Distributed-Systems-Guide
awesome-tensor-compilers
Our great sponsors
| Distributed-Systems-Guide | awesome-tensor-compilers | |
|---|---|---|
| 1 | 9 | |
| 22 | 2,171 | |
| - | - | |
| 4.2 | 4.4 | |
| over 2 years ago | 3 months ago | |
| - | - |
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.
Distributed-Systems-Guide
-
Useful Tools and Resources for Distributing Computing
A useful list of Tools, Programs, and Resources for Distributing Computing.
awesome-tensor-compilers
-
MatX: Faster Chips for LLMs
> So long as Pytorch only practically works with Nvidia GPUs, everything else is little more than a rounding error.
This is changing.
https://github.com/merrymercy/awesome-tensor-compilers
There are more and better projects that can compile an existing PyTorch codebase into a more optimized format for a range of devices. Triton (which is part of PyTorch) TVM and the MLIR based efforts (like torch-MLIR or IREE) are big ones, but there are smaller fish like GGML and Tinygrad, or more narrowly focused projects like Meta's AITemplate (which works on AMD datacenter GPUs).
Hardware is in a strange place now... It feels like everyone but Cerebras and AMD/Intel was squeezed out, but with all the money pouring in, I think this is temporary.
-
Run Llama2-70B in Web Browser with WebGPU Acceleration
I think this is true of AI compilation in general. Torch MLIR, AITemplate and really everything here fly under the radar.
https://github.com/merrymercy/awesome-tensor-compilers#open-...
-
Ask HN: How to get good as a self taught ML engineer?
> I really want to do some great work and help people.
Have you looked into ML compilation?
https://github.com/merrymercy/awesome-tensor-compilers
IMO there is low hanging fruit in the space between high performance ML compilers/runtimes and the actual projects people use. If you practice porting projects you use to these frameworks, that would give you a massive performance edge.
-
Ask HN: What new programming language(s) are you most excited about?
While not all "languages" persay, I am excited about the various ML compilation efforts:
https://github.com/merrymercy/awesome-tensor-compilers
Modern ML training/inference is inefficient, and lacks any portability. These frameworks are how that changes.
-
Research Papers on ML in Compilers
You might be interested in this: https://github.com/merrymercy/awesome-tensor-compilers
-
The Distributed Tensor Algebra Compiler (2022)
* collection of papers in https://github.com/merrymercy/awesome-tensor-compilers
I also have an interest in the community more widely associated with pandas/dataframes-like languages (e.g. modin/dask/ray/polars/ibis) with substrait/calcite/arrow their choice of IR
- A list of compiler projects and papers for tensor computation and deep learning
- A List of Tensor Compilers
-
C-for-Metal: High Performance SIMD Programming on Intel GPUs
Compiling from high-level lang to GPU is a huge problem, and we greatly appreciate efforts to solve it.
If I understand correctly, this (CM) allows for C-style fine-level control over a GPU device as though it were a CPU.
However, it does not appear to address data transit (critical for performance). Compilation and operator fusing to minimize transit is possibly more important. See Graphcore Poplar, Tensorflow XLA, Arrayfire, Pytorch Glow, etc.
Further, this obviously only applies to Intel GPUs, so investing time in utilizing low-level control is possibly a hardware dead-end.
Dream world for programmers is one where data transit and hardware architecture are taken into account without living inside a proprietary DSL Conversely, it is obviously against hardware manufacturers' interests to create this.
Is MLIR / LLVM going to solve this? This list has been interesting to consider:
https://github.com/merrymercy/awesome-tensor-compilers
What are some alternatives?
Awesome-Microservices-DotNet - 💎 A collection of awesome training series, articles, videos, books, courses, sample projects, and tools for Microservices in .NET
Arraymancer - A fast, ergonomic and portable tensor library in Nim with a deep learning focus for CPU, GPU and embedded devices via OpenMP, Cuda and OpenCL backends
storj - Ongoing Storj v3 development. Decentralized cloud object storage that is affordable, easy to use, private, and secure.
alpa - Training and serving large-scale neural networks with auto parallelization.
tune - An abstraction layer for parameter tuning
Fable - The project has moved to a separate organization. This project provides redirect for old Fable web site.
bagua - Bagua Speeds up PyTorch
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration
fugue - A unified interface for distributed computing. Fugue executes SQL, Python, Pandas, and Polars code on Spark, Dask and Ray without any rewrites.
tvm - Open deep learning compiler stack for cpu, gpu and specialized accelerators
awesome-machine-learning-in-compilers - Must read research papers and links to tools and datasets that are related to using machine learning for compilers and systems optimisation
modin - Modin: Scale your Pandas workflows by changing a single line of code