Introduction to parallel programming, Parallel hardware and parallel software: Classifications of parallel computers, SIMD systems, MIMD systems, Interconnection networks, Cache coherence, Shared-memory vs. distributed-memory, Coordinating the processes/threads, Shared-memory, Distributed-memory.

GPU programming, Programming hybrid systems, MIMD systems, GPUs, Performance: Speedup and efficiency in MIMD systems, Amdahl’s law, Scalability in MIMD systems, Taking timings of MIMD programs, GPU performance. lab Parallel computing

Distributed memory_programming with MPI: MPI functions, The trapezoidal rule in MPI, Dealing with I/O, Collective communication, MPI-derived datatypes, Performance evaluation of MPI programs, A parallel sorting algorithm.

Shared-memory_programming with OpenMP: openmp pragmas and directives, The trapezoidal rule, Scope of variables, The reduction clause, loop carried dependency, scheduling, producers and consumers, Caches, cache coherence and false sharing in openmp, tasking, tasking, thread safety. Deep learning lab

GPU programming with CUDA: GPUs and GPGPU, GPU architectures, Heterogeneous computing, Threads, blocks, and grids Nvidia compute capabilities and device architectures, Vector addition, Returning results from CUDA kernels, CUDA trapezoidal rule I, CUDA trapezoidal rule II: improving performance, CUDA trapezoidal rule III: blocks with more than one warp.