Triton Academy

Triton Academy is an initiative to educate developers on writing efficient GPU kernels using Triton. It's a work in progress and it's goal is to provide:

🎓 Tutorials – Learn Triton step by step
🔍 Famous Triton Kernels detailed explanations
📐 Mathematic formulas and proofs behind backpropogation in kernels
📖 Documentation & Debugging – Official docs and best practices
🔬 Benchmarks – Performance comparisons with CUDA

What is Triton?

Triton is an open-source programming language and compiler designed specifically for GPU programming. It aims to simplify the development of efficient GPU kernels by providing a higher-level abstraction than CUDA or other low-level GPU programming models.

Triton enables developers to write high-performance GPU code with Python-like syntax while automatically handling many low-level optimizations that would otherwise require significant expertise in GPU architecture. It was developed by OpenAI and is now widely used in machine learning and scientific computing applications.

Why Triton instead of CUDA?

While CUDA offers fine-grained control over GPU programming, it comes with several challenges:

1. Steep Learning Curve: CUDA requires understanding complex GPU architecture details
2. Verbose Code: Simple operations often require extensive boilerplate code
3. Manual Optimization: Developers must manually handle memory coalescing, tiling, and other optimizations
4. Limited Portability: CUDA is specific to NVIDIA GPUs

Triton addresses these issues by:

1. Higher Abstraction: Provides intuitive programming constructs
2. Automatic Optimization: Handles many low-level optimizations automatically
3. Python Integration: Seamlessly integrates with the Python ecosystem
4. Performance: Achieves performance comparable to hand-optimized CUDA in many cases
5. Readability: More maintainable code that clearly expresses intent

Quick Example: Vector Addition in Triton

Let’s expand on your example and provide comprehensive resources on Triton, details on Triton Academy, and how you can contribute!

import torch
import triton
import triton.language as tl

@triton.jit
def vector_add_kernel(X, Y, Z, N: tl.constexpr):
    pid = tl.program_id(axis=0)  # Get the program ID
    block_size = 128  # Number of elements per block
    offsets = pid * block_size + tl.arange(0, block_size)  # Compute memory offsets
    mask = offsets < N  # Ensure we don’t go out of bounds
    x = tl.load(X + offsets, mask=mask)  # Load X
    y = tl.load(Y + offsets, mask=mask)  # Load Y
    tl.store(Z + offsets, x + y, mask=mask)  # Store the result

# Example usage
N = 1024
X = torch.randn(N, device="cuda")
Y = torch.randn(N, device="cuda")
Z = torch.empty(N, device="cuda")

grid = (N // 128,)
vector_add_kernel[grid](X, Y, Z, N=N)
print(Z)  # Output: X + Y

Explanation: • tl.program_id(axis=0) → Gets the program index
• tl.arange(0, block_size) → Generates thread-local indices
• tl.load and tl.store → Handle memory operations efficiently

Resources