Approaches to high-performance compute
- Scale-up and scale-out
- Concurrency and parallelism
- Acceleration, optimisation, and re-organisation
- Measuring and evaluating performance
- Managing latency vs throughput
- Data-placement and access
- Design patterns for high-performance compute
- Environmental impact and ethics of using large-scale compute
Specific systems and tools:
- MPI
- Hadoop
- Data pipelines
Real-world case studies:
- Distributed learning
- Physics problems
Learning and Teaching
Teaching and learning methods
Lectures, problem classes, and self-driven lab exercises
