CS 7542
Life at Scale: Parallel and High-Performance Computing for Genomics
Topics include scalable algorithms for sequence alignment, genome assembly, pangenomics, population genetics, and more, with an emphasis on irregular computation, graph analytics, sparse methods, and hardware acceleration.
Topics
- Characterize the computational structure of core genomics algorithms and identify opportunities for parallelism.
- Formulate genomics problems using sparse linear algebra and graph-analytic primitives.
- Analyze sources of irregular computation and evaluate mitigation strategies at the algorithmic and systems levels.
- Evaluate hardware acceleration trade-offs, considering programmability and portability.
- Interpret scaling experiments and critically assess performance claims in the literature.
- Synthesize knowledge of HPC systems, algorithms, and genomics in written critiques and discussions.
Official roster learning outcomes
Prerequisites & course information
CS 3410.
Sources
- Course catalogue ↗2026-2027