GPU Computing

🚀 Production Deep Learning: GPU Computing

The Factory Analogy

Imagine you’re running a toy factory. You have two options:

1. One super-skilled worker (CPU) who makes toys one at a time, very carefully
2. Thousands of simple workers (GPU) who can all make toys at the same time!

That’s exactly how GPUs work! Let’s explore this magical world together.

🎮 What is GPU Computing?

The Big Idea

A GPU (Graphics Processing Unit) is like having thousands of tiny workers instead of one smart worker.

Simple Example:

• CPU: Like reading a book, one word at a time. Smart, but slow for big jobs.
• GPU: Like having 1000 friends each read one word. Fast for big jobs!

Why Deep Learning Loves GPUs

Deep learning needs to do millions of tiny math problems. A CPU would take forever, but a GPU can do them all at once!

graph TD
    A["Deep Learning Task"] --> B["Millions of Math Problems"]
    B --> C{Choose Your Tool}
    C -->|CPU| D["One at a time 🐌"]
    C -->|GPU| E["All at once! 🚀"]
    D --> F["Hours or Days"]
    E --> G["Minutes!"]

Real Life Example

Training a face recognition model:

• CPU: 2 weeks of waiting 😴
• GPU: 2 hours of fun! 🎉

🧱 Tensor Operations

What’s a Tensor?

Think of tensors like building blocks of different sizes:

Basic Operations

Adding Tensors is like adding matching LEGO blocks:

# Two small towers
a = [1, 2, 3]
b = [4, 5, 6]

# Stack them!
result = [5, 7, 9]
# 1+4=5, 2+5=7, 3+6=9

Multiplying Tensors is where the magic happens:

# Matrix multiply
A = [[1, 2],
     [3, 4]]

B = [[5, 6],
     [7, 8]]

# Result: each cell is a
# sum of products!

Why GPUs Love Tensors

Each tiny GPU worker can handle one number. When you have thousands of numbers, you have thousands of workers doing math at the same time!

📐 Tensor Shapes and Broadcasting

Understanding Shapes

Shape tells you how big your building blocks are:

# Shape: (3,) - a row of 3
[1, 2, 3]

# Shape: (2, 3) - 2 rows, 3 columns
[[1, 2, 3],
 [4, 5, 6]]

# Shape: (2, 2, 3) - 2 pages of
# 2 rows and 3 columns

The Magic of Broadcasting

Problem: You want to add a small thing to a big thing.

Broadcasting: The computer automatically stretches the small thing!

graph TD
    A["Small: [1, 2, 3]"] --> B["Broadcasting Magic ✨"]
    C["Big: [[10, 20, 30],<br>[40, 50, 60]]"] --> B
    B --> D["Result: [[11, 22, 33],<br>[41, 52, 63]]"]

Real Example:

# You have 100 photos
# Each photo has RGB values
photos = shape(100, 256, 256, 3)

# You want to make them
# all brighter by [10, 20, 30]
brightness = [10, 20, 30]

# Broadcasting stretches it!
# Adds to ALL 100 photos!
result = photos + brightness

Broadcasting Rules (Simple!)

1. Same size? Add directly!
2. One is smaller? Stretch to match!
3. Can’t stretch? Error! ❌

📦 Batched Operations

What’s a Batch?

Instead of cooking one pancake at a time, you cook 32 pancakes together!

graph TD
    A["32 Images"] --> B["GPU"]
    B --> C["32 Results"]
    D["Same time! ⚡"]

Why Batch?

Batch Size Matters

# Too small - GPU workers bored
batch_size = 1  # 😴

# Just right - GPU happy!
batch_size = 32  # 😊

# Too big - Out of memory!
batch_size = 10000  # 💥

Real Code Example

# Without batching (slow)
for image in images:
    result = model(image)

# With batching (fast!)
for batch in chunks(images, 32):
    results = model(batch)

💾 Memory Management

GPU Memory is Precious!

Your GPU has limited memory (like a small backpack). You need to pack wisely!

The Memory Problem

graph TD
    A["Your Model: 2GB"] --> B["GPU Memory: 8GB"]
    C["Training Data: 4GB"] --> B
    D["Gradients: 2GB"] --> B
    B --> E["8GB Used - Full! 🎒"]
    F["More Data?"] --> G["CRASH! 💥"]

Memory Saving Tricks

1. Clear Unused Tensors

# After using a tensor
del big_tensor

# Tell GPU to clean up
torch.cuda.empty_cache()

2. Use Mixed Precision

# Normal: 32 bits per number
# Mixed: 16 bits for most things!
# Result: 2x more stuff fits!

3. Gradient Checkpointing Instead of remembering everything, remember just the checkpoints!

Like saving your game at key points, not every second.

4. Smaller Batch Sizes

# Out of memory?
# Make batches smaller!
batch_size = 32  # Too big? 💥
batch_size = 16  # Try this!
batch_size = 8   # Still too big?

Memory Monitoring

# Check how much memory used
used = torch.cuda.memory_allocated()
total = torch.cuda.get_device_properties(0).total_memory

print(f"Using {used/total*100:.1f}%")

🎯 Putting It All Together

Here’s how all these concepts work together:

graph TD
    A["Your Data"] --> B["Create Tensors"]
    B --> C["Check Shapes"]
    C --> D["Make Batches"]
    D --> E["Send to GPU"]
    E --> F["GPU Computing Magic!"]
    F --> G["Monitor Memory"]
    G --> H["Get Results!"]

The Complete Picture

🌟 Key Takeaways

1. GPUs = Many Workers - Parallel processing power!
2. Tensors = Building Blocks - Organize your data
3. Broadcasting = Smart Stretching - Shapes work together
4. Batching = Efficiency - Process groups, not individuals
5. Memory = Your Limit - Manage it or crash!

💪 You’ve Got This!

GPU computing might seem scary, but remember:

• It’s just many workers instead of one
• Tensors are just organized numbers
• Broadcasting stretches automatically
• Batching makes things faster
• Memory management keeps you safe

Now you understand how the world’s smartest AI systems work under the hood!

Go forth and train amazing models! 🚀