REPETO Releases Report on Challenges of Practical Reproducibility for Systems and HPC Computer Science

Findings from the November 2024 Community Workshop on Practical Reproducibility in HPC

View or contribute to the experiment packaging and style checklists (appendix A and B) on our GitHub repository here.

Download the report here.

We’re excited to announce the publication of the NSF-sponsored REPETO Report on Challenges of Practical Reproducibility for Systems and HPC Computer Science, a culmination of our Community Workshop on Practical Reproducibility in HPC, held in November 2024 in Atlanta, GA (reproduciblehpc.org).

Fair-CO2: Fair Attribution for Cloud Carbon Emissions

Understanding and accurately distributing responsibility for carbon emissions in cloud computing

by Leo Han

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.

Faster Multimodal AI, Lower GPU Costs

HiRED: Cutting Inference Costs for Vision-Language Models Through Intelligent Token Selection

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.

Importing GitHub Repositories to Trovi: A Step-by-Step Guide

Streamline Your Research Workflow with Trovi's New GitHub Integration

Learn how to leverage Trovi's new GitHub integration to easily create and update reproducible research artifacts. This step-by-step guide shows you how to configure your GitHub repository with RO-crate metadata and import it directly into Trovi, enabling better collaboration and adherence to FAIR principles for your experiments.

EditLord: Learning Code Transformation Rules for Code Editing

Making code edits more effective, robust, and transparent through explicit transformation rules

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.