CORE

GPU Architecture and Programming

24CSE7133 · S7 · 4 credits

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

Syllabus

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⚡ DAY-BEFORE EXAM

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Strategy: High-yield concepts + PYQs + weak areas

2. Topic Prioritization (Highest Marks First)Tap a module to mark as weak

PRIORITY A

Module 1

Why:Foundation module

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PRIORITY A

Module 2

Why:High exam relevance

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PRIORITY B

Module 3

Why:Core module

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PRIORITY B

Module 4

Why:Applied concepts

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PRIORITY C

Module 5

Why:Standard exam coverage

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Modules

Module 01GPU Structure) (7 hoursexpand

Graphics Processing Units (GPU) as Parallel Computers - Understanding Parallelism with GPU- Architecture of a modern GPU - Parallel Programming Languages and Models - Overarching Goals - History of GPU computing - Evolution of Graphics Pipelines - GPU Computing.

Module 02Parallel Programming) (7 hours)expand

Goals of Parallel Programming - Problem Decomposition - Algorithm Selection - Computational Thinking - Some Common Parallel Patterns- Loop-based patterns - Fork/join pattern - Tiling/grids - Divide and conquer.

Module 03CUDA Programming) (8 hours)expand

Data Parallelism - CUDA Program Structure – Matrix Multiplication Example - Device Memories and Data Transfer - Kernel Functions and Threading - Function declarations - Kernel launch - Predefined variables - Runtime API - CUDA Threads: CUDA Thread Organization -Thread Assignment - Thread Scheduling and Latency Tolerance - CUDA Memories: Importance of Memory Access Efficiency - CUDA Device Memory Types - A Strategy for Reducing Global Memory Traffic.

Module 04Performance considerations) (7 hours)expand

Thread execution - Global memory bandwidth - Dynamic partitioning of SM resources - Data prefetching Instruction mix - Thread Granularity - Floating Point considerations: FP format - Representable numbers – Special bit patterns and precision - Arithmetic accuracy and rounding - Algorithm considerations - Debugging and Profiling: Debugging CUDA programs - Profiling CUDA programs

Module 05Introduction to OPENCL: Data Parallelism Modelexpand

Device Architecture - Kernel Functions – Device Management and Kernel Launch - Memory Model - Basic OpenCL Examples.

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