Sumários SIGARRA

Introduction to large scale data and Python libraries for handling large scale data.

Introduction to parallelism. Shared memory programming model and architecture.

Distributed programming model and architecture. Loop restructuring and Python multiprocessing pool.

Parallel and Distributed platforms. High Performance Computing, High Throuhput Computing, Cloud Computing.

Introduction to virtual machines and virtualization. The MapReduce model.

Introduction to Apache Spark.

First Test. Duration 1h 50 min.
Topics:

Concept of cloud and types of clouds
Concept of virtualization
Types of computer architectures
programming models, data distribution
advantages and disadvantages
characteristics
Exercises given in practical classes

Suggested book chapters and sections (Cloud Computing - Theory and Practice, by Dan Marinescu. 1st edition - Chapters and sections may change if you use the second edition)
- C1: intro, s1.3, s1.4, s1.5, s1.6, s1.7
- C2: intro, s2.1, s2.2, s2.9, s2.10
- C3: intro, s3.2, s3.7, s3.8, s3.9, s3.10
- C4: intro, s4.1, s4.2, s4.6, s4.7, s4.8, s4.9, s4.10
- C5: intro, s5.1, s5.2, s5.3, s5.4
- C6: intro

Apache Beam, Dask and other Python libraries.

Modin and joblib Python libraries.
Graph Neural Networks.

Using logic as knowledge representation and as an alternative to graph networks.
Introduction to GPU programming.

Opportunities for parallelization: programming and data.

Second test. Duration: 1h 50 min.
Contents:

Data distribution and schedulers
apache beam, dask, modin, joblib
GNNs and pytorch geometric
cupy, numba, cudnn, rapids-ai

Review the links suggested in theoretical and practical classes
Review practical classes