Why Deep Learning Needs GPU

Why Deep Learning Needs GPU

GPUs are everywhere today. Let’s explain to a 12-year-old why deep learning needs them and why they are so fast.

Imagine your computer’s brain (the CPU) is good at solving one math problem at a time, like doing homework step by step. But sometimes, you must solve many issues simultaneously—like solving a whole page of math problems.

This is where the GPU (Graphics Processing Unit) comes in.
Now, inside a GPU, there are special helpers called “tensor cores.” These are like super-fast math problem solvers.

Scenario 1 : GPU without  Tensor Cores
The robot looks at a picture and does the math step by step to determine whether it’s a cat or a dog.
This GPU can solve math problems quickly, but it does so in a regular way—solving one piece of the problem at a time.

Scenario 2 : GPU with Tensor Cores
The robot uses a GPU with tensor cores, which can solve many math problems at once in a special, more efficient way.
Tensor cores are designed to handle these types of calculations, which involve large groups of numbers (called matrices or tensors).

How GPU Works :
GPUs are designed for massively parallel processing, with thousands of cores that handle multiple calculations simultaneously. This architecture enables:
SIMD (Single Instruction, Multiple Data): Executing the same instruction on multiple data elements.
Pipelining: Processing multiple tasks in stages, increasing throughput.
Multi-Threading: Handling multiple threads, leveraging thread-level parallelism.

How Deep Learning Needs GPUs :
Deep learning relies heavily on matrix operations, for which GPUs are optimized. Key benefits include:
Fast Matrix Multiplications: Essential for neural network computations.
Parallel Data Processing: GPUs handle large datasets efficiently.
Scalability: Multiple GPUs can be combined for increased performance.

Why GPUs Are Fast :
High-Bandwidth Memory: Dedicated memory (e.g., GDDR6) for rapid data access.
Comprehensive Memory Interface: Enabling fast data transfer.
Advanced Manufacturing: Higher transistor density (e.g., 5nm or 7nm) increases performance.

The Impact :
GPUs have transformed deep learning, enabling:
Faster Training: Reduced training times for complex models.
Larger Models: Handling increased complexity and data.
Breakthroughs: Advancements in computer vision, NLP, and more.

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Source: LinkedIn

Credits: Mr. Mahesh Mallikarjunaiah