VTU 2022 Scheme  ·  Degree  ·  CSE

Parallel Computing BCS702

Module-wise notes, PYQs, and a built-in resource explorer — everything you need to crack BCS702 in one focused page.

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CodeBCS702
Credits04
CIE / SEE50 / 50
TypeTheory
Exam3 Hours
Hours / Week3:0:2:0
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Last Updated:  15 March 2026

Module Overview

M1

Module 1 Overview

Introduction to parallel programming: Parallel hardware and parallel software - Classifications of parallel computers, SIMD systems, MIMD systems, Interconnection networks, Cache coherence, Shared-memory vs. distributed-memory, Coordinating the processes/threads, Shared-memory, Distributed-memory.

M2

Module 2 Overview

GPU programming: Programming hybrid systems, MIMD systems, GPUs, Performance - Speedup and efficiency in MIMD systems, Amdahl's law, Scalability in MIMD systems, Taking timings of MIMD programs, GPU performance.

M3

Module 3 Overview

Distributed memory programming with MPI: MPI functions, The trapezoidal rule in MPI, Dealing with I/O, Collective communication, MPI-derived datatypes, Performance evaluation of MPI programs, A parallel sorting algorithm.

M4

Module 4 Overview

Shared-memory programming with OpenMP: OpenMP pragmas and directives, The trapezoidal rule, Scope of variables, The reduction clause, loop carried dependency, scheduling, producers and consumers, Caches, cache coherence and false sharing in OpenMP, tasking, tasking, thread safety.

M5

Module 5 Overview

GPU programming with CUDA: GPUs and GPGPU, GPU architectures, Heterogeneous computing, Threads, blocks, and grids Nvidia compute capabilities and device architectures, Vector addition, Returning results from CUDA kernels, CUDA trapezoidal rule I, CUDA trapezoidal rule II: improving performance, CUDA trapezoidal rule III: blocks with more than one warp.

Resource Explorer

Browse all BCS702 study materials — notes, PYQs, and revision resources. Navigate folders for module-wise content and preview files before downloading.

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Frequently Asked Questions

What is BCS702 (Parallel Computing BCS702)?

Parallel Computing BCS702 is a VTU course covered through module-wise syllabus, notes, and PYQ-driven exam practice available on this page.

How many credits is BCS702?

Credits for BCS702: 04.

Are notes and previous year question papers available for BCS702?

Yes. You can access organized notes, PDFs, and PYQ material from the file explorer/resources section on this page.

How should I prepare Parallel Computing BCS702 for VTU exams?

Start with module summaries, solve recent PYQs unit-wise, and finish with complete paper practice under time constraints for SEE readiness.

Is this BCS702 page updated for current VTU scheme?

Yes, this page is maintained with current scheme-oriented materials and practical exam-focused resource curation.

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About Parallel Computing (BCS702)

Parallel Computing (BCS702) is a critical course in the VTU curriculum, essential for any student looking to master the foundations of engineering. It covers key theoretical frameworks and practical concepts that are widely used in the industry today, ensuring students are well-prepared for both exams and their future careers.

Success Strategy

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📘 Detailed Syllabus & Topic Breakdown

Detailed Subject Overview

Parallel Computing (BCS702) is designed to provide a comprehensive look into the core methodologies and advanced theories that define this field. Understanding this subject is fundamental for anyone looking to excel in modern technical domains and industrial engineering.

By studying this course, you will learn how to approach complex problems with a structured mindset, optimizing systems for better performance and reliability—skills that are highly valued in both AI research and software architecture.

Module-by-Module Breakdown

Module 1
Essential

Master the Introduction to parallel programming Parallel hardware and parallel software - Classifications of parallel computers, SIMD systems, MIMD systems, Interconnection networks, Cache coherence...

Key: Exam Priority Concept
Module 2
Math Heavy

Master the GPU programming Programming hybrid systems, MIMD systems, GPUs, Performance - Speedup and efficiency in MIMD systems, Amdahl's law, Scalability in MIMD systems, Takin...

Key: Exam Priority Concept
Module 3
Logic Core

Master the Distributed memory programming with MPI MPI functions, The trapezoidal rule in MPI, Dealing with I/O, Collective communication, MPI-derived datatypes, Performance evaluation of MPI programs,...

Key: Exam Priority Concept
Module 4
Exam Focus

Master the Shared-memory programming with OpenMP OpenMP pragmas and directives, The trapezoidal rule, Scope of variables, The reduction clause, loop carried dependency, scheduling, producers and cons...

Key: Exam Priority Concept
Module 5
High Weight

Master the GPU programming with CUDA GPUs and GPGPU, GPU architectures, Heterogeneous computing, Threads, blocks, and grids Nvidia compute capabilities and device architectures, Vector ad...

Key: Exam Priority Concept

Professional Career Relevance

This subject provides a strong foundation for various technical roles, emphasizing analytical thinking, system design, and the practical application of engineering principles in the modern industry. Mastering these concepts prepares you for high-demand roles in Data Science, System Architecture, and Technical Leadership in top-tier tech companies.

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