CSC4700 -Introduction to GPU Programming

About this Video

• Video Title: CSC4700 - Introduction to GPU Programming
• Channel: Stellar at LSU
• Speakers: Dr. Coleman
• Duration: 01:18:37

Introduction

This video lecture introduces GPU programming. The speaker, Dr. Coleman, compares CPUs and GPUs, highlighting their architectural differences and how these impact programming, particularly focusing on CUDA. He explains the evolution of GPUs from graphics processing to general-purpose computation and covers key aspects of CUDA programming, including memory management and thread organization.

Key Takeaways

1. Significant Architectural Differences between CPUs and GPUs: CPUs excel at handling complex control flow, while GPUs prioritize massive parallelism for floating-point operations. This difference fundamentally affects programming approaches.
2. CUDA's Role in GPU Programming: CUDA is a programming language developed by Nvidia to facilitate general-purpose computation on GPUs, moving beyond their initial role in graphics processing.
3. Parallelism and Memory Management: GPU programming demands careful consideration of parallelism and efficient memory management due to the large number of threads and relatively smaller cache compared to CPUs. Programmers must manage threads in groups (warps) and utilize shared memory effectively to optimize performance.
4. High Bandwidth Memory (HBM): GPUs achieve high off-chip data bandwidth by stacking DRAM chips directly onto the GPU chip, improving performance but reducing memory upgradability.
5. Compute Capability: GPUs evolve rapidly; "compute capability" designates the features available to programmers in different GPU generations. This impacts code compatibility and efficiency.