Dr. Xiaoyi Lu is a research assistant professor at The Ohio State University focusing on High Performance Interconnects and Protocols, Big Data Computing, Deep Learning, Parallel Computing, Virtualization, and Cloud Computing. In this blog post, we explore his research and usage of Chameleon Cloud.
This summer, a team of students worked on an experiment that ultimately became part of the LinnOS paper that infers the SSD performance with the help of its built in light neural network architecture. The LinnOS paper, which utilizes Chameleon testbed to provide a public executable workflow, will be presented in OSDI ’20 and is available here.
Two of the students, Levent Toksoz and Mingzhe Hao, write about their experience in this Chameleon User Stories series. Toksoz is a recent graduate of the University of Chicago computer science masters program. He studied physics and math as an undergrad at ...
Motivated by the opportunity to optimize the architecture of data transfer infrastructure, we recently prototyped an elastic architecture for data transfer on Chameleon Cloud in which the DTNs expand and shrink based on the demand...
Exploring custom high-performance drivers in specialized operating systems with an aim to scale HPC applications in order to meet the future needs of exascale computing has motivated us to build a high-performance InfiniBand driver for Nautilus (Aero-Kernel) and evaluate its performance. In this blog, we would like to share our experimental framework and results achieved.
This blog describes a prototype of a system that leverages the capabilities of flexible switches that incorporate protocol-independent packet processing in order to intelligently route traffic based on application headers.
- Tickets of the Year: Solutions to Your 2020 (Ticket) Problems
- Performance Analysis of Deep Learning Workloads Using Roofline Trajectories on Chameleon
- Reproducing Solid State Drive Simulator Research Results on Chameleon
- Trovi: the Google Drive for Chameleon Experiments
- Chameleon and Reproducibility: LinnOS Case Study