SAFARI Research Group at ETH Zurich and Carnegie Mellon University
- 923 followers
- ETH Zurich and Carnegie Mellon University
- https://safari.ethz.ch/
- omutlu@gmail.com
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Repositories
- DPC4 Public
The GitHub repository containing all resources used in the 4th Data Prefetching Championship (DPC4), co-located with HPCA 2026.
- Virtuoso Public
Virtuoso is a fast, accurate and versatile simulation framework designed for virtual memory research. Virtuoso uses a new simulation methodology for estimating OS overheads and models diverse VM designs, incorporating state-of-the-art TLB techniques, page table structures etc. More details in our ASPLOS 2025 paper: https://arxiv.org/pdf/2403.04635
- DRAM-Bender-Prebuilt-Bitstreams Public
This repository stores prebuilt bitstreams for DRAM Bender separately from the main DRAM Bender repository at https://github.com/CMU-SAFARI/DRAM-Bender.
- HBM-Power Public
An accurate power model for high bandwidth memory (HBM) building on detailed power consumption characterization of 36 real HBM2 stacks
- Terracotta Public
- Hermes Public
A speculative mechanism to accelerate long-latency off-chip load requests by removing on-chip cache access latency from their critical path, as described by MICRO 2022 paper by Bera et al. (https://arxiv.org/pdf/2209.00188.pdf)
- ramulator2 Public
Ramulator 2.0 is a modern, modular, extensible, and fast cycle-accurate DRAM simulator. It provides support for agile implementation and evaluation of new memory system designs (e.g., new DRAM standards, emerging RowHammer mitigation techniques). Described in our IEEE CAL 2023 paper: https://arxiv.org/pdf/2308.11030.
- MORDOR Public
- ramulator Public
A Fast and Extensible DRAM Simulator, with built-in support for modeling many different DRAM technologies including DDRx, LPDDRx, GDDRx, WIOx, HBMx, and various academic proposals. Described in the IEEE CAL 2015 paper by Kim et al. at http://users.ece.cmu.edu/~omutlu/pub/ramulator_dram_simulator-ieee-cal15.pdf
- Harmonia Public
Harmonia is a lightweight multi-agent reinforcement learning framework that jointly optimizes data placement and migration in hybrid storage systems. Harmonia uses two coordinated RL agents to adapt to workload and storage-device conditions. More details in our ICS 2026 paper: https://arxiv.org/pdf/2503.20507
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