MemFriend

Home:

• https://github.com/pnnl/memfriend (now part of MemGaze)

• Performance Lab for EXtreme Computing and daTa

About: A detailed understanding of data locality is an essential tool for effective hardware/software co-design. Today's locality analysis focuses on single memory locations and therefore can fail to provide sufficient insight into correlations between memory regions and data structures.

MemFriend, a new analysis module within the MemGaze framework, 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)
• Andrés Marquez (PNNL)

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.

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