# spu

**Repository Path**: yyjb5/spu

## Basic Information

- **Project Name**: spu
- **Description**: 隐语spu仓库mirror自github
- **Primary Language**: C++
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: https://spu.readthedocs.io/
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 3
- **Created**: 2023-01-14
- **Last Updated**: 2023-01-14

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# SPU: Secure Processing Unit

[![CircleCI](https://dl.circleci.com/status-badge/img/gh/secretflow/spu/tree/main.svg?style=svg)](https://dl.circleci.com/status-badge/redirect/gh/secretflow/spu/tree/main)

SPU (Secure Processing Unit) aims to be a `provable`, `measurable` secure computation device, which provides computation ability while keeping your private data protected.

SPU could be treated as a programmable device, it's not designed to be used directly. Normally we use SecretFlow framework, which use SPU as the underline secure computing device.

Currently, we mainly focus on `provable` security. It contains a secure runtime that evaluates [XLA](https://www.tensorflow.org/xla/operation_semantics)-like tensor operations, which use [MPC](https://en.wikipedia.org/wiki/Secure_multi-party_computation) as the underline evaluation engine to protect privacy information.

SPU python package also contains a simple distributed module to demo SPU usage, but it's **NOT designed for production** due to system security and performance concerns, please **DO NOT** use it directly in production.

## Contribution Guidelines

If you would like to contribute to SPU, please check [Contribution guidelines](CONTRIBUTING.md).

This documentation also contains instructions for [build and testing](CONTRIBUTING.md#build).

## Installation Guidelines

Please follow [Installation Guidelines](INSTALLATION.md) to install SPU.

## Acknowledgement

We thank the significant contributions made by [Alibaba Gemini Lab](https://alibaba-gemini-lab.github.io).