Accelerate random number generation with OpenRNG and Performix
Introduction
Set up your environment
Run the baseline data-processing example
Identify code hotspots with Arm Performix
Accelerate distribution generation with OpenRNG
Measure performance improvements with a microbenchmark
Next Steps

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About this Learning Path

Skill level:  Introductory
Reading time:  45 min
Last updated:  22 Apr 2026
Skill level:
  Introductory
Reading time:
  45 min
Last updated:
  22 Apr 2026
Author:Kieran Hejmadi
Arm IP:Neoverse
Tags: Performance and Architecture Linux CMake Arm Performix OpenRNG Arm Performance Libraries
Author:
  • Kieran Hejmadi
Arm IP:
Neoverse
Tags:
Performance and Architecture Linux CMake Arm Performix OpenRNG Arm Performance Libraries

Who is this for?

This is an introductory topic for C++ developers who want to profile a data-processing workload on Arm Linux, identify performance bottlenecks with Arm Performix, and accelerate random number generation using OpenRNG and Arm Performance Libraries.

What will you learn?

Upon completion of this Learning Path, you will be able to:

  • Build and run a baseline C++ data-processing workload on Arm Linux
  • Use Arm Performix Code Hotspots to identify the highest-impact optimization target
  • Accelerate random number generation by integrating OpenRNG and Arm Performance Libraries
  • Measure performance improvements using a microbenchmark across multiple data sizes

Prerequisites

Before starting, you will need the following:

  • An Arm Linux (aarch64) server, such as an AWS Graviton3 instance
  • Basic understanding of C++ and CMake
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