In this work, the case is made for a wholistic top-down re-envisioning of the system stack from the programming language level down through the system architecture to bridge this complexity gap. The key goal of our design is to address the critical need for the programmer to articulate solutions with higher level abstractions at the problem level while having the runtime system stack subsume and hide a broad scope of diffuse sub-applications and inter-machine resources. This work also presents the design of a production-grade realization of such a system stack architecture called Jaseci, and corresponding programming language Jac. Jac and Jaseci has been released as open source and has been leveraged by real product teams to accelerate developing and deploying sophisticated AI products and other applications at scale. Jac has been utilized in commercial production environments to accelerate AI development timelines by ~10x, with the Jaseci runtime automating the decisions and optimizations typically falling in the scope of manual engineering roles on a team such as what should and should not be a microservice and changing those dynamically.
翻译:在这项工作中,从编程语言层面到系统架构,对系统堆叠进行全方位自上而下的自上而下重新翻版,以弥合这一复杂差距。我们设计的关键目标是解决程序设计者在问题层面以更高层次的抽取形式阐述解决方案的迫切需要,同时让运行时间系统堆叠层进行覆盖并隐藏广泛的扩散子应用程序和机器间资源。这项工作还提出设计一种生产级的系统堆叠结构(Jaseci)和相应的编程语言Jac。Jac和Jaseci已经作为开放源发布,并被实际产品团队用来加速开发和部署先进的AI产品和其他规模应用。Jac在商业生产环境中被利用,以加快使用~10x的人工智能开发时间,Jaseci运行时间将决定和优化通常属于团队人工工程任务的范围,例如什么应该而且不应该是微观服务,并动态地改变这些任务。