MemGaze

Home:

• https://github.com/pnnl/memgaze

• Performance Lab for EXtreme Computing and daTa

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.

Memgaze now includes MemFriend, a new analysis module that introduces spatial and temporal locality analysis that captures affinity (access correlation) between pairs of memory locations. MemFriend's multi-resolution analysis identifies significant memory segments and simultaneously prunes the analysis space such that time and space complexity is modest. MemFriend creates signatures, selectable at 3D, 2D, and 1D resolutions, that provide novel insights and enable predictive reasoning about application performance. The results aid data layout optimizations, and data placement decisions.

Contacts: (firstname.lastname@pnnl.gov)

• Nathan R. Tallent (www), (www)

Contributors:

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

References

• Yasodha Suriyakumar, Nathan R. Tallent, Andrés Marquez, and Karen Karavanic, "MemFriend: Understanding memory performance with spatial-temporal affinity," in Proc. of the International Symposium on Memory Systems (MemSys 2024), September 2024.

• 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

Acknowledgements

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