Performance analysis and hardware acceleration of real software: the case of MadGraph (Lecture 1)

About

Abstract: The growing computational demands of high-precision simulations for the High-Luminosity LHC era require a careful optimization of Monte Carlo event generators. In these lectures, we will explore different profiling techniques and methods to implement hardware acceleration, with a focus on their application to MadGraph5_aMC@NLO (MadGraph), one of the most widely used software in high-energy physics to simulate particle collisions. We will begin with an overview of state-of-the-art profiling methodologies and tools for performance analysis on modern architectures. These techniques will then be applied to MadGraph to study the performance characteristics of the various components of the code, identify key areas for optimization, and discuss strategies to exploit hardware acceleration, including data-parallel and heterogeneous computing paradigms. Finally, we will assess the impact of these optimizations on overall performance. The goal of the lectures is to provide both conceptual and practical insights into how profiling can guide software optimization, and how data parallelism can enable efficient use of next-generation computing resources.

00:00:00 Slide 1

00:00:12 Slide 2

00:01:01 Slide 3

00:01:42 Slide 4

00:02:13 Slide 5

00:03:21 Slide 6

00:03:55 Slide 7

00:04:23 Slide 8

00:04:55 Slide 9

00:05:52 Slide 10

00:07:32 Slide 11