A Mess of Memory System Benchmarking, Simulation and Application Profiling
<|reference_start|>A Mess of Memory System Benchmarking, Simulation and Application Profiling: The Memory stress (Mess) framework provides a unified view of the memory system benchmarking, simulation and application profiling. The Mess benchmark provides a holistic and detailed memory system characterization. It is based on hundreds of measurements that are represented as a family of bandwidth--latency curves. The benchmark increases the coverage of all the previous tools and leads to new findings in the behavior of the actual and simulated memory systems. We deploy the Mess benchmark to characterize Intel, AMD, IBM, Fujitsu, Amazon and NVIDIA servers with DDR4, DDR5, HBM2 and HBM2E memory. The Mess analytical memory simulator uses bandwidth--latency concept for the memory performance simulation. We integrate Mess with widely-used CPUs simulators enabling modeling of all high-end memory technologies. The Mess simulator is fast, easy to integrate and it closely matches the actual system performance. By design, it enables a quick adoption of new memory technologies in hardware simulators. Finally, the Mess application profiling positions the application in the bandwidth--latency space of the target memory system. This information can be correlated with other application runtime activities and the source code, leading to a better overall understanding of the application's behavior. The current Mess benchmark release covers all major CPU and GPU ISAs, x86, ARM, Power, RISC-V, and NVIDIA's PTX. We also release as open source the ZSim, gem5 and OpenPiton Metro-MPI integrated with the Mess simulator for DDR4, DDR5, Optane, HBM2, HBM2E and CXL memory expanders. The Mess application profiling is already integrated into a suite of production HPC performance analysis tools.<|reference_end|>
@article{esmaili-dokht2024a,
    title={A Mess of Memory System Benchmarking, Simulation and Application
  Profiling},
    author={Pouya Esmaili-Dokht, Francesco Sgherzi, Valeria Soldera Girelli, Isaac
  Boixaderas, Mariana Carmin, Alireza Monemi, Adria Armejach, Estanislao
  Mercadal, German Llort, Petar Radojkovic, Miquel Moreto, Judit Gimenez,
  Xavier Martorell, Eduard Ayguade, Jesus Labarta, Emanuele Confalonieri,
  Rishabh Dubey and Jason Adlard},
    journal={arXiv preprint arXiv:2405.10170},
    year={2024},
    archivePrefix={arXiv},
    eprint={2405.10170},
    primaryClass={cs.AR}
}