Onur Mutlu

Haocong Luo, F. Nisa Bostancı, Ataberk Olgun et al.

Ramulator 2.1 is a major overhaul of Ramulator 2.0 that substantially improves the simulator in three directions: 1) support of modern and emerging DRAM and memory-controller features, 2) better usability and extensibility of the simulator, and 3) more comprehensive tests and validation workflows. Ramulator 2.1 adds support for advanced features in recent and emerging DRAM standards and memory controllers, including HBM3/4, LPDDR5/6, and GDDR7. To improve usability and extensibility, Ramulator 2.1 introduces a Python-based modeling and configuration interface backed by a two-way code-generation framework that 1) hides low-level C++ code behind high-level DRAM specifications written in Python, and 2) automatically creates Python proxies for all components of the simulator. Doing so enables users to rapidly create variants of DRAM standards and automate design-space-exploration workflows. To improve trustworthiness in simulation results, Ramulator 2.1 provides a comprehensive testing and validation infrastructure that covers both 1) fine-grained validation of specific DRAM timing constraints and memory-controller scheduling behavior, and 2) system-level performance evaluation using latency-throughput curves. To aid performance analysis and debugging, Ramulator 2.1 also includes an easy-to-use and high-performance DRAM command trace visualizer. Ramulator 2.1 is open-source on GitHub and under active development.

Ataberk Olgun, Haocong Luo, Ismail Emir Yuksel et al.

Real memory chip characterization yields insights into fundamental operational characteristics of modern memory, enabling new mechanisms that improve memory performance, robustness, security, and energy efficiency. We describe our large-scale DRAM characterization laboratory for understanding DRAM. A key building block of this laboratory is DRAM Bender, a versatile and easy-to-use modern DRAM characterization infrastructure. We have updated DRAM Bender to i) introduce support for new types of characterization experiments, ii) expand on its DRAM interface standard support, and iii) make it easier to use at large scale. This paper introduces these updates for the first time. We hope our infrastructure enables the community to discover new problems and solve critical memory scaling issues, enabling the overcoming of the huge memory bottleneck that plagues modern computing systems.

F. Nisa Bostanci, Haocong Luo, Ataberk Olgun et al.

Cycle-level DRAM simulators provide accurate and flexible models for DRAM and memory controller operations and enable research on current and future memory systems. Therefore, they are critical for improving the performance, efficiency, and robustness of DRAM-based memory systems. Ramulator 2.0 (successor of Ramulator) is a highly modular and extensible cycle-accurate DRAM simulator that enables rapid exploration of new ideas in DRAM-based memory systems. A MICRO 2024 best paper runner-up publication, A Mess of Memory System Benchmarking, Simulation and Application Profiling, which we refer to as "the Mess paper," with all three artifact badges awarded (including "Reproducible"), proposes a new benchmark to evaluate real and simulated memory system performance. While doing so, it makes strong negative claims about Ramulator 2.0 and shows unexpected results. In this talk and the associated extended abstract, we demonstrate that these results and claims in the Mess paper are incorrect and are due to configuration and simulator usage errors made in the Mess paper. We describe four best practices to aid users and developers of simulation tools to avoid such issues in the future. We emphasize the importance of contacting simulator authors and developers when unexpected results are observed (especially and importantly before publishing such results), to ensure these simulators are used with correct configurations and as intended. Our investigation also aims to stimulate discussion on artifact evaluation practices and on mechanisms for correcting results and artifacts after publication. To aid future works and reproduction of all our results, we open source all our code and scripts at https://github.com/CMU-SAFARI/Cleaning-up-the-Mess. We refer the reader to our full ISPASS 2026 paper and its artifact for the complete analysis, detailed methodology, and extended results.