Parallel computing
Undergraduate · CS / Programming
Syllabus focus
Standard syllabus · STEM / applied
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$60.00 · 60 min · Undergraduate · Online ($60/hr)
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Topics typically covered
Standard syllabus
Parallel models
- Flynn's taxonomy; shared vs distributed memory
- Amdahl's and Gustafson's laws
- Threads, locks, and race conditions
- Synchronization primitives and lock-free ideas (intro)
- Parallel algorithm design: divide-and-conquer, data parallelism
Programming interfaces
- Pthreads or std::thread programming (intro)
- OpenMP directives for loop parallelism
- MPI basics: send, receive, collective ops (intro)
- GPU computing with CUDA or OpenCL (survey)
- Deterministic debugging of concurrent programs
STEM / applied
Performance engineering
- Cache coherence and false sharing
- Scheduling and work-stealing (intro)
- Benchmarking parallel speedup and efficiency
- Domain decomposition for PDEs/grids (intro)
- Pipeline parallelism in data processing frameworks
Applications
- Parallel sorting and graph algorithms (intro)
- MapReduce/Hadoop/Spark programming model (survey)
- Scientific computing workloads on clusters
- Fault tolerance in distributed jobs (intro)
- Ethics and energy costs of large-scale compute
Notes
Mix of theory and programming varies; some courses emphasize HPC, others data-parallel frameworks.