Source code for the architectural simulator used for modeling the PUD system proposed in our HPCA 2024 paper `MIMDRAM: An End-to-End Processing-Using-DRAM System for High-Throughput, Energy-Efficient and Programmer-Transparent Multiple-Instruction Multiple-Data Processing''. Paper is at: https://arxiv.org/pdf/2402.19080.pdf

PaCRAM is a technique that reduces the performance overhead of RowHammer mitigation mechanisms by carefully reducing the preventive refresh latency that are issued by RowHammer mitigations without compromising system security.

PIM-ML is a benchmark for training machine learning algorithms on the UPMEM architecture, which is the first publicly-available real-world processing-in-memory (PIM) architecture. Described in the ISPASS 2023 paper by Gomez-Luna et al. (https://arxiv.org/pdf/2207.07886.pdf).

A customizable hardware prefetching framework using online reinforcement learning as described in the MICRO 2021 paper by Bera et al. (https://arxiv.org/pdf/2109.12021.pdf).

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 paper https://people.inf.ethz.ch/omutlu/pub/Ramulator2_arxiv23.pdf

RawHash can accurately and efficiently map raw nanopore signals to reference genomes of varying sizes (e.g., from viral to a human genomes) in real-time without basecalling. Described by Firtina et al. (published at https://academic.oup.com/bioinformatics/article/39/Supplement_1/i297/7210440).

MegIS is the first in-storage processing system designed to significantly reduce the data movement overhead of the end-to-end metagenomic analysis pipeline. Described in the ISCA 2024 paper by Mansouri Ghiasi et al.: https://arxiv.org/pdf/2406.19113

BreakHammer is a technique that reduces the performance overhead of RowHammer mitigation mechanisms by carefully reducing the number of performed RowHammer-preventive actions without compromising system robustness. Described in the MICRO 2024 paper: https://arxiv.org/abs/2404.13477.

PyGim is the first runtime framework to efficiently execute Graph Neural Networks (GNNs) on real Processing-in-Memory systems. It provides a high-level Python interface, currently integrated with PyTorch, and supports various GNN models and real-world input graphs. Described by SIGMETRICS'25 by Giannoula et al. (https://arxiv.org/pdf/2402.16731)

Source code & scripts for distributed machine learning training workloads on a real-world Processing-In-Memory system (i.e., UPMEM). Described in our PACT'24 paper by Rhyner et al. at https://arxiv.org/pdf/2404.07164v2