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README
Apache-2.0

前言

cu-bridge 尝试成为GPU程序员使用一种编程语言进行开发、并且可运行在多种GPU硬件平台的解决方案。

使GPU程序员沉浸在编程体验和算法创造的大千世界里

本系列项目将持续扩展和更新

欢迎大家Star关注,一起尝试更多的可能!

背景

行业现状

  • 数字经济时代,算力成为新的生产力,CPU加GPU的异构计算系统是算力中心的主流配置,其中大部分算力由GPU提供。
  • GPU作为算力的基石,CPU加GPU的异构计算也就成为了众多GPU厂商的角斗场,各个厂商陆续推出了自己的异构计算软件生态,从最初NVIDIA公司的一家独大,进入到目前的百花齐放。
  • GPU主流编程语言和技术包括CUDAOpenCLROCm+HIPSYCLSPIR-VVulkanMXMACA等,分别应用于不同厂商和不同架构的GPU异构硬件平台。

开发困境

  • GPU程序员开发一个GPU应用程序,如果需要运行到不同厂商的GPU异构硬件平台,就需要重新编写GPU程序代码、并进行项目工程重新构建以及各种调试验证。
  • 异构计算平台上的应用程序,其源代码项目即包含借助GPU加速异构计算代码,也包含CPU控制和计算部分代码;有些应用的项目工程构建由非常多的互相调用的子工程共同组成,一些用于生成库文件,一些用于实现逻辑功能,相互之间的调用关系复杂而严格。
  • 不同异构计算平台的硬件有很多不同特性,软件接口也有很大的差别。GPU程序员需要把同一个应用程序的开发任务,分别适配到不同厂商的GPU异构平台,对大多数程序员来说都是一个非常痛苦和抓狂的过程。

目标

  • cu-bridge 让GPU程序员只需要用CUDA编程语言开发一个应用程序,然后轻松地生成可以在不同厂商的GPU异构硬件平台上可以运行的目标文件。
  • 首先使用CUDA编程语言开发应用程序,通过cu-bridge生成在MXMACA异构硬件平台上可以运行的目标文件。MXMACA是一个进入异构计算软件生态圈的一门新兴GPU编程语言,如果cu-bridge能够在MXMACA上实验成功,cu-bridge通往其它更加成熟GPU编程语言的桥梁也必然可以打造成功。

实现简介/原理

详见实现原理

项目环境

  • Ubuntu 18.04

使用指南

详见使用指南

参考示例

详见参考示例

版本说明

TAG 说明
0.0.1 想法和方案设计。
1.0.0 支持CUDA Runtime API
1.0.1 支持CUDA Driver API
1.0.2 支持CUDA 部分头文件(Part1)
1.0.3 支持CUDA 部分头文件(Part2)
1.0.4 支持CUDA 部分头文件(Part3)
2.0.0 支持CUDA 部分加速库(Part1)
2.0.1 支持CUDA 部分加速库(Part2)
2.0.2 支持CUDA 部分加速库(Part3)

开发任务列表

  1. 支持CUDA 剩余头文件
  2. 持续更新...

开源协议

基于Apache-2.0开源协议

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cu-bridge 尝试成为GPU程序员使用一种编程语言进行开发、并且可运行在多种GPU硬件平台的解决方案。 展开 收起
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