Mansard Roofline Model: Reinforcing the Accuracy of the Roofs

Abstract

Continuous enhancements and diversity in modern multi-core hardware, such as wider and deeper core pipelines and memory subsystems, bring to practice a set of hard-to-solve challenges when modeling their upper-bound capabilities and identifying the main application bottlenecks. Insightful roofline models are widely used for this purpose, but the existing approaches overly abstract the micro-architecture complexity, thus providing unrealistic performance bounds that lead to a misleading characterization of real-world applications. To address this problem, the Mansard Roofline Model (MaRM), proposed in this work, uncovers a minimum set of architectural features that must be considered to provide insightful, but yet accurate and realistic, modeling of performance upper bounds for modern processors. By encapsulating the retirement constraints due to the amount of retirement slots, Reorder-Buffer and Physical Register File sizes, the proposed model accurately models the capabilities of a real platform (average rRMSE of 5.4%) and characterizes 12 application kernels from standard benchmark suites. By following a herein proposed MaRM interpretation methodology and guidelines, speed-ups of up to 5× are obtained when optimizing real-world bioinformatic application, as well as a super-linear speedup of 18.5× when parallelized.

On‐demand JSON: A better way to parse documents?

“…The number of instructions retired includes only the instructions that have been fully executed and excludes instructions issued solely due to speculative execution. [24][25][26] To benchmark and validate our work, we need various tasks. Our goal is to compare On-Demand with existing generic JSON parsers.…”

“…It allows us to compute the actual average processor frequency: we verify that it can reach 3.2 GHz as expected. The number of instructions retired includes only the instructions that have been fully executed and excludes instructions issued solely due to speculative execution 24–26 …”