On the Algebraic Properties of Flame Graphs
January 21, 2023 Β· Declared Dead Β· π arXiv.org
"No code URL or promise found in abstract"
Evidence collected by the PWNC Scanner
Authors
Gabriele N. Tornetta
arXiv ID
2301.08941
Category
cs.SE: Software Engineering
Cross-listed
cs.LG,
cs.PF
Citations
0
Venue
arXiv.org
Last Checked
5 months ago
Abstract
Flame graphs are a popular way of representing profiling data. In this paper we propose a possible mathematical definition of flame graphs. In doing so, we gain some interesting algebraic properties almost for free, which in turn allow us to define some operations that can allow to perform an in-depth performance regression analysis. The typical documented use of a flame graph is via its graphical representation, whereby one scans the picture for the largest plateaux. Whilst this method is effective at finding the main sources of performance issues, it leaves quite a large amount of data potentially unused. By combining a mathematical precise definition of flame graphs with some statistical methods we show how to generalise this visual procedure and make the best of the full set of collected profiling data.
Community Contributions
Found the code? Know the venue? Think something is wrong? Let us know!
π Similar Papers
In the same crypt β Software Engineering
R.I.P.
π»
Ghosted
R.I.P.
π»
Ghosted
Microservices: yesterday, today, and tomorrow
π
π
The Cartographer
A Survey of Machine Learning for Big Code and Naturalness
R.I.P.
π»
Ghosted
An Overview on Smart Contracts: Challenges, Advances and Platforms
R.I.P.
π»
Ghosted
Slither: A Static Analysis Framework For Smart Contracts
R.I.P.
π»
Ghosted
ContractFuzzer: Fuzzing Smart Contracts for Vulnerability Detection
Died the same way β π» Ghosted
R.I.P.
π»
Ghosted
Federated Learning: Strategies for Improving Communication Efficiency
R.I.P.
π»
Ghosted
In-Datacenter Performance Analysis of a Tensor Processing Unit
R.I.P.
π»
Ghosted
Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning
R.I.P.
π»
Ghosted