Python Numba Vector Addition – Beginner Project

A beginner-friendly introduction to GPU programming in Python using Numba CUDA. This project demonstrates the difference between CPU and GPU computation using a simple vector addition example.

Perfect for absolute beginners who want to:

Learn GPU programming fundamentals
Understand parallel computing concepts
Build a portfolio project for GitHub
Explore NVIDIA CUDA with Python

📚 Introduction

What is CPU Computation?

The CPU (Central Processing Unit) is the main processor in your computer. When you run normal Python code, it executes on the CPU. The CPU is great for general-purpose tasks but processes instructions sequentially (one at a time).

Think of it like a single cashier at a supermarket checkout – they can only serve one customer at a time.

What is GPU Computation?

The GPU (Graphics Processing Unit) is a specialized processor originally designed for graphics. Modern GPUs have thousands of cores that can work in parallel (simultaneously).

Think of it like having hundreds of cashiers at the supermarket – they can serve many customers at the same time!

Why are GPUs Faster for Parallel Tasks?

When you need to perform the same operation on large amounts of data, GPUs excel because they can process many data elements simultaneously. This is called parallel processing.

For example:

Adding two arrays with 1,000,000 elements
Processing pixels in an image
Training machine learning models
Scientific simulations

What is CUDA?

CUDA (Compute Unified Device Architecture) is a parallel computing platform and programming model developed by NVIDIA. It's a set of tools and APIs that allows you to write programs that run on NVIDIA GPUs for general-purpose computing, not just graphics.

Key Points about CUDA:

🎯 It's a GPU programming model made by NVIDIA
🚀 Enables general-purpose GPU computing (not just graphics)
💻 Traditionally uses C/C++ with special CUDA keywords
⚡ Allows you to run thousands of tasks (threads) at the same time
🔧 Gives you explicit control over GPU memory management
📦 Includes a complete toolkit with compilers, libraries, and debugging tools

Example: Imagine you're training an AI model for face detection. It needs millions of math operations. Using only the CPU would be slow (sequential processing). But with CUDA, you can send these operations to the GPU, which processes many of them simultaneously, making it 10-100x faster!

CUDA Components

CUDA is not just one tool—it's a complete ecosystem:

Component	Description	Purpose
Driver	Low-level software that controls the GPU	Lets your computer communicate with the GPU hardware
Toolkit	Complete development package	Includes compiler (nvcc for C++), libraries, debugging tools, IDE plugins
CUDA C/C++	Extended C/C++ with GPU keywords	Write GPU code with full control (requires learning C++)
Numba	Just-in-time compiler for Python	Write GPU code in Python without C++ (easier for Python developers!)

The CUDA Workflow (High-Level Process):

CPU (Host) starts the program and initializes the GPU device
CPU allocates memory on both CPU (host) and GPU (device)
CPU copies data from host memory to device memory
CPU launches kernel (GPU function) with specified threads/blocks
GPU executes the kernel across thousands of threads in parallel
GPU copies results back from device memory to host memory
CPU continues processing or repeats steps 3-6 as needed
Program cleans up memory and terminates

How Does Numba Help?

Numba is a Python library that compiles Python code to run on NVIDIA GPUs using CUDA. Instead of learning complex C++ CUDA programming, you can write GPU code in Python!

Key advantages:

✅ Write GPU code in Python (no C/C++ required)
✅ Easy to learn for Python developers
✅ Great performance boost for numerical computations
✅ Works with NumPy arrays
✅ Just-in-time (JIT) compilation—code is compiled automatically when you run it
✅ Automatic type inference—Numba figures out data types for you

How Numba Execution Works

When you use Numba's @cuda.jit decorator, here's what happens behind the scenes:

Your Python Code (@cuda.jit function)
          ↓
   Python Bytecode
          ↓
   Bytecode Analysis
          ↓
     Numba IR (Intermediate Representation)
          ↓
   Type Inference (figures out data types)
          ↓
   IR Optimization
          ↓
     LLVM IR (Low-Level Virtual Machine)
          ↓
   LLVM JIT Compilation
          ↓
   Machine Code (GPU binary)
          ↓
   Execute on GPU!

What this means: You write normal Python code, add @cuda.jit, and Numba automatically transforms it into GPU machine code. No manual compilation needed!

Example:

@cuda.jit
def my_gpu_function(a, b, c):
    # Your Python code here
    c[0] = a[0] + b[0]

Numba reads this, compiles it to GPU code, and executes it—all automatically!

What Python Code Works with Numba?

Numba doesn't support all Python features on the GPU. Here's what DOES work:

✅ Supported on GPU:

if, elif, else statements
for and while loops
Basic math operators: +, -, *, /, **, %
Math module functions: math.sin(), math.cos(), math.sqrt(), etc.
Tuples
NumPy arrays

❌ NOT Supported on GPU:

Strings (text operations)
Lists (use NumPy arrays instead)
Dictionaries
File I/O operations
Print statements (limited support)
Most Python libraries

Example:

# ✅ This works in Numba CUDA:
for i in range(1000):
    result[i] = math.sqrt(a[i] * a[i] + b[i] * b[i])

# ❌ This does NOT work in Numba CUDA:
message = "Hello"  # Strings not supported
my_list = [1, 2, 3]  # Lists not supported

🎯 Concepts Demonstrated

This project teaches the following concepts:

Concept	Description
CPU vs GPU Computation	Compare sequential CPU processing with parallel GPU processing
Numba CUDA Kernel	Learn to write GPU functions using `@cuda.jit` decorator
Thread Indexing	Understand `cuda.threadIdx.x` and how threads map to data
Memory Management	Copy data between host (CPU) and device (GPU) memory
Parallel Execution	See how thousands of GPU threads work simultaneously
Thread Hierarchy	Understand threads, blocks, and grids organization
Warps	Learn how GPU groups 32 threads together for execution

🧠 Deep Dive: CUDA Concepts

To truly understand GPU programming, you need to learn some core CUDA concepts. Don't worry—we'll explain everything with simple analogies!

🧵 Threads: The Basic Unit of Execution

What is a Thread?

A thread is the smallest unit of execution in CUDA. Think of it as one tiny worker doing one small task.

Each thread runs independently
Each thread executes the same code (the kernel function)
Each thread works on different data
Thousands of threads run simultaneously on the GPU

Analogy: Imagine 1,000 students all solving the same type of math problem (a + b), but each student has different numbers. Each student is like one thread.

Example from our project:

# Thread 0: computes c[0] = a[0] + b[0]
# Thread 1: computes c[1] = a[1] + b[1]
# Thread 2: computes c[2] = a[2] + b[2]
# ... all happening at the same time!

📦 Thread Blocks: Organizing Threads

What is a Block?

Threads are organized into blocks. A block is a group of threads that:

Run on the same SM (Streaming Multiprocessor—a physical GPU core)
Can communicate and share memory with each other
Can be synchronized (wait for each other)

Typical block sizes: 32, 64, 128, 256, 512, or 1024 threads per block.

Analogy: Think of a block as a classroom with 32 students (threads). All students in one classroom can work together and share resources.

🗂️ Grid: The Complete Collection

What is a Grid?

A grid is a collection of blocks. When you launch a kernel, you define:

How many blocks you want
How many threads per block

The GPU then organizes and executes all these threads.

Analogy: If a block is a classroom, a grid is the entire school with many classrooms.

Visual Hierarchy:

GPU Device
   └─ Grid (launched by CPU)
       ├─ Block 0
       │   ├─ Thread 0
       │   ├─ Thread 1
       │   └─ ... Thread 255
       ├─ Block 1
       │   ├─ Thread 0
       │   ├─ Thread 1
       │   └─ ... Thread 255
       └─ Block 2...

🎯 Thread Identification: Finding Your Place

Every thread needs to know which data it should work on. CUDA provides built-in variables:

Variable	What it tells you
`cuda.threadIdx.x`	Thread's position within its block (0 to blockDim-1)
`cuda.blockIdx.x`	Block's position within the grid (0 to gridDim-1)
`cuda.blockDim.x`	Total number of threads in one block
`cuda.gridDim.x`	Total number of blocks in the grid

Computing Global Thread ID:

Each thread calculates its unique global index:

global_id = cuda.blockIdx.x * cuda.blockDim.x + cuda.threadIdx.x

Example with Real Numbers:

Imagine 4 blocks, each with 8 threads (total 32 threads):

Global ID:  0  1  2  3  4  5  6  7 | 8  9 10 11 12 13 14 15 | 16 17 18 19 20 21 22 23 | 24 25 26 27 28 29 30 31
Thread ID:  0  1  2  3  4  5  6  7 | 0  1  2  3  4  5  6  7 | 0  1  2  3  4  5  6  7 | 0  1  2  3  4  5  6  7
Block ID:         Block 0          |        Block 1         |        Block 2         |        Block 3

Finding thread #26:

It's in Block 3 (blockIdx.x = 3)
It's thread #2 within that block (threadIdx.x = 2)
Global ID: 3 × 8 + 2 = 26 ✓

Theater Analogy: You have seat #26 in a theater with 4 rows (blocks), each row having 8 seats (threads). Your seat is: Row 3 × 8 seats + seat 2 = seat 26.

🌊 Warps: How GPU Actually Executes Threads

What is a Warp?

A warp is a group of 32 consecutive threads that execute together. This is a hardware concept.

Key Facts:

The GPU hardware automatically divides blocks into warps of 32 threads
All threads in a warp execute the same instruction at the same time (SIMT: Single Instruction, Multiple Threads)
Only one warp executes per cycle on an SM, but many warps are scheduled
Warp scheduling has zero overhead—switching between warps is instant

Why 32? This is a hardware design choice by NVIDIA. All CUDA-capable GPUs use warps of 32 threads.

Analogy: Think of 32 students in a classroom all solving the same math problem at exactly the same time. The teacher (warp scheduler) gives one instruction, and all 32 students follow it simultaneously.

Important: You don't explicitly create warps in your code. The GPU automatically handles this. But understanding warps helps you write more efficient code.

🏗️ Software vs Hardware: How They Map

This is crucial to understand:

Software (What You Define)	Hardware (Physical GPU)
Grid	Entire GPU Device
Block	Assigned to one SM (Streaming Multiprocessor)
Thread	Runs on one CUDA Core
Warp	Group of 32 threads scheduled together

Important Notes:

You define grids and blocks in your code
CUDA/GPU automatically assigns blocks to SMs
CUDA/GPU automatically assigns threads to CUDA cores
You don't manually assign threads to specific cores
Blocks are scheduled dynamically—no guaranteed execution order
One SM can run multiple blocks if it has enough resources

🎭 The Complete Flow: From Code to Execution

What You Do (Software):

Write a kernel function with @cuda.jit
Define: threads_per_block = 256
Define: blocks_per_grid = 100
Launch: my_kernel[blocks_per_grid, threads_per_block](...)

What CUDA Does Automatically (Hardware):

Creates 100 blocks × 256 threads = 25,600 threads total
Assigns each block to an available SM (GPU core)
Divides each block into warps (256 ÷ 32 = 8 warps per block)
The warp scheduler in each SM decides which warp runs next
Threads in the active warp execute on CUDA cores
Process repeats until all blocks finish

💾 CUDA Memory Hierarchy

Understanding GPU memory is crucial for writing efficient CUDA code.

Memory Types

Memory Type	Location	Speed	Scope	Lifetime	Size
Registers	On-chip (inside SM)	Fastest	Per thread	Thread duration	Very small (~64KB per SM)
Shared Memory	On-chip (inside SM)	Very fast	Per block	Block duration	Small (~48-96KB per SM)
Local Memory	Off-chip (DRAM)	Slow	Per thread	Thread duration	Large
Global Memory	Off-chip (DRAM)	Slow	Entire grid	Application duration	Very large (GB)
Constant Memory	Off-chip (DRAM, cached)	Medium	Entire grid	Application duration	64KB

Memory Explained Simply

1. Global Memory (What we use in this project)

Largest and slowest
Accessible by all threads
Where you allocate arrays with cuda.device_array() or cuda.to_device()
Must explicitly copy data between host (CPU) and device (GPU)

2. Shared Memory

Fast, small, shared within a block
Threads in the same block can share data quickly
Manually managed (advanced topic)
Great for optimization

3. Registers

Fastest memory
Automatic—compiler uses them for local variables
Limited per thread

4. Constant Memory

Read-only for kernels
Good for values that don't change
Cached for fast access

Memory Transfer Pattern (This Project):

CPU Memory (Host)                  GPU Memory (Device)
    a, b arrays
        ↓
  cuda.to_device() ──────────────→  a_device, b_device
                                          ↓
                                    GPU Kernel Executes
                                    (uses Global Memory)
                                          ↓
                                      c_device
        ↓
  c = c_device.copy_to_host() ←──────────
        ↓
    c array

Important: Data transfer between CPU and GPU is slow. For real applications:

Minimize transfers
Keep data on GPU as long as possible
Process multiple operations on GPU before copying results back

🚀 Why This Project Matters

Vector addition is the "Hello World" of GPU programming!

Just like printing "Hello World" is the first step in learning a programming language, vector addition is the first step in learning GPU programming. It's simple enough to understand but demonstrates the core concepts:

Writing GPU kernels
Managing GPU memory
Launching parallel threads
Understanding speedup from parallelization

Once you understand vector addition on the GPU, you can apply these concepts to more complex problems like:

Matrix multiplication
Image processing
Deep learning
Scientific computing

📁 Project Structure

python-numba-vector-addition/
├── cpu/
│   └── vector_add_cpu.py      # CPU implementation (sequential)
├── cuda/
│   └── vector_add_gpu.py      # GPU implementation (parallel)
└── README.md                  # This file

What Each File Does

`cpu/vector_add_cpu.py`

Language: Standard Python
Hardware: Runs on CPU only
Method: Uses a simple for-loop to add vectors element by element
Speed: Slower for large datasets (sequential processing)
Purpose: Demonstrates traditional CPU approach

Key Code:

for i in range(n):
    c[i] = a[i] + b[i]  # One addition at a time

`cuda/vector_add_gpu.py`

Language: Python + Numba CUDA
Hardware: Runs on NVIDIA GPU
Method: Uses GPU kernel with thousands of parallel threads
Speed: Much faster for large datasets (parallel processing)
Purpose: Demonstrates modern GPU approach

Key Code:

@cuda.jit
def vector_add_kernel(a, b, c):
    idx = cuda.threadIdx.x + cuda.blockIdx.x * cuda.blockDim.x
    if idx < c.size:
        c[idx] = a[idx] + b[idx]  # Many additions at the same time!

🛠️ Requirements

Hardware Requirements

NVIDIA GPU (GeForce, Quadro, Tesla, RTX, etc.)
- Any CUDA-capable NVIDIA GPU will work
- To check if you have one: Run nvidia-smi in terminal

Software Requirements

Python 3.7+
CUDA Toolkit (usually installed with NVIDIA drivers)
Numba package

Installing Numba

Install Numba using pip:

pip install numba

This will automatically install NumPy as well, which is required for array operations.

💻 How to Run

Running the CPU Version

The CPU version uses standard Python and will run on any computer:

python cpu/vector_add_cpu.py

Expected Output:

============================================================
CPU VECTOR ADDITION - Standard Python
============================================================

Creating two vectors with 10,000,000 elements each...
Vector A: first 10 elements = [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
Vector B: first 10 elements = [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

Performing vector addition on CPU...
Result C: first 10 elements = [2. 2. 2. 2. 2. 2. 2. 2. 2. 2.]
Expected: [2. 2. 2. 2. 2. 2. 2. 2. 2. 2.]

✓ CPU computation completed in X.XXXX seconds

Running the GPU Version

The GPU version requires an NVIDIA GPU and Numba CUDA:

python cuda/vector_add_gpu.py

Expected Output:

✓ CUDA is available!
✓ Detected GPU: NVIDIA GeForce RTX XXXX

============================================================
GPU VECTOR ADDITION - Python + Numba CUDA
============================================================

Creating two vectors with 10,000,000 elements each...
Vector A: first 10 elements = [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
Vector B: first 10 elements = [1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]

Step 1: Copying data from CPU to GPU...
✓ Data copied to GPU memory

Step 2: Configuring GPU execution...
  - Threads per block: 256
  - Blocks in grid: 39,063
  - Total threads: 10,000,128

Step 3: Executing vector addition on GPU...
✓ GPU computation completed in X.XXXX seconds

Step 4: Copying result from GPU back to CPU...
✓ Result copied back to CPU memory

RESULTS:
Result C: first 10 elements = [2. 2. 2. 2. 2. 2. 2. 2. 2. 2.]
Expected: [2. 2. 2. 2. 2. 2. 2. 2. 2. 2.]

✓ Verification PASSED: GPU result matches expected values!

📖 Understanding the Code

CPU Version – Key Concepts

def vector_add_cpu(a, b):
    n = len(a)
    c = np.zeros(n)
    
    # Sequential loop - one addition at a time
    for i in range(n):
        c[i] = a[i] + b[i]
    
    return c

What happens:

Create empty result array
Loop through each index (0, 1, 2, ...)
Add corresponding elements one by one
Each addition happens after the previous one finishes

Performance: Slow for large arrays because operations are sequential.

GPU Version – Key Concepts

The Kernel Function

@cuda.jit  # Tells Numba: compile this for GPU!
def vector_add_kernel(a, b, c):
    # Calculate this thread's unique global index
    idx = cuda.threadIdx.x + cuda.blockIdx.x * cuda.blockDim.x
    
    # Bounds check (important when thread count > array size)
    if idx < c.size:
        # Each thread computes ONE element
        c[idx] = a[idx] + b[idx]

What happens:

GPU launches thousands of threads simultaneously
Each thread calculates its unique global index (idx)
Thread with idx=0 computes c[0] = a[0] + b[0]
Thread with idx=1 computes c[1] = a[1] + b[1]
Thread with idx=2 computes c[2] = a[2] + b[2]
... all at the same time (parallel execution)!
Threads with idx ≥ array size do nothing (bounds check)

Performance: Much faster because thousands of additions happen in parallel.

Launching the Kernel

# Configuration
threads_per_block = 256  # Each block has 256 threads
blocks_per_grid = (n + threads_per_block - 1) // threads_per_block

# Launch the kernel
vector_add_kernel[blocks_per_grid, threads_per_block](a_device, b_device, c_device)

The Launch Configuration: [blocks, threads]

This tells CUDA:

How many blocks to create
How many threads per block

Example Calculation:

Array size: 10,000,000 elements
Threads per block: 256
Blocks needed: 10,000,000 ÷ 256 = 39,063 blocks (rounded up)
Total threads launched: 39,063 × 256 = 10,000,128 threads
Extra threads: 10,000,128 - 10,000,000 = 128 (these do nothing thanks to bounds check)

Why the bounds check if idx < c.size:?

Because we often launch more threads than needed! We round up to the nearest block size. Without the bounds check, extra threads would access invalid memory (crash!).

CUDA Keywords and Functions Explained

Decorators and Kernel Definition

Keyword	Explanation	Example
`@cuda.jit`	Decorator that compiles the function to run on GPU	`@cuda.jit` `def my_kernel(...):`

Thread Identification (Built-in Variables)

Variable	Type	Explanation	Range
`cuda.threadIdx.x`	int	Thread ID within its block	0 to (blockDim.x - 1)
`cuda.threadIdx.y`	int	Thread ID (Y-axis) for 2D/3D blocks	0 to (blockDim.y - 1)
`cuda.threadIdx.z`	int	Thread ID (Z-axis) for 3D blocks	0 to (blockDim.z - 1)
`cuda.blockIdx.x`	int	Block ID within the grid	0 to (gridDim.x - 1)
`cuda.blockIdx.y`	int	Block ID (Y-axis) for 2D/3D grids	0 to (gridDim.y - 1)
`cuda.blockIdx.z`	int	Block ID (Z-axis) for 3D grids	0 to (gridDim.z - 1)
`cuda.blockDim.x`	int	Number of threads per block (X)	Set by you at launch
`cuda.blockDim.y`	int	Number of threads per block (Y)	Set by you at launch
`cuda.blockDim.z`	int	Number of threads per block (Z)	Set by you at launch
`cuda.gridDim.x`	int	Number of blocks in grid (X)	Calculated by CUDA

Computing Global Thread ID:

# 1D case (our project):
idx = cuda.blockIdx.x * cuda.blockDim.x + cuda.threadIdx.x

# 2D case (for images):
row = cuda.blockIdx.y * cuda.blockDim.y + cuda.threadIdx.y
col = cuda.blockIdx.x * cuda.blockDim.x + cuda.threadIdx.x

Memory Management Functions

Function	Purpose	Returns	Example
`cuda.to_device(array)`	Copy array from CPU to GPU	GPU array	`a_gpu = cuda.to_device(a_cpu)`
`cuda.device_array(shape)`	Allocate empty array on GPU	GPU array	`c_gpu = cuda.device_array(1000)`
`gpu_array.copy_to_host()`	Copy GPU array back to CPU	NumPy array	`result = c_gpu.copy_to_host()`
`cuda.synchronize()`	Wait for all GPU operations to finish	None	`cuda.synchronize()`

GPU Information Functions

Function	Purpose	Example
`cuda.is_available()`	Check if CUDA GPU is available	`if cuda.is_available():`
`cuda.get_current_device()`	Get current GPU device object	`device = cuda.get_current_device()`
`device.name`	Get GPU name	`print(device.name.decode())`

What is a Kernel Function?

A kernel is a special function that:

Is defined with @cuda.jit decorator (or __global__ in C++ CUDA)
Runs on the GPU (device)
Is called/launched from CPU code (host)
Cannot explicitly return values (must write results to arrays)
Runs asynchronously (CPU doesn't wait for it to finish by default)

Key Characteristics:

Characteristic	Description
Execution	Runs on GPU, launched from CPU
Returns	Cannot return values—must write to output arrays
Declaration	Must specify blocks and threads when launching
Asynchronous	CPU continues immediately after launch (unless you synchronize)

Launching a Kernel (Invocation):

# Define the configuration
my_kernel[blocks_per_grid, threads_per_block](arg1, arg2, arg3)
#         ^^^^^^^^^^^^^^^^ ^^^^^^^^^^^^^^^^^  ^^^^^^^^^^^^^^^^^^^^^
#         Grid config      Block config      Function arguments

Asynchronous Execution:

# CPU launches kernel
my_kernel[100, 256](a, b, c)  # CPU doesn't wait!

# CPU continues immediately
print("Kernel launched!")  # This runs while GPU is still working

# Explicitly wait for GPU to finish
cuda.synchronize()  # Now CPU waits for GPU

print("GPU finished!")  # This runs after GPU completes

Memory Transfer Flow

CPU (Host) Memory          GPU (Device) Memory
      
      A, B  ─────────────────────>  A, B          (cuda.to_device)
                                       │
                                       │ GPU Kernel
                                       │ Computes C
                                       ↓
                                       C
                                       
      C     <─────────────────────  C             (.copy_to_host)

Important: Moving data between CPU and GPU takes time! For real applications, you want to minimize these transfers and keep data on the GPU as much as possible.

Transfer Bottleneck Example:

GPU computation: 0.001 seconds ⚡ (very fast)
CPU→GPU transfer: 0.010 seconds 🐌 (10x slower!)
GPU→CPU transfer: 0.010 seconds 🐌
Total time: 0.021 seconds (transfer dominates!)

Optimization Strategy:

Batch multiple operations on GPU before transferring back
Reuse GPU memory across multiple kernel calls
Overlap computation with transfers (advanced)

⚡ Performance Comparison

For a vector with 10 million elements:

Implementation	Hardware	Time (typical)	Speedup
CPU (Python loop)	CPU	~1-3 seconds	1× (baseline)
GPU (Numba CUDA)	GPU	~0.01-0.1 seconds	10-100× faster

Note: Actual speedup depends on:

GPU model (newer = faster)
Vector size (larger = better GPU advantage)
Data transfer overhead
CPU model

For very small vectors (< 10,000 elements), the CPU might be faster because the overhead of copying data to/from GPU dominates the computation time.

🎓 Learning Path

After understanding this project, you can explore:

Matrix Multiplication – 2D arrays and more complex thread indexing
Image Processing – Apply filters to images using GPU
Reduction Operations – Sum, max, min across large arrays
Shared Memory – Advanced technique for faster GPU computation
CuPy – NumPy-like library that runs entirely on GPU
PyTorch/TensorFlow – Deep learning frameworks that use GPUs

📝 Important Notes

CPU Version

✅ Uses standard Python – runs on any computer
✅ No special hardware required
✅ Easy to understand and modify
⚠️ Slower for large datasets (sequential processing)

GPU Version

✅ Uses Numba CUDA – runs on NVIDIA GPUs
✅ Much faster for large datasets (parallel processing)
⚠️ Requires NVIDIA GPU with CUDA support
⚠️ Requires Numba package (pip install numba)
⚠️ Data transfer between CPU/GPU adds overhead

🐛 Troubleshooting

"CUDA is not available"

Problem: GPU code won't run

Solutions:

Check if you have an NVIDIA GPU: nvidia-smi
Install/update NVIDIA drivers
Install CUDA Toolkit
Install Numba: pip install numba

"No module named 'numba'"

Problem: Numba is not installed

Solution:

pip install numba

Very slow GPU performance

Possible reasons:

Vector size is too small (GPU overhead dominates)
Old/low-end GPU
Using integrated graphics instead of dedicated GPU

🌟 Why This is Portfolio-Worthy

This project demonstrates:

✅ Understanding of parallel computing – You understand CPU vs GPU
✅ Modern Python skills – Using advanced libraries like Numba
✅ Performance optimization – You know when and how to use GPUs
✅ Clear documentation – Professional README and comments
✅ Practical application – Real-world performance comparison

Perfect for:

GitHub portfolio showcasing GPU programming skills
Learning foundation for machine learning (GPUs power modern AI)
Understanding high-performance computing concepts
Interview talking point for data science/ML positions

📚 Additional Resources

👨‍💻 Target Audience

This project is designed for:

Absolute beginners in GPU programming
Python developers wanting to learn CUDA
Students learning parallel computing
Data scientists exploring GPU acceleration
Anyone building a GitHub portfolio

Prerequisites:

Basic Python knowledge (functions, loops, arrays)
Understanding of NumPy arrays (helpful but not required)
No prior CUDA or GPU programming experience needed!

📄 License

This project is open source and available for educational purposes.

🙋 Questions?

Common questions beginners ask:

Q: Do I need a powerful GPU?
A: No! Any CUDA-capable NVIDIA GPU will work, even older models.

Q: Will this work on AMD GPUs?
A: No, Numba CUDA only works with NVIDIA GPUs. For AMD, look into ROCm.

Q: Can I run this on Google Colab?
A: Yes! Colab provides free NVIDIA Tesla GPUs. Perfect for learning!

Q: Is this faster than NumPy?
A: NumPy is already optimized. GPU shines for custom operations or very large arrays.

Q: What's next after this project?
A: Try matrix multiplication, image processing, or explore deep learning frameworks!

Happy GPU Programming! 🚀

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README.md		README.md

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