TOOLS

Open Source

Tools & Resources

Open-source frameworks, tutorials, talks, training courses, and datasets from our research.

The Showroom

Our Tools

Mess Framework logo

Mess Framework

Memory System Stress Framework

A unified framework for memory system benchmarking, simulation, and application profiling. The Mess Framework encompasses multiple tools that work together to provide holistic memory system characterization.

Components

Mess Benchmark logo

Mess Benchmark

v2.0

Memory System Stress Framework

Unified benchmarking, simulation and profiling across x86, ARM, RISC-V, and GPU architectures. Now faster and more portable in v2.0

Mess-Paraver logo

Mess-Paraver

Tool

Mess Integration with Paraver

Utility to use Mess together with the BSC's Paraver for advanced memory system analysis and visualization.

Mess Simulator logo

Mess Simulator

Simulator

Memory System Simulator

Advanced simulation framework for modeling memory systems in HPC and AI workloads.

PROFET logo

PROFET

Stable

Performance & Energy Prediction

Analytical models predicting application performance and energy changes on future memory systems.

FAiNDER logo

FAiNDER

AI Hardware Explorer

Open-source platform to navigate AI model requirements and optimize hardware choices.

P

Prediction of DRAM Errors in the field

DRAM Error Prediction and Mitigation

Advanced tools for predicting and mitigating uncorrected errors in DRAM systems, focusing on next-generation HPC and AI requirements.

Components

U

UEPREDICT

DRAM Error Prediction

Method for predicting DRAM Uncorrected Errors and evaluating its model's performance. Our work focuses on advanced memory technologies and memory requirements of next generation HPC and AI systems.

U

UERL

Adaptive UE Mitigation with RL

Source code for an adaptive mitigation method designed to address uncorrected errors (UEs) in DRAM. Leveraging Reinforcement Learning (RL) techniques, the method dynamically adapts to the probability and potential cost of encountering such errors, offering a proactive approach to mitigate their impact. This is the supplemental code for the HPDC24 paper 'Reinforcement Learning-based Adaptive Mitigation of Uncorrected DRAM Errors in the Field'.