May 2019 - talks - lists.cs.princeton.edu

Reminder: Michael Uhlar and Sathya Gunasekar tomorrow at 12:30
by Emily C. Lawrence 02 May '19

02 May '19

Michael Uhlar and Sathya Gunasekar from Facebook Friday, May 3 at 12:30pm Computer Science - Room 104 (large auditorium) [ https://www.cs.princeton.edu/events/25851 | https://www.cs.princeton.edu/events/25851 ] CacheLib - Unifying & Abstracting HW for caching @ Facebook In order to operate with high efficiency, Facebook’s infrastructure relies on caching in many different backend services. These services place very different demands on their caches, e.g., in terms of working set sizes, access patterns, and throughput requirements. Historically, each service used a different cache implementation, leading to inefficiency, duplicated code and effort. CacheLib is an embedded caching engine, which addresses this requirement with a unified API for building a cache implementation across many HW mediums. CacheLib transparently combines volatile and non-volatile storage in a single caching abstraction. To meet the varied demands, CacheLib successfully provides a flexible, high-performance solution for many different services at Facebook. In this talk, we describe CacheLib’s design, challenges, and several lessons learned.

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Ross Teixeira will present his MSE thesis talk “SwitchScope: A View from the Inside” on Wednesday, May 8th, 2019 at 3pm in CS 402.
by Nicki Mahler 01 May '19

01 May '19

Ross Teixeira will present his MSE thesis talk “SwitchScope: A View from the Inside” on Wednesday, May 8th, 2019 at 3pm in CS 402. The members of his committee are Jennifer Rexford (adviser) and David Walker. Everyone is invited to attend his talk. His abstract follows below. Title: “SwitchScope: A View from the Inside” Abstract: Running a modern network requires constant diligence to maintain efficiency and reliability for customers. This is made challenging by the fact that the life of a packet inside each switch is complex, and yet currently quite hard to observe and debug. Packets entering a switch may be dropped (by access control policy or a full queue), or delayed (by a long queue) or modified (by policy) before heading to the next hop. Prior telemetry systems allow network operators to analyze flows that a switch sees, but they do not grapple with the internals of the switch’s multiple pipelines. In this talk, I will discuss our contributions towards SwitchScope, a flexible telemetry system that utilizes programmable (PISA) switches, and which allows debugging switch configurations and determining the source of bottlenecks or lost packets. I will detail our work on an expressive query language that provides network operators insight into the life of packets in a switch, as well as our compilation strategies to support useful queries about lost packets while minimizing the use of switch resources.

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PACM COLLOQUIUM - Monday, May 6 - Yuxin Chen, Princeton University
by Emily Lawrence 01 May '19

01 May '19

DATE/LOCATION: Monday, May 6, 2019, 4:00 – 5:00 PM / 214 Fine Hall SPEAKER: Yuxin Chen (Princeton University) TITLE: Bridging convex and nonconvex optimization in noisy matrix completion: Stability and uncertainty quantification ABSTRACT: This talk is concerned with noisy matrix completion: given partial and corrupted entries of a large low-rank matrix, how to estimate and infer the underlying matrix? Arguably one of the most popular paradigms to tackle this problem is convex relaxation, which achieves remarkable efficacy in practice. However, the statistical stability guarantees of this approach is still far from optimal in the noisy setting, falling short of explaining the empirical success. Moreover, it is generally very challenging to pin down the distributions of the convex solution, which presents a major roadblock in assessing the uncertainty, or “confidence”, for the obtained estimates --- a crucial task at the core of statistical inference. Our recent work makes progress towards understanding stability and uncertainty quantification for matrix completion. When the rank of the unknown matrix is a constant: (1) we demonstrate that convex programming achieves near-optimal estimation errors vis-a-vis random noise; (2) we develop a de-biased estimator that admits accurate distributional characterizations, thus enabling asymptotically optimal inference. All of this is enabled by bridging convex relaxation with the nonconvex Burer-Monteiro approach, a seemingly distinct algorithmic paradigm that is provably robust against noise. This is joint work with Cong Ma, Yuling Yan, Yuejie Chi, and Jianqing Fan.

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