Parallel Programming Tools and Model
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Follow on Instagram for UpdatesParallel Programming Tools and Model Syllabus — Unit-wise Content
The official GTU curriculum for Parallel Programming Tools and Model (subject code 3710220) as prescribed for CEE · Semester 1 divides the course into 8 units, covering 37 listed topics. The unit-wise breakdown below is read from the GTU syllabus document for this subject code, so you can check what the course actually covers before deciding which papers to attempt.
Download the official syllabus PDF (3710220)Unit 1 Introduction to Parallel Programming Paradigms and performance
- analysis: Necessary background to follow an parallel programming course
- Issues when programming multicore architectures
- General introduction of the main techniques and basic features of current performance analysis tools
Unit 2 Parallel Architecture
- Introduction to parallel hardware: Multi-cores and multiprocessors
- Parallel Architecture Components
- Flynn’s Taxonomy
- Amdahl’s Law, Network Topology
- Multiprocessor organization and Cache Coherence
- Cache Coherence Protocols
- Memory Consistency models
Unit 3 Shared and Distributed Memory Programming
- MPI and PVM: Synchronization Locks and barriers
- Hardware primitives for efficient lock implementation
- Lock algorithms
- Relaxed consistency models
- High-level language memory models (such Java and/or C++)
- Memory fences
- Summary of basic features in OpenMP, MPI, PVM and POSIX thread API
- Advanced features in OpenMP, MPI, PVM and hybrid programming
Unit 4 Data acquisition and performance analytics
- Tracing of sequential and parallel applications
- Trace processing and performance analytics
- Profile Tools
Unit 5 Models and performance prediction
- Trace-based modeling of parallel performance
- Architectural parameters: CPU, memory
Unit 6 Dataflow programming and novel paradigms for accelerator-based
- architectures: Dataflow paradigms (OmpSs)
- Runtime exploitation of parallelism and architecture hiding
- Advanced parallel programming using accelerators: CUDA
- OpenACC and others if any
Unit 7 Principles of Parallel Algorithm Design
- Embarrassingly Parallel Computations
- Partitioning and Divide-and-Conquer Strategies
- Pipelined Computations
- Synchronous Computations
- Load Balancing and Termination Detection
- Sorting Algorithms
- Numeric Algorithms
- Image Processing Algorithms
Unit 8 Analysis and optimization of real applications
- Analysis of large applications (sequential and/or parallel) and optimization using hybrid programming paradigms (dataflow
- shared- and distributed-memory and accelerators)
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