Traditional distributed databases are often slowed down by network communication overhead. The Tigon project introduces a new database design that tackles this bottleneck using Compute Express Link (CXL), a technology that allows multiple computer hosts to access a shared memory pool. Tigon employs a hybrid approach, keeping most data in fast, local host memory while moving only actively shared data to the CXL memory. This results in significant performance gains, achieving up to 2.5 times higher throughput than traditional databases. Since the multi-host CXL hardware required for this research was not yet commercially available, the project was brought to life …

Cloud infrastructures are powerful but often operate as isolated islands. See how Germán Moltó and his team are bridging these gaps, enabling massive, collaborative scientific experiments by connecting clouds across the Atlantic using the Infrastructure Manager to dynamically deploy custom testbeds.

IT infrastructure forms the backbone of modern society, but traditional scaling approaches face critical limitations that expose services to security and reliability shortcomings. This research investigates Statistical Multiplexed Computing (SMC) principles to build infrastructures without scaling limits, similar to how TCP/IP protocols enabled indefinite network scaling.

Teaching cloud computing effectively requires hands-on experience, but establishing local datacenters or using commercial cloud providers presents significant barriers for students. Chameleon Cloud provides the perfect solution, offering real cloud infrastructure experience without access limitations or costs, enabling comprehensive cloud computing education across European universities.

Leo Han, a second-year Ph.D. student at Cornell Tech, conducted pioneering research on the fair attribution of cloud carbon emissions, resulting in the development of Fair-CO2. Enabled by the unique bare-metal capabilities and flexible environment of Chameleon Cloud, this work tackles the critical issue of accurately distributing responsibility for carbon emissions in cloud computing. This research underscores the potential of adaptable testbeds like Chameleon in advancing sustainability in technology.

High-resolution Vision-Language Models (VLMs) offer impressive accuracy but come with significant computational costs—processing thousands of tokens per image can consume 5GB of GPU memory and add 15 seconds of latency. The HiRED (High-Resolution Early Dropping) framework addresses this challenge by intelligently selecting only the most informative visual tokens based on attention patterns. By keeping just 20% of tokens, researchers achieved a 4.7× throughput increase and 78% latency reduction while maintaining accuracy across vision tasks. This research, conducted on Chameleon's infrastructure using RTX 6000 and A100 GPUs, demonstrates how thoughtful optimization can make advanced AI more accessible and affordable.

In this interview, Weichen Li, a PhD student from the University of Chicago discusses research on improving code editing through explicit transformation rules. EditLord breaks down the code editing process into clear, step-by-step transformations, significantly enhancing editing performance, robustness, and functional correctness compared to existing methods.

Scientific workflows often fail in unexpected ways, but traditional detection systems require massive amounts of training data. This groundbreaking approach generates just the right data needed to train anomaly detection models, improving accuracy while reducing resource consumption.

The AutoAppendix project evaluates computational artifact reproducibility across SC24 conference submissions, revealing that most researchers struggle with creating truly replicable experiments despite their importance to scientific validity. By developing one-click reproduction templates for the Chameleon Cloud platform, this research aims to transform how computational scientists share and validate their work, potentially saving countless hours of frustration for both authors and reviewers.

Processing large-scale genomics data efficiently is a monumental task, often hindered by high costs and resource allocation challenges. This blog dives into an innovative system designed to optimize genomics workflows by minimizing out-of-memory failures—a critical bottleneck in such operations. Through a combination of scalable benchmarking tools and a failure-aware scheduler, researchers are unlocking new possibilities for resource efficiency and reliability. Leveraging insights from Chameleon, this solution paves the way for groundbreaking advancements in genomic data processing.