CORE

Parallel and Distributed Algorithms

24CSE753 · S7 · 4 credits

Official KTU 2024 syllabus subject — study with practice, PYQs and AI tools.

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Modules
Module 01Overview of parallel processing landscape (7 hours)expand

why and how, types of parallelism, Flynn’s taxonomy and brief overview of parallel architectures. Basic concepts in parallel processing: formal definition of parallelism, concepts of work, speedup, efficiency, overhead, strong and weak scaling (Amdahl’s law, Gustafson’s law), practical considerations using parallel reduction and parallel prefix

Module 02Multi-core programming (6 hours)expand

Shared memory and shared address space, data and task parallelism, summary of available APIs (OpenMP, TBB). OpenMP and parallel merge sort, pointer jumping, parallel BFS and basic linear algebra.

Module 03Distributed memory programming (7 hours)expand

Message Passing Interface, latency + bandwidth model, distributed hashing, sample sort, parallel BFS, and basic linear algebra (matrix-vector, maxtrixmatrix products) with relation to graph algorithms.

Module 04Higher-level programming models (7 hours)expand

Stateless programming in Map/Reduce, Apache Spark and fault-tolerance via Resilient Distributed Datasets. Triangle counting, connected components, single source shortest path using Spark.

Module 05Many-core programming (8 hours)expand

SIMD parallelism and massively parallel GPGPU accelerators. Brief overview of available APIs (OpenACC, oneAPI, OpenCL, SYCL). Programing GPUs in NVIDIA CUDA: data movement and organization, 1D/2D stencils, parallel prefix, matrix- matrix product.

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