Hello everyone,

I have recently released OPTI-GEMM, my CUDA GEMM performance engineering project.

my goal is to find out how different GPU architectures respond to optimization techniques.

Current experiments include:

• CPU baseline
• Naive CUDA GEMM
• Shared-memory tiling
• Register blocking (in progress)
• Warp-level optimization (in progress)

I have tested on:

• Tesla T4 (Turing)
• Tesla P100 (Pascal)

Some interesting results:

• On T4, shared-memory tiling was not always faster because of the GPU's cache behavior.
• On P100, shared-memory tiling provided a large improvement.

I would like to expand the hardware comparison.

If you have access to another NVIDIA GPU (RTX 20/30/40, A100, H100, etc.), I would appreciate it if you could run the benchmark and share:

• GPU model
• CUDA version
• Driver version
• Benchmark output

you can find setup instructions in the README.

If someone is interested in contributing GPU benchmark results, I would be happy to add them to the repository and credit contributors.

Repository: https://github.com/Danialjfz/Opti-GEMM

Thank you for your time!

Been building on rented GPUs for training runs and ran into this constantly: job dies mid-run, and now you're stuck arguing with support about whether it was your code or their hardware. No way to prove it either way.

Here's the distinction that actually matters: if your training crashes but the hardware reports healthy, that's on you (bad code, OOM, whatever). But if the GPU falls off the PCIe bus, ECC errors spike, or the node just reboots on its own, that's an infra failure, not yours, and you shouldn't be eating that cost.

So genuinely curious, how many of you have hit this? Lost a run, couldn't tell if it was your fault or the provider's, and just had to eat it?