Topics covered:
1. Introduction to parallel and distributed computing <lect1>
2. Parallel and distributed architectures <lect2>
3. Parallel algorithms <loop transform>
4. Performance modeling <performance ¶llel algo>
5. Cluster computing with MPI
6. Big data platform: Hadoop, MapReduce programming, Hadoop Ecosystems
7. Multicore programming with OpenMP
8. GPUs programming framework: CUDA, and OpenACC
9. Tuning Parallel and GPU programs
10. Machine learning tools with GPUs/CUDA
11. HPC clouds with dockers (NVIDIA dockers)/ NVIDIA clouds
12. Deep learning acceleration with GPUs ( Caffe,PyTorch, Tensorflow)
and Distributed computing <slide> <youtube1> <youtube2>
pytorch data parallel <video> <github>
tensorflow distributed <video>
-- Machine learning tools with GPUs/CUDA
python dask <video1> <video2>
<final project guide> ( Sunday 17 November at 13.00)
13. Case studies
Linked to Fall 2018:
Linked to Fall 2019:
https://sites.google.com/a/ku.th/parallel-computing/parallel-and-distributed-computing-2019