Perfect adjustment — “inside out” sounds like a deep-dive seminar, while “a glimpse into GPU programming” hits the right level for a 30-minute talk. It promises insight without overwhelming the audience.

Here’s a refined version of the outline tuned for that scope and pacing — aimed at a PyTorch-literate audience who wants to peek under the hood just enough to get Triton.

🎯 Title

From PyTorch to Triton — A Glimpse into GPU Programming

1. Motivation: Why Look Beneath PyTorch

Start with a relatable question:

Why is my PyTorch model fast on GPU… but not as fast as NVIDIA’s benchmarks?

Use a simple PyTorch snippet (like softmax, swish, or a fused activation) to illustrate the hidden magic:

y = torch.softmax(x, dim=-1)

Then reveal the hidden stack:

Python → PyTorch → CUDA Kernels → GPU Hardware

Point out that while PyTorch gives expressiveness, its prebuilt CUDA kernels are black boxes. If your idea doesn’t fit exactly into those kernels — you leave performance on the table. That’s where Triton steps in.

2. What Happens When You Run a PyTorch Op

Visualize the hidden journey:

  1. You write a Python function.
  2. PyTorch dispatches to a precompiled CUDA kernel.
  3. That kernel runs across thousands of GPU threads.

Show the GPU hierarchy briefly:

GPU
 ├─ SMs (Streaming Multiprocessors)
 │   ├─ Thread Blocks
 │   │   ├─ Warps
 │   │   │   └─ Threads

Explain intuitively:

  • A kernel = one GPU “program.”
  • It’s launched many times, each on a different block.
  • Threads inside cooperate on small chunks of data.

This is the hardware layer Triton makes accessible.

3. Why High-Level PyTorch Isn’t Always Efficient

Demonstrate with a simple operation fusion example:

y = x * torch.sigmoid(x)

Explain that PyTorch launches two kernels (one for sigmoid, one for mul), each reading and writing to global memory.

Use a small diagram showing redundant global memory reads/writes — the GPU spends more time moving data than computing.

Introduce the key performance idea:

GPU performance depends on how much work you do per byte moved — the arithmetic intensity.

This sets up why custom kernels matter.

4. Meet Triton — Pythonic GPU Programming

Introduce Triton as:

  • A Python DSL that compiles to efficient GPU kernels.
  • Lets you think in tiles or blocks of data, not individual threads.
  • Designed to make performance-critical code accessible to non-CUDA experts.

Show a short kernel:

@triton.jit
def swish_kernel(x_ptr, y_ptr, n_elements, BLOCK_SIZE: tl.constexpr):
    pid = tl.program_id(0)
    offsets = pid * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
    mask = offsets < n_elements
    x = tl.load(x_ptr + offsets, mask=mask)
    y = x * tl.sigmoid(x)
    tl.store(y_ptr + offsets, y, mask=mask)

Then launch it:

grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)
swish_kernel[grid](x, y, n_elements, BLOCK_SIZE=1024)

Explain in plain terms:

  • Each program (like a CUDA block) processes BLOCK_SIZE elements.
  • tl.arange gives vectorized access — coalesced memory.
  • The compiler emits optimized PTX code, just like a hand-tuned CUDA kernel.

5. Tiling and Memory Reuse — Thinking Like the GPU

Move one layer deeper to tile-based computation.

Use a simple matrix multiply illustration:

A_tile ---> \
              x  => C_tile
B_tile ---> /

Explain how each tile:

  • Is small enough to fit in shared memory or registers.
  • Is reused multiple times before fetching new data.
  • Maximizes arithmetic intensity and memory coalescing.

Triton makes this pattern explicit and composable in Python, which is why it can match or beat handwritten CUDA for many workloads.

6. Where Triton Fits in the Stack

Help the audience place it mentally:

Layer Tool Who Controls It Example
Framework PyTorch You torch.nn.Linear
Kernel Library cuDNN / cuBLAS NVIDIA torch.matmul
Custom Kernels Triton You custom_softmax
Hardware GPU SMs / Memory Hardware Executes PTX

Mention that PyTorch 2.0+ uses Triton internally for things like torch.compile, so learning it connects directly to the modern stack.

7. When to Reach for Triton

Quick bullet-style use cases (no deep dive):

  • Fusing multiple PyTorch ops into one kernel.
  • Custom attention or normalization layers.
  • Research ideas outside cuDNN’s patterns.
  • Optimizing memory-bound operations.

8. Takeaway — A Glimpse into the GPU’s Mind

End with a conceptual zoom-out:

GPU programming isn’t about writing C++; it’s about understanding data movement. Triton gives you a lens — and a toolkit — to express that understanding directly in Python.

Encourage exploration: “You don’t need to replace PyTorch. You just need to peek underneath once — and you’ll never see your tensors the same way again.”

This version fits comfortably in 25–30 minutes with time for short demos or audience questions. Would you like me to annotate this outline with which parts to demo vs explain with slides (so it flows like a talk plan)?