E-WarP: a system-wide framework for memory bandwidth profiling and management
Files
Accepted manuscript
Date
2020-12-02
DOI
Authors
Sohal, Parul
Tabish, Rohan
Drepper, Ulrich
Mancuso, Renato
Version
OA Version
Accepted manuscript
Citation
Parul Sohal, Rohan Tabish, Ulrich Drepper, Renato Mancuso. 2020. "E-WarP: a System-wide Framework for Memory Bandwidth Profiling and Management." 41st IEEE Real-Time Systems Symposium (RTSS 2020). Huston, TX (held online), 2020-12-01 - 2020-12-04.
Abstract
The proliferation of multi-core, accelerator-enabled embedded systems has introduced new opportunities to consolidate real-time systems of increasing complexity. But the road to build confidence on the temporal behavior of co-running applications has presented formidable challenges. Most prominently, the main memory subsystem represents a performance bottleneck for both CPUs and accelerators. And industry-viable frameworks for full-system main memory management and performance analysis are past due. In this paper, we propose our Envelope-aWare Predictive model, or E-WarP for short. E-WarP is a methodology and technological framework to: (1) analyze the memory demand of applications following a profile-driven approach; (2) make realistic predictions on the temporal behavior of workload deployed on CPUs and accelerators; and (3) perform saturation-aware system consolidation. This work aims at providing the technological foundations as well as the theoretical grassroots for truly workload-aware analysis of real-time systems. We provide a full implementation of our techniques on a commercial platform (NXP S32V234) and make two key observations. First, we achieve, on average, a 6% overprediction on the runtime of bandwidth-regulated applications. Second, we experimentally validate that the calculated bounds hold if the main memory subsystem operates below saturation.