We present a framework for a large-scale distributed eScience Artificial Intelligence search. Our approach is generic and can be used for many different problems. Unlike many other approaches, we do not require dedicated machines, homogeneous infrastructure or the ability to communicate between nodes. We give special consideration to the robustness of the framework, minimising the loss of effort even after total loss of infrastructure, and allowing easy verification of every step of the distribution process. In contrast to most eScience applications, the input data and specification of the problem is very small, being easily given in a paragraph of text. The unique challenges our framework tackles are related to the combinatorial explosion of the space that contains the possible solutions and the robustness of long-running computations. Not only is the time required to finish the computations unknown, but also the resource requirements may change during the course of the computation. We demonstrate the applicability of our framework by using it to solve a challenging and hitherto open problem in computational mathematics. The results demonstrate that our approach easily scales to computations of a size that would have been impossible to tackle in practice just a decade ago.
Serving and Optimizing Machine Learning Workflows on Heterogeneous Infrastructures
Duke University（杜克大学）；Microsoft Research（微软研究院）
随着智能设备和物联网的无处不在的部署，用于机器学习推理的数据源越来越多地移动到网络边缘。现有的机器学习推理平台通常假设同构基础设施，并且不考虑更复杂和分层的计算基础设施，包括边缘设备、本地集线器、边缘计算中心和云计算中心。另一方面，最近的AutoML努力为异构环境的模型压缩、修剪和量化提供了可行的解决方案;对于机器学习模型，现在我们可以很容易地找到甚至生成一系列在准确性和效率之间具有不同权衡的模型。我们设计并实现了JellyBean，一个在异构基础设施上服务和优化机器学习推理工作流的系统。给定服务级别目标（例如，吞吐量、准确性），JellyBean选择符合准确性目标的最具成本效益的模型，并决定如何在不同的基础架构层中部署它们。评估表明，与最先进的模型选择和工人分配解决方案相比，JellyBean将视觉问答的总服务成本降低了58%，将NVIDIA AI城市挑战赛的车辆跟踪成本降低了36%。JellyBean还优于先前的ML服务系统（例如，Spark on the Cloud），服务成本高达5倍。
