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FEKT-MPA-PZPAcad. year: 2024/2025
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.
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Topics remain the same:
1. Introduction to Parallel Computing.2. CPU Parallel Computing – Designing Parallel Programs, Threads, Processes, Synchronization.3. Introduction to GPU – Streaming Multiprocessors, Threads, Blocks, Grids, and PyCUDA.4. GPU Memory – Global, Shared; Speed and Sizes.5. GPU Synchronization – Atomic Operations, Warps.6. GPU Parallel Patterns – Warp Shuffles, Asynchronous Function Execution, Parallel Reduction.7. GPU Matrix Operations and Streams – Matrix Multiplication, Streams and Devices, Utilizing Multiple GPUs.8. Introduction to Spark – Jobs, Stages, Tasks, DAG, etc.9. Advanced Operations in Spark – Shared Variables, Partitioning, Web Interface, DataFrames.10. Machine Learning with Spark – Statistics, Pipelines, Feature Extraction, Classification, Clustering, etc.11. Spark Streaming – DStreams, SQL Operations, MLlib Operations.12. Other Parallel Technologies – Apache Kafka, Nvidia Jetson, TPU.
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