kompute

Blazing fast, mobile-enabled, asynchronous, and optimized for advanced GPU acceleration usecases.

Join the Discord & Community Calls Documentation Blog Post ⌨ Examples

Kompute is backed by the Linux Foundation as a hosted project by the LF AI & Data Foundation.

Principles & Features

• Flexible Python module with C++ SDK for optimizations
• Asynchronous & parallel processing support through GPU family queues
• Mobile enabled with examples via Android NDK across several architectures
• BYOV: Bring-your-own-Vulkan design to play nice with existing Vulkan applications
• Explicit relationships for GPU and host memory ownership and memory management
• Robust codebase with 90% unit test code coverage
• Advanced use-cases on machine learning , mobile development and game development .
• Active community with monthly calls, discord chat and more

Projects using Kompute ❤️

• GPT4ALL - An ecosystem of open-source on-edge large language models that run locally on your CPU and nearly any GPU.
• llama.cpp - Port of Facebook's LLaMA model in C/C++ (now decomissioned).
• tpoisonooo/how-to-optimize-gemm - row-major matmul optimization.
• vkJAX - JAX interpreter for Vulkan.

Getting Started

Below you can find a GPU multiplication example using the C++ and Python Kompute interfaces.

You can join the Discord for questions / discussion, open a github issue, or read the documentation.

Your First Kompute (C++)

The C++ interface provides low level access to the native components of Kompute, enabling for advanced optimizations as well as extension of components.

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Your First Kompute (Python)

The Python package provides a high level interactive interface that enables for experimentation whilst ensuring high performance and fast development workflows.

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Interactive Notebooks & Hands on Videos

You are able to try out the interactive Colab Notebooks which allow you to use a free GPU. The available examples are the Python and C++ examples below:

You can also check out the two following talks presented at the FOSDEM 2021 conference.

Both videos have timestamps which will allow you to skip to the most relevant section for you - the intro & motivations for both is almost the same so you can skip to the more specific content.

Architectural Overview

The core architecture of Kompute includes the following:

• Kompute Manager - Base orchestrator which creates and manages device and child components
• Kompute Sequence - Container of operations that can be sent to GPU as batch
• Kompute Operation (Base) - Base class from which all operations inherit
• Kompute Tensor - Tensor structured data used in GPU operations
• Kompute Algorithm - Abstraction for (shader) logic executed in the GPU

To see a full breakdown you can read further in the C++ Class Reference.

Asynchronous and Parallel Operations

Kompute provides flexibility to run operations in an asynrchonous way through vk::Fences. Furthermore, Kompute enables for explicit allocation of queues, which allow for parallel execution of operations across queue families.

The image below provides an intuition on how Kompute Sequences can be allocated to different queues to enable parallel execution based on hardware. You can see the hands on example, as well as the detailed documentation page describing how it would work using an NVIDIA 1650 as an example.

Mobile Enabled

Kompute has been optimized to work in mobile environments. The build system enables for dynamic loading of the Vulkan shared library for Android environments, together with a working Android NDK wrapper for the CPP headers.

More examples

Simple examples

• Simple multiplication example
• Record batch commands with a Kompute Sequence
• Run Asynchronous Operations
• Run Parallel Operations Across Multiple GPU Queues
• Create your custom Kompute Operations
• Implementing logistic regression from scratch

End-to-end examples

• Machine Learning Logistic Regression Implementation
• Parallelizing GPU-intensive Workloads via Multi-Queue Operations
• Android NDK Mobile Kompute ML Application
• Game Development Kompute ML in Godot Engine

Python Package

Besides the C++ core SDK you can also use the Python package of Kompute, which exposes the same core functionality, and supports interoperability with Python objects like Lists, Numpy Arrays, etc.

The only dependencies are Python 3.5+ and Cmake 3.4.1+. You can install Kompute from the Python pypi package using the following command.

pip install kp

You can also install from master branch using:

pip install git+git://github.com/KomputeProject/kompute.git@master

For further details you can read the Python Package documentation or the Python Class Reference documentation.

C++ Build Overview

The build system provided uses cmake, which allows for cross platform builds.

The top level Makefile provides a set of optimized configurations for development as well as the docker image build, but you can start a build with the following command:

   cmake -Bbuild

You also are able to add Kompute in your repo with add_subdirectory - the Android example CMakeLists.txt file shows how this would be done.

For a more advanced overview of the build configuration check out the Build System Deep Dive documentation.

Kompute Development

We appreciate PRs and Issues. If you want to contribute try checking the "Good first issue" tag, but even using Kompute and reporting issues is a great contribution!

Contributing

Dev Dependencies

• Testing
  • GTest
• Documentation
  • Doxygen (with Dot)
  • Sphynx

Development

• Follows Mozilla C++ Style Guide https://www-archive.mozilla.org/hacking/mozilla-style-guide.html
  • Uses post-commit hook to run the linter, you can set it up so it runs the linter before commit
  • All dependencies are defined in vcpkg.json
• Uses cmake as build system, and provides a top level makefile with recommended