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• https://github.com/pnnl/memgaze

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About: As memory systems are the primary bottleneck in many workloads, effective hardware/software co-design requires a detailed understanding of memory behavior. Unfortunately, current analysis of word-level sequences of memory accesses incurs time slowdowns of O(100×).

MemGaze is a memory analysis toolset that combines high-resolution trace analysis and low overhead measurement, both with respect to time and space.

MemGaze provides high-resolution by collecting world-level memory access traces, where the highest resolution supported is back-to-back sequences. In particular, it leverages emerging Processor Tracing support to collect data. It achieves low-overhead in space and time by leveraging sampling and various methods of hardware support for collecting traces.

MemGaze provides several post-mortem trace processing methods, including multi-resolution analysis for locations vs. operations; accesses vs. spatio-temporal reuse, and reuse (distance, rate, volume) vs. access patterns.

Contacts: (firstname.lastname@pnnl.gov)

• Nathan R. Tallent (www), (www)

Contributors:

• Ozgur Kilic (Now BNL)
• Nathan R. Tallent (PNNL) (www), (www)
• Yasodha Suriyakumar (Portland State University)
• Andrés Marquez (PNNL)
• Onur Cankur (University of Maryland)
• Chenhao Xie (PNNL)
• Stephane Eranian (Google)

• Ozgur O. Kilic, Nathan R. Tallent, Yasodha Suriyakumar, Chenhao Xie, Andrés Marquez, and Stephane Eranian, "MemGaze: Rapid and effective load-level memory and data analysis," in Proc. of the 2022 IEEE Conf. on Cluster Computing, IEEE, Sep 2022.

• Ozgur O. Kilic, Nathan R. Tallent, and Ryan D. Friese, "Rapid memory footprint access diagnostics," in Proc. of the 2020 IEEE Intl. Symp. on Performance Analysis of Systems and Software, IEEE Computer Society, May 2020. https://10.1109/ISPASS48437.2020.00047

• Ozgur O. Kilic, Nathan R. Tallent, and Ryan D. Friese, "Rapidly measuring loop footprints," in Proc. of IEEE Intl. Conf. on Cluster Computing (Workshop on Monitoring and Analysis for High Performance Computing Systems Plus Applications), pp. 1--9, IEEE Computer Society, September 2019. https://doi.org/10.1109/CLUSTER.2019.8891025

This work was supported by the U.S. Department of Energy's Office of Advanced Scientific Computing Research:

• Orchestration for Distributed & Data-Intensive Scientific Exploration

• Advanced Memory to Support Artificial Intelligence for Science