stanford-cs149 / cs149gpt
☆58Updated last year
Alternatives and similar repositories for cs149gpt:
Users that are interested in cs149gpt are comparing it to the libraries listed below
- Small scale distributed training of sequential deep learning models, built on Numpy and MPI.☆114Updated last year
- Cataloging released Triton kernels.☆155Updated last week
- Stanford CS149 -- Assignment 1☆77Updated 3 months ago
- ☆170Updated this week
- ☆138Updated 11 months ago
- Fastest kernels written from scratch☆118Updated last month
- An ML Systems Onboarding list☆647Updated 2 months ago
- Collection of kernels written in Triton language☆90Updated 2 months ago
- Fast low-bit matmul kernels in Triton☆187Updated last week
- Step-by-step optimization of CUDA SGEMM☆270Updated 2 years ago
- Applied AI experiments and examples for PyTorch☆211Updated this week
- ring-attention experiments☆116Updated 3 months ago
- ☆178Updated 6 months ago
- Fast CUDA matrix multiplication from scratch☆579Updated last year
- extensible collectives library in triton☆76Updated 3 months ago
- KernelBench: Can LLMs Write GPU Kernels? - Benchmark with Torch -> CUDA problems☆89Updated this week
- Stanford CS149 -- Assignment 3☆21Updated 2 months ago
- Learn CUDA with PyTorch☆14Updated 2 months ago
- Notes on "Programming Massively Parallel Processors" by Hwu, Kirk, and Hajj (4th ed.)☆53Updated 5 months ago
- ☆66Updated 3 weeks ago
- Learning about CUDA by writing PTX code.☆31Updated 10 months ago
- ☆64Updated 2 months ago
- CUDA Learning guide☆289Updated 6 months ago
- High-speed GEMV kernels, at most 2.7x speedup compared to pytorch baseline.☆93Updated 6 months ago
- Stanford CS149 -- Assignment 2☆10Updated 2 months ago
- An implementation of the transformer architecture onto an Nvidia CUDA kernel☆167Updated last year
- ☆151Updated last year
- ☆27Updated this week
- ☆154Updated 7 months ago
- CUDA Matrix Multiplication Optimization☆152Updated 5 months ago