Course Review for Final Exam

• SSE/AVX/SIMD instructions: basic idea, how to convert loops (unwrap + cleanup), how to convert branches (SIMD bitwise branch trick), general performance limits (number of SIMD units) and pitfalls (branch heavy or divergent code).
  
• OpenMP/pthreads multithreaded shared memory: basic idea, how to convert loops (divide up loop iterations), performance limits (core count and memory bus), and pitfalls (race conditions, false sharing).
• sockets/MPI distributed memory message passing: basic idea, how to write an application (divide up work, pass messages), performance limits (core count, network speed), and pitfalls (waiting for messages that will never arrive).
• GPU programming: basic idea, how to write an application (call a kernel, move memory around), performance limits (SIMD*cores*pipeline depth), and pitfalls (sequential code).

• Programming model: machines must be easy to program, or nobody will use the fancy parallel stuff.
  
• Load balance: keep all the parallel hardware busy doing useful work, or performance suffers.  Amdahl's Law.
  
• Data movement: balance accesses between network, RAM, coherent caches, local caches, and registers.  Race conditions.