Slides.

1. Computer anatomy
   • CPU, RAM, HDD
   • Desktop vs. cluster
2. Why do programs run slowly?
   • Hardware bottlenecks
   • Algorithmic complexity
3. How do you know why a program is slow?
   • System utilities: time, top, and profiling tools
   • Code/algorithm analysis
4. Parallelizeable problems
   • Monte Carlo
   • split-apply-combine
   • Amdahl’s Law
   • Race conditions
5. HPC environments
   • multithreading
   • clusters/supercomputers
   • cloud/grid computing
   • scheduling jobs
6. Relevant technologies
   • Shared vs. distributed memory
   • OpenMP, MPI, other language-specific libraries
   • Hadoop, MapReduce
7. GPUs and related terminology
   • Single precision computation
   • Double precision computation
   • Memory management in GPU
   • Languages for GPUs - CUDA-C, Python, …