We are thrilled to announce that Chameleon, our experimental testbed for computer science research, has been awarded $12 million in funding from the U.S. National Science Foundation (NSF) for its fourth phase. Four more years of Chameleon!
Rethinking Memory Management for Multi-Tiered Systems
Exploring Efficient Page Profiling and Migration in Large Heterogeneous Memory
Explore the cutting-edge research of Professor Dong Li from UC Merced as he tackles the challenges of managing multi-tiered memory systems. Learn how his innovative MTM (Multi-Tiered Memory Management) system optimizes page profiling and migration in large heterogeneous memory environments. Discover how Chameleon's unique hardware capabilities enabled this groundbreaking experiment, and gain insights into the future of high-performance computing memory management. This blog offers a glimpse into the complex world of computer memory hierarchies and how researchers are working to make them more efficient and accessible.
Expanding Horizons with CHI@Edge: New Peripheral Support
Enhancing Edge Computing Research with Advanced Sensors and Cameras
This blog post introduces the latest advancements in peripheral support for the CHI@Edge research testbed. It highlights the platform's holistic approach to integrating a wide range of sensors and cameras, opening up new possibilities for edge computing experiments. The post covers recent updates to documentation and tutorials, showcasing specific peripherals like the Waveshare Sense HAT-B and the Pi Camera Module 3. It also provides real-world examples of edge computing applications in fields such as precision agriculture and marine biology. Researchers are guided through the process of utilizing these new capabilities, with links to comprehensive tutorials on GPIO, sensors, and camera integration. This update represents a significant step forward in CHI@Edge's mission to facilitate cutting-edge research in edge computing and IoT applications.
Chameleon Changelog for June 2024
This month, we are preparing for a big upgrade on July 15th. Additionally, we have CHI-in-a-box for edge sites, new hardware at CHI@UC, and improvements to project management.
Real-time Scheduling for Time-Sensitive Networking: A Systematic Review and Experimental Study
Optimizing Network Performance with Chameleon's Computing Power
In this study, Chuanyu Xue tackles the complex challenge of optimizing Time-Sensitive Networking (TSN) for real-world applications. Using Chameleon's powerful computing resources, he conducts a comprehensive evaluation of 17 scheduling algorithms across 38,400 problem instances. This research not only sheds light on the strengths and weaknesses of various TSN scheduling methods but also demonstrates how large-scale experimentation can drive advancements in network optimization. Readers will gain insights from Xue's journey, including key findings, implementation challenges, and valuable tips for leveraging Chameleon in their own research.
Power Measurement and Management on Chameleon
Exploring Power Monitoring Techniques with RAPL, DCMI, and Scaphandre
Monitoring power consumption is crucial for understanding the energy efficiency of your applications and systems. In this post, we explore various techniques for measuring power usage on Chameleon nodes, including leveraging Intel's RAPL interface for fine-grained CPU and memory power data, utilizing IPMI's DCMI commands for system-level power information, and employing the Scaphandre tool for detailed per-process power monitoring and visualization. We provide practical examples and step-by-step instructions to help you get started with power measurement on Chameleon, enabling you to gain valuable insights into the energy footprint of your workloads.
Chameleon Changelog for May 2024
This month we’ve again been focusing on improvements to the Chameleon backend, but we still are excited to show our CHI@Edge peripheral guide and new CHI-in-a-box features. We also are preparing for an OpenStack upgrade on July 1st.
Optimizing Production ML Inference for Accuracy and Cost Efficiency
Pushing the Boundaries of Cost-Effective ML Inference on Chameleon Testbed
In this blog post, we explore groundbreaking research on optimizing production ML inference systems to achieve high accuracy while minimizing costs. A collaboration between researchers from multiple institutions has resulted in the development of three adaptive systems - InfAdapter, IPA, and Sponge - that tackle the accuracy-cost trade-off in complex, real-world ML scenarios. Learn how these solutions, implemented on the Chameleon testbed, are pushing the boundaries of cost-effective ML inference and enabling more accessible and scalable ML deployment.
Chameleon Team is Hiring! Apply Now
Seeking out a talented Senior Research Software Engineer to boost our team
Discover the impact you can make in scientific research as a Senior Research Software Engineer on the Nimbus team. Join us to innovate and transform science on the Chameleon platform!
Seamless SSH Container Access with CHI@Edge
Simplifying Your Development Process
Recognizing the need for easier modification of running containers, we’ve introduced a straightforward method for SSHing into CHI@Edge containers, enhancing your development and experimentation processes. Our new tutorial on Trovi helps you create an SSH-ready base container, extending the CHI@Edge “hello world” example to enable remote access. This allows you to troubleshoot, configure, and update containers as if they were real servers. Access the tutorial via the CHI@Edge dashboard, and start developing with enhanced flexibility and ease.