XPS: EXPL: Hippogriff: Efficient Heterogeneous Servers for Data Centers and Cloud Services

人工智能、网络服务和云存储的重要性日益增长，推动了构建可以同时执行许多操作的强大计算机系统的需求。 构建具有不同种类的计算处理器的计算机（即，异构处理）是实现这一目标的有效途径。然而，这种方法也产生了新的问题，可能会抵消它提供的一些好处。 特别是，为不同的任务使用不同的处理器需要在这些处理器之间移动数据。 这种移动需要时间，并且可能会抵消异构处理所提供的节省。该项目通过使数据在不同处理器之间的移动更有效来解决异构计算系统中的这个问题。 这种提高的效率直接导致科学和商业重要性的应用程序的好处。异构计算系统的数据移动的大部分成本源于根深蒂固的中央处理器（CPU）为中心的编程模型。 该项目正在重新审视应用程序接口、系统软件和硬件组件的设计，以从移动数据的关键路径中删除CPU和主存储器。 该项目提供了一个高效的编程模型，允许系统软件栈自动有效地设置异构处理器之间的数据移动。我们正在将该系统应用于大型数据库系统，Spark和MapReduce等大规模并行编程系统以及为重要的日常应用和研究项目提供动力的科学计算。

The growing importance of artificial intelligence, network services, and cloud storage drives the demand of building powerful computer systems that can perform many operation at once. Building computers with different kinds of computing processors (i.e., heterogeneous processing) is an effective way to achieve this goal. However, this approach also creates new problems that can negate some of the benefits it provides. In particular, using different processors for different tasks requires moving data between those processors. This movement takes time and can cancel out saving heterogeneous processing provides. This project is addressing this problem in heterogeneous computing systems by making the movement of data between different processors more efficient. This improved efficiency leads directly to benefits for applications of scientific and commercial importance.Much of the cost of data movement heterogeneous computing systems stems from the entrenched central processing unit (CPU)-centric programming model. This project is revisiting the design of the application interface, system software and hardware components to remove CPUs and main memory from the critical path of moving data. The project provides an efficient programming model that allows the system software stack to automatically and efficiently setup the data movements between heterogeneous processors. We are applying the system to large-scale database systems, massive parallel programming systems like Spark and MapReduce as well as scientific computing that power important daily applications and research projects.