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Accelerating Genome Analysis with FPGAs, GPUs, and New Execution Paradigms

Course Description

A genome encodes a set of instructions for performing some functions within our cells. Analyzing our genomes helps, for example, to determine differences in these instructions (known as genetic variations) from human to human that may cause diseases or different traits. One benefit of knowing the genetic variations is better understanding and diagnosis of diseases and the development of efficient drugs.

Computers are widely used to perform genome analysis using dedicated algorithms and data structures. However, timely analysis of genomic data remains a daunting challenge, due to the complex algorithms and large datasets used for the analysis. Increasing the number of processing cores used for genome analysis decreases the overall analysis time, but significantly escalates the cost of building, maintaining, and cooling such a computing cluster, as well as the power/energy consumed by the cluster. This is a critical shortcoming with respect to both energy production and environmental friendliness. Cloud computing platforms can be used as an alternative to distribute the workload, but transferring the data between the clinic and the cloud poses new privacy and legal concerns.

In this course, we will cover the basics of genome analysis to understand the computational steps of the entire pipeline and find the computational bottlenecks. Students will learn about the existing efforts for accelerating one or more of these steps and will have the chance to carry out a hands-on project to improve these efforts.

Prerequisites of the course:

  • No prior knowledge in bioinformatics or genome analysis is required.
  • Digital Design and Computer Architecture (or equivalent course)
  • A good knowledge in C programming language is required.
  • Experience in at least one of the following is highly desirable: FPGA implementation and GPU programming.
  • Interest in making things efficient and solving problems

The course is conducted in English.

Course description page


Name E-mail Office
Lead Supervisor Mohammed Alser CAB F 72
Supervisor Jeremie Kim CAB E 78
Supervisor Can Firtina CAB F 74.2
Supervisor Juan Gómez Luna CAB D 77
Supervisor Damla Senol Cali

2020 Meetings/Schedule

Week Date Meeting Learning Materials Assignments
W1 29.09
M1: P&S Accelerating Genomics Course Presentation
Required Materials
Recommended Materials

Learning Materials

Meeting 1: Required Materials

Meeting 1: Recommended Materials

More Learning Materials

bioinformatics.txt · Last modified: 2021/02/05 09:39 (external edit)