Interested in learning how you can save power in computer systems, especially with latency critical applications? Learn more about how profile-guided frequency scaling can help solve this problem, with research supervised by Assistant Professor Vinicius Petrucci at the University of Pittsburgh and presented last month at IEEE/ACM CCGrid!
This work is part of George Mason University PhD student Zheng Chai and Prof. Yue Cheng’s research on solving federated learning (FL) bottlenecks for edge devices. Learn more about the authors, their research, and their novel FL training system, FedAT which already has impressive results, improving prediction performance by up to 21.09% and reducing communication cost by up to 8.5 times compared to state-of-the-art FL systems.
This blog feature explores 4th year University of Michigan PhD student Peifeng Yu’s research on hyperparameter tuning, presented earlier this month at MLSys21. Learn more about Yu, the hyperparameter tuning engine, and how it can improve your deep learning model training process.
Learn about using Chameleon to develop automated calibration for cyberinfrastructure research as part of WRENCH research team member's William Koch's Master's thesis. A M.S. student at the University of Hawai`i at Manoa (UHM), Koch explores cyberinfrastructure research, this research project's approach, and his research background in this blog post.
Dr. Mariam Kiran is a research scientist in the Scientific Networking Division, as a member of the Prototypes and Testbed group at ESnet, LBNL, and is leading research efforts in AI solutions for operational network research and engineering problems. In this blog, she discusses her research project DAPHNE (Deep and Autonomous High-speed Networks), her use of Chameleon, and her research background.
January’s User Experiment’s blog features Keivan Bahmani, a PhD candidate at Clarkson University. Learn more about Bahmani and his use of Chameleon for biometric research.
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.
November’s Chameleon User Experiments blog features Nanqinqin Li, a first-year PhD student at Princeton University. Learn more about Li, his summer research on reproducibility and Solid-State Drive Simulators, and learn where to replicate his experiment on Trovi!
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...