructure for High-Performance and Reproducible Deep Learning
- UPC++: A High-Performance Communication Framework for Asynchronous Computation
- Large-Scale Hierarchical k-means for Heterogeneous Many-Core Supercomputers
- A Framework for the Automatic Vectorization of Parallel Sort on x86-based Processors
- self-suggested topic (needs approval)