Memory Consistency Models: Performance Impact and Parallel Computing

This lecture discusses memory consistency models and their impact on performance when running custom binaries on different processors. It also covers the differences between Instruction-Level Parallelism (ILP) and Thread-Level Parallelism (TLP), as well as the implementation of atomic subroutines like Compare-And-Swap (CAS) in processors. Furthermore, it explores Transactional Memory properties, GPU architecture, shared memory usage, CUDA kernel execution steps, and GPU memory access patterns. The lecture concludes with a comparison of multithreaded workloads and the suitability of different multithreading granularities for processors.