Abstract
Recent emergence of systems with multiple performance and capacity tiers of memory invites a fresh consideration of strategies for optimal placement of data into the various tiers. This work explores a variety of cross-layer strategies for managing application data in multi-tiered memory. We propose new profiling techniques based on the automatic classification of program allocation sites, with the goal of using those classifications to guide memory tier assignments. We evaluate our approach with different profiling inputs and application strategies, and show that it outperforms other state-of-the-art management techniques.
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- 1.
This allocation site-based strategy for optimizing accesses-per-byte is designed to obviate tracing or sampling on an object-by-object basis.
- 2.
The primary goal of this work is to study the potential benefits of automated application guidance. While our simulation-based evaluation neglects overhead of profiling, Sect. 4 covers how in practice, allocation site based guidance can be generated (either online or offline) and applied in direct execution with negligible overhead.
- 3.
Phase transitions may be detected online by several means, including through models of instruction and data access behaviors, hardware event ratios, etc.
- 4.
For direct execution, an alternative to Pin based instrumentation is to use LLVM inserted wrappers (as described in Sect. 4.2.1), and to sample access rates through hardware-based counters (e.g., using the PEBS facility on modern Intel processors).
- 5.
Other, more compact encodings of the allocation sites may also be employed – e.g., a low-overhead approximate method in direct execution is to use a hash over (call-return) last branch records (LBR) recorded by a processor’s monitoring unit.
- 6.
We had to omit zeusmp due to an incompatibility with our adopted basic block vector collection tool [23].
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Acknowledgements
This research is supported in part by the National Science Foundation under CCF-1619140, CCF-1617954, and CNS-1464288, as well as a grant from the Software and Services Group (SSG) at Intel®.
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Effler, T.C., Howard, A.P., Zhou, T., Jantz, M.R., Doshi, K.A., Kulkarni, P.A. (2018). On Automated Feedback-Driven Data Placement in Multi-tiered Memory. In: Berekovic, M., Buchty, R., Hamann, H., Koch, D., Pionteck, T. (eds) Architecture of Computing Systems – ARCS 2018. ARCS 2018. Lecture Notes in Computer Science(), vol 10793. Springer, Cham. https://doi.org/10.1007/978-3-319-77610-1_14
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