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Addressing Challenges of Cross-Architecture Dynamic Migration Over Heterogeneous Acceleration System | 解决异构加速系统上跨架构动态迁移的挑战 – Yanjun Chen, China Mobile
With the surge of application computing demand, the industry began to run AI applications on diverse acceleration hardwares (GPU, FPGA, NPU…) to gain more processing capability. One key problem to use diverse accelerators is tool chain & vendor lock-in in application Dev-to-Run processes. Cross-system (multi-arch chips + multi-vendor tool chain) application development and migration is hard to achieve. In this presentation China Mobile will introduce the practices to solve above challenges allowing AI applications smoothly migrate among different accelerators. It includes a unified abstraction for diverse accelerators, a middle-compiler using existing compilers (CUDA, ROCm, oneAPI…) to achieve cross-architecture compile in the same execution, and a runtime supporting dynamic and replaceable link. We want to enable applications migrate freely between diverse accelerators without changing development habits, and show the architecture design, open source plans and a demo.
随着应用计算需求的激增，行业开始在各种加速硬件（GPU、FPGA、NPU等）上运行AI应用程序，以获得更多的处理能力。在使用各种加速器时，一个关键问题是在应用程序开发到运行过程中的工具链和供应商锁定。跨系统（多架构芯片+多供应商工具链）应用程序开发和迁移很难实现。在这个演示中，中国移动将介绍解决上述挑战的实践，使AI应用程序能够在不同的加速器之间平稳迁移。这包括对各种加速器的统一抽象，使用现有编译器（CUDA、ROCm、oneAPI等）的中间编译器实现跨架构编译在同一执行中，以及支持动态和可替换链接的运行时。我们希望能够使应用程序在不改变开发习惯的情况下自由迁移至各种加速器，并展示架构设计、开源计划和演示。
