prajna-lang / prajna
a program language for AI infrastructure
☆88Updated this week
Alternatives and similar repositories for prajna
Users that are interested in prajna are comparing it to the libraries listed below
Sorting:
- a tensor computing compiler based tile programming for gpu, cpu or tpu☆35Updated this week
- Assembler and Decompiler for NVIDIA (Maxwell Pascal Volta Turing Ampere) GPUs.☆78Updated 2 years ago
- An unofficial cuda assembler, for all generations of SASS, hopefully :)☆83Updated 2 years ago
- We invite you to visit and follow our new repository at https://github.com/microsoft/TileFusion. TiledCUDA is a highly efficient kernel …☆181Updated 3 months ago
- play gemm with tvm☆91Updated last year
- ☆70Updated 2 years ago
- A model compilation solution for various hardware☆434Updated last week
- An MLIR-based toy DL compiler for TVM Relay.☆58Updated 2 years ago
- ☆91Updated last month
- Free resource for the book AI Compiler Development Guide☆43Updated 2 years ago
- ☆68Updated 7 months ago
- Triton to TVM transpiler.☆19Updated 7 months ago
- Play with MLIR right in your browser☆135Updated last year
- CUDA PTX-ISA Document 中文翻译版☆39Updated 2 months ago
- 📚FFPA(Split-D): Extend FlashAttention with Split-D for large headdim, O(1) GPU SRAM complexity, 1.8x~3x↑🎉 faster than SDPA EA.☆174Updated last week
- This is a demo how to write a high performance convolution run on apple silicon☆54Updated 3 years ago
- Benchmark Framework for Buddy Projects☆54Updated 2 months ago
- A practical way of learning Swizzle☆19Updated 3 months ago
- ☆148Updated 4 months ago
- Efficient operation implementation based on the Cambricon Machine Learning Unit (MLU) .☆116Updated this week
- 分层解耦的深度学习推理引擎☆73Updated 3 months ago
- hands on model tuning with TVM and profile it on a Mac M1, x86 CPU, and GTX-1080 GPU.☆48Updated last year
- ☆96Updated 3 years ago
- ☆140Updated 4 months ago
- examples for tvm schedule API☆101Updated last year
- ☆193Updated 2 years ago
- My study note for mlsys☆15Updated 6 months ago
- ☆237Updated 3 months ago
- A benchmark suited especially for deep learning operators☆42Updated 2 years ago
- ⚡️Write HGEMM from scratch using Tensor Cores with WMMA, MMA and CuTe API, Achieve Peak⚡️ Performance.☆76Updated last week