Home // Parallelisation of a Hierarchical Volume Data Preprocessor
Type of thesis: Bachelorarbeit / location: Dresden / Status of thesis: Finished theses
Visualization of large three-dimensional data sets on consumer hardware requires pre-processing into hierarchical volume image data to reduce required data transfers onto GPUs, making the rendering process more efficient. This processing is typically an I/O-bound problem and can thus benefit from high bandwidth storage found in modern High Performance Computing (HPC) systems.
In this Bachelor thesis, a serial application for generating hierarchical volume image data will be extended to exploit multiple levels of parallelism on HPC hardware. In order to use the aggregated I/O bandwidth of multiple nodes, multi-process parallelism needs to be added to the application, e.g., using MPI. Additionally, thread-based parallelism will be required to better exploit the resources of a single compute node.
For this work, basic knowledge of parallel programming in C/C++ will be required.
The language can be either German or English.
Service and Transfer Center
Performance analysis/estimation of Big data applications, Big data frameworks on HPC
ScaDS.AI Dresden/Leipzig (Center for Scalable Data Analytics and Artificial Intelligence) is a center for Data Science, Artificial Intelligence and Big Data with locations in Dresden and Leipzig.
Chemnitzer Str. 46b,
Copyright 2021 © ScaDS.AI Dresden/Leipzig – All rights reserved.