INF9380 – High Performance Computing in Bioinformatics
Changes in the course due to coronavirus
Autumn 2020 we plan for teaching and examinations to be conducted as described in the course description and on semester pages. However, changes may occur due to the corona situation. You will receive notifications about any changes at the semester page and/or in Canvas.
Spring 2020: Teaching and examinations was digitilized. See changes and common guidelines for exams at the MN faculty spring 2020.
This course focuses on the application of high performance computing (HPC) to bioinformatics analysis. The main target is to provide a background on how to effectively use HPC clusters for running computationally or data intensive bioinformatics applications. The course will mainly include teaching students selected bioinformatics tools and workflows, and how to use HPC platforms to speed up and maximize the overall throughput of intensive bioinformatics analysis. This would include, e.g. how to optimize the use of available compute nodes, and how to adapt the application to the available resources on each compute node.The course will cover both how to efficiently use parallelism when writing your own programs, as well as how to adapt and wrap existing tools in manner that efficiently exploits resources available on parallel architectures.
After finishing the course the students should know:
- Resource intensive bioinformatics tools for, e.g. assembly, mapping/alignment, and multiple alignment. This would include the use of commandline tools and portal based tools, e.g. Galaxy.
- How those tools work, how this would influence the runtime, and the possibility of parallelizing the computation.
- When to use parallelization and distribution.
- The basic structure of HPC clusters, and how to run jobs on a cluster
- How to evaluate the use of resources on a cluster, and how to optimize the use of memory and CPUs
- How to write your own tools that works efficiently on parallel hardware
- How to adapt or write wrappers around existing tools to process large datasets efficiently using parallellisation
Admission to the course
A maximum of 30 PhD-student can get admission to this course. The course is mainly for PhD-students in the NORBIS program. It may be possible to take the course for others if it is capacity.
Recommended previous knowledge
Basic unix competence and basic knowledge of bioinformatics applications is required. Basic programming skills, preferably in Python.
- 5 credits overlap with INF5380 – High Performance Computing in Bioinformatics.
This as an intensive two weeks course with lectures and hands on exercises 7 hours a day, Monday to Friday. About half the time with lectures and the other half with exercises. In total lectures and exercises are estimated to 70 hours. Selfstudy/reading of curriculum is estimated to 30 hours. Preparation time for written report (home exam) is estimated to 40 hours. The total workload for the course is estimated to 140 hours.
Practical student project (home exam) with hand-in of written report.
Grades are awarded on a pass/fail scale. Read more about the grading system.