Course detail
Parallel Data Processing
FEKT-MPC-PZPAcad. year: 2021/2022
Parallelization using CPU. Parallelization using GPU (matrix operations, deep learning algorithms). Technologies: Apache Spark, Hadoop, Kafka, Cassandra. Distributed computations for operations: data transformation, aggregation, classification, regression, clustering, frequent patterns, optimization. Data streaming – basic operations, state operations, monitoring. Further technologies for distributed computations.
Guarantor
Department
Learning outcomes of the course unit
Prerequisites
Co-requisites
Recommended optional programme components
Literature
BARLAS, Gerassimos. Multicore and gpu programming: an integrated approach. ISBN 9780124171374 (EN)
Planned learning activities and teaching methods
Assesment methods and criteria linked to learning outcomes
Language of instruction
Work placements
Course curriculum
2. CPU Parallel Computing
3. GPU Introduction
4. GPU Memory
5. GPU Synchronization
6. GPU Parallel Patterns
7. GPU Matrix Operations and Streams
8. Spark Introduction
9. Spark Advanced Operations
10. Spark Machine Learning
11. Spark Streaming
12. Other Parallel Technologies
13. Overview and Discussion
14. Final exam
Aims
Specification of controlled education, way of implementation and compensation for absences
Type of course unit
eLearning