Senior LevelAI, LLMs, Embeddings & MCP
How do you write custom GPU Attention Kernels using OpenAI Triton?
❓ Interview Prompt: Explain OpenAI Triton language, GPU SRAM block loading, matrix multiplication tiling, and writing custom CUDA kernels.
Writing low-level CUDA C++ code to optimize non-standard Transformer layers requires immense effort. OpenAI Triton is a Python-based language and compiler that generates high-performance GPU kernels.
Triton Key Concepts:
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Block-Level Programming: Developers write code operating on 2D matrix blocks (e.g. $64 \times 64$) rather than individual threads.
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Automatic SRAM Staging: Triton's compiler automatically manages GPU Shared Memory (SRAM) staging, coalescing, and vectorization.
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Enables writing custom fused attention, specialized quantization decoders, or custom activation kernels in ~50 lines of Python code!
Code Implementation Example
python
# Custom Vector Add Kernel in OpenAI Triton (Python)
import triton
import triton.language as tl
@triton.jit
def add_kernel(x_ptr, y_ptr, out_ptr, n_elements, BLOCK_SIZE: tl.constexpr):
pid = tl.program_id(axis=0)
block_start = pid * BLOCK_SIZE
offsets = block_start + tl.arange(0, BLOCK_SIZE)
mask = offsets < n_elements
x = tl.load(x_ptr + offsets, mask=mask)
y = tl.load(y_ptr + offsets, mask=mask)
tl.store(out_ptr + offsets, x + y, mask=mask)💡Custom GPU vector addition kernel written in OpenAI Triton.
🎯 Interview Key Takeaway
OpenAI Triton allows writing high-performance GPU kernels in Python with block-level SRAM memory compilation.
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