As GPU vendors supporting heterogeneous architectures continue to multiply, their software and hardware ecosystems remain fragmented—effectively forming isolated “silos.” Rather than forcing tight hardware-level co-execution (“hard coordination”), the path forward lies in software-level interoperability: empowering flexible deployment and efficient scheduling of heterogeneous inference workloads through a universal, vendor-agnostic communication library.

Recently, I’ve taken some time to review recent research papers to explore emerging trends in eBPF and reflect on its evolving landscape. In this article, I focus on the growing trend of using eBPF for system acceleration. Specifically, I will discuss three key areas:

• Network performance optimization,
• System I/O acceleration, and
• Observability with minimal overhead.

With the rapid development of machine learning and deep learning, model management and deployment have become important and complex challenges. To simplify this process, we introduce OneInfer, an open-source command-line tool designed to help users efficiently manage and serve various types of machine learning models.

AI革命提升了计算需求，加速基础设施建设，但传统网络技术的局限显现。业界需更先进网络解方案以支撑AI性能，UEC联盟于2023年成立，致力于开发高性能、开放的通信架构，以满足AI与HPC的网络需求，引领未来网络技术革新。

简要介绍了Actor-Critic类强化学习算法的发展历程，从传统的策略梯度方法到AC、A2C，再到并发异步的A3C算法，探讨了它们如何逐步优化以提高训练效率和模型性能。