imate, short for Implicit Matrix Trace Estimator, is a modular and high-performance C++/CUDA library distributed as a Python package that provides scalable randomized algorithms for the computationally expensive matrix functions in machine learning.

Links

• Documentation
• PyPI
• Anaconda
• Docker Hub
• Github

Install

Install with pip

pip install imate

Install with conda

conda install -c s-ameli imate

Docker Image

docker pull sameli/imate

Supported Platforms

Successful installation and tests performed on the following operating systems, architectures, and Python and PyPy versions:

Platform	Arch	Device	Python Version				PyPy Version 1			Continuous Integration
			3.9	3.10	3.11	3.12	3.8	3.9	3.10
Linux	X86-64	CPU	✔	✔	✔	✔	✔	✔	✔
		GPU	✔	✔	✔	✔	✔	✔	✔
	AARCH-64	CPU	✔	✔	✔	✔	✔	✔	✔
		GPU	✔	✔	✔	✔	✔	✔	✔
macOS	X86-64	CPU	✔	✔	✔	✔	✔	✔	✔
		GPU 2	✖	✖	✖	✖	✖	✖	✖
	ARM-64	CPU	✔	✔	✔	✔	✖	✔	✔
		GPU 2	✖	✖	✖	✖	✖	✖	✖
Windows	X86-64	CPU	✔	✔	✔	✔	✖	✖	✖
		GPU	✔	✔	✔	✔	✖	✖	✖

Python wheels for imate for all supported platforms and versions in the above are available through PyPI and Anaconda Cloud. If you need imate on other platforms, architectures, and Python or PyPy versions, raise an issue on GitHub and we build its Python Wheel for you.

^{1. Our wheels for PyPy are exclusively available through pip and cannot be installed using conda.}

^{2. MacOS does not natively support NVIDIA GPUs.}

Supported GPU Architectures

imate can run on CUDA-capable multi-GPU devices. Using the docker container is the easiest way to run imate on GPU devices. The supported GPU micro-architectures and CUDA version are as follows:

Version \ Arch	Fermi	Kepler	Maxwell	Pascal	Volta	Turing	Ampere	Hopper
CUDA 9	✖	✖	✖	✖	✖	✖	✖	✖
CUDA 10	✖	✔	✔	✔	✔	✔	✔	✔
CUDA 11	✖	✖	✖	✔	✔	✔	✔	✔
CUDA 12	✖	✖	✖	✔	✔	✔	✔	✔

Documentation

See documentation, including:

• What This Packages Does?
• Comprehensive Installation Guide
• How to Work with Docker Container?
• How to Deploy on GPU Devices?
• API Reference
• Interactive Notebook Tutorials
• Publications

Performance

imate is scalable to very large matrices. Its core library for basic linear algebraic operations is faster than OpenBLAS, and its pseudo-random generator is a hundred-fold faster than the implementation in the standard C++ library.

Read about the performance of imate in practical applications:

• Performance on GPU Farm
• Comparison of Randomized Algorithms
• Comparison With and Without OpenBLAS
• Interpolation of Affine Matrix Functions

How to Contribute

We welcome contributions via GitHub's pull request. If you do not feel comfortable modifying the code, we also welcome feature requests and bug reports as GitHub issues.

How to Cite

If you publish work that uses imate, please consider citing the manuscripts available here.

License

This project uses a BSD 3-clause license, in hopes that it will be accessible to most projects. If you require a different license, please raise an issue and we will consider a dual license.