Cornell Systems Lunch
CS 7490 Fall 2020
The Systems Lunch is a seminar for discussing recent, interesting papers in the systems area, broadly defined to span operating systems, distributed systems, networking, architecture, databases, and programming languages. The goal is to foster technical discussions among the Cornell systems research community. We meet once a week on Fridays at 11:45 on-line by Zoom.
The systems lunch is open to all Cornell Ph.D. students interested in systems. First-year graduate students are especially welcome. Non-Ph.D. students have to obtain permission from the instructor. Student participants are expected to sign up for CS 7490, Systems Research Seminar, for one credit.
Links to papers and abstracts below are unlikely to work outside the Cornell CS firewall. If you have trouble viewing them, this is the likely cause.
The Zoom link is https://cornell.zoom.us/j/99550502984?pwd=Y28xdlhBSmJKak9TdGRnN3UveWNudz09 (accessible from .cornell.edu and select other domains).
|September 4||Organizational meeting
|September 11||HotStuff: BFT Consensus with Linearity and Responsiveness
Maofan (Ted) Yin (Cornell), Dahlia Malkhi (VMWare), Michael K. Reiter (UNC), Guy Golan Gueta (VMWare), Ittai Abraham (VMWare)
|Haobin Ni (video, slides)|
|September 18||Serverless in the Wild: Characterizing and Optimizing the Serverless Workload at a Large Cloud Provider
Mohammad Shahrad, Rodrigo Fonseca, Íñigo Goiri, Gohar Chaudhry, Paul Batum, Jason Cooke, Eduardo Laureano, Colby Tresness, Mark Russinovich, and Ricardo Bianchini, Microsoft Azure and Microsoft Research
USENIX ATC 20
|Yueying Li and Burcu Canakci (video, slides)|
|September 25||Specification and verification in the field: Applying formal methods
to BPF just-in-time compilers in the Linux kernel
This talk presents our ongoing efforts of applying formal methods to a critical component in the Linux kernel, the just-in-time compilers ("JITs") for the extended Berkeley Packet Filter (BPF). Building on our automated verification framework Serval, we have developed Jitterbug, a tool for writing JITs and proving them correct. We have used Jitterbug to find 30+ new bugs in the BPF JITs for the x86-32, x86-64, arm32, arm64, and riscv64 architectures, and to develop a new BPF JIT for riscv32, RISC-V compressed instruction support for riscv64, and new optimizations in existing JITs. All of these changes have been upstreamed to the Linux kernel.
|Xi Wang (UW)|
|October 2||Computational wireless sensing at scale
Computational wireless sensing is an exciting field of research where we use wireless signals from everyday computing devices to enable sensing. The key challenge is to enable new sensing capabilities that can be deployed at scale and have an impact in the real world. In this talk, I will show how to enable computational wireless sensing at scale by leveraging ubiquitous hardware such as smartphones. Specifically, I will present core technology that can wirelessly sense motion and physiological signals such as breathing using just a smartphone, in a contactless manner. To achieve this, we transform smartphones into active sonar systems. I will show how we can use this technology to detect potentially life-threatening conditions such as opioid overdoses as well as sleep apnea. Finally, I will talk about my work that leverages new hardware trends in micro-controllers and low power wireless backscatter technologies to enable sensing applications ranging from object tracking to sensing using live insects such as bees.
|Rajalakshmi Nandakumar (video)|
|October 9||Scaling AI Systems with Optical I/O
The emergence of optical I/O chiplets enables compute/memory chips to communicate with several Tbps bandwidth. Many technology trends point to the arrival of optical I/O chiplets as a key industry inflection point to realize fully disaggregated systems. In this talk, I will focus on the potential of optical I/O-enabled accelerators for building high bandwidth interconnects tailored for distributed machine learning training. Our goal is to scale the state-of-the-art ML training platforms, such as NVIDIA DGX, from a few tightly connected GPUs in one package to hundreds of GPUs while maintaining Tbps communication bandwidth across the chips. Our design enables accelerating the training time of popular ML models using a device placement algorithm that partitions the training job with data, model, and pipeline parallelism across nodes while ensuring a sparse and local communication pattern that can be supported efficiently on the interconnect.
Bio: Manya Ghobadi is an assistant professor at the EECS department at MIT. Before MIT, she was a researcher at Microsoft Research and a software engineer at Google Platforms. Manya is a computer systems researcher with a networking focus and has worked on a broad set of topics, including data center networking, optical networks, transport protocols, and network measurement. Her work has won the best dataset award and best paper award at the ACM Internet Measurement Conference (IMC) as well as Google research excellent paper award.
|Manya Ghobadi (MIT)|
|October 23||Fast and secure global payments with Stellar
Marta Lokhava, Giuliano Losa, David Mazieres, Graydon Hoare, Nicolas Barry, Eli Gafni, Jonathan Jove, Rafael Malinowsky, and Jed McCaleb (Stellar Development Foundation)
|Florian Suri-Payer and Ted Yin|
|October 30||Approximate Partition Selection for Big-Data Workloads using Summary Statistics
Kexin Rong, Yao Lu, Peter Bailis, Srikanth Kandula, Philip Levis (Stanford and Microsoft)
|Saehan Jo and Junxiong Wang|
|November 6||CrossFS: A Cross-layered Direct-Access File System
Yujie Ren, Rutgers University; Changwoo Min, Virginia Tech; Sudarsun Kannan, Rutgers University
|Yu-Ju Huang and Kevin Negy|
|November 13||Rama Venu (Google)|
|November 20||Semi-final Exams, no meeting.|
|November 27||Thanksgiving Break, no meeting.|