Machine Learning at Microsoft with ML .NET

Machine Learning is transitioning from an art and science into a technology available to every developer. In the near future, every application on every platform will incorporate trained models to encode data-based decisions that would be impossible for developers to author. This presents a significant engineering challenge, since currently data science and modeling are largely decoupled from standard software development processes. This separation makes incorporating machine learning capabilities inside applications unnecessarily costly and difficult, and furthermore discourage developers from embracing ML in first place. In this paper we present ML .NET, a framework developed at Microsoft over the last decade in response to the challenge of making it easy to ship machine learning models in large software applications. We present its architecture, and illuminate the application demands that shaped it. Specifically, we introduce DataView, the core data abstraction of ML .NET which allows it to capture full predictive pipelines efficiently and consistently across training and inference lifecycles. We close the paper with a surprisingly favorable performance study of ML .NET compared to more recent entrants, and a discussion of some lessons learned

Signature Matching in Network Processing Using SIMD/GPU Architectures

Deep packet inspection is becoming prevalent for modern network processing systems. They inspect packet payloads for a variety of reasons, including intrusion detection, traffic policing, and load balancing. The focus of this paper is deep packet inspection in intrusion detection/prevention systems (IPSes). The performance critical operation in these systems is signature matching: matching payloads against signatures of vulnerabilities. Increasing network speeds of today?s networks and the transition from simple string-based signatures to complex regular expressions has rapidly increased the performance requirement of signature matching.To meet these requirements, solutions range from hardware-centric ASIC/FPGA implementations to software implementations using high-performance microprocessors. In this paper, we propose a programmable SIMD architecture design for IPSes and develop a prototype implementation on an Nvidia G80 GPU. We first present a detailed architectural and microarchitectural analysis of signature matching. Our analysis shows that signature matching is well suited for SIMD processing because of regular control flow and parallelism available at the packet level. We examine the conventional approach of using deterministic finite automata (DFAs) and a new approach called extended finite automata (XFAs) which require far less memory than DFAs, but require scratch memory and small amounts of computation in each state. We then describe a SIMD design to implement DFAs and XFAs. Using a SIMD architecture provides flexibility, programmability, and design productivity which ASICs lack, while being area and power efficient which superscalar processors lack. Finally, we develop a prototype implementation using the G80 GPU as an example SIMD implementation. This system out-performs a Pentium4 by up to 9X and shows SIMD systems are a promising candidate for signature matching