# Accelerate search performance with SVE2 MATCH on Arm servers

## In this learning path

- [Introduction](https://learn.arm.com/learning-paths/servers-and-cloud-computing/sve2-match/)
- [Understand SVE2 MATCH for multi-key search](https://learn.arm.com/learning-paths/servers-and-cloud-computing/sve2-match/sve2-match-search/)
- [Implement scalar and SVE2 MATCH search](https://learn.arm.com/learning-paths/servers-and-cloud-computing/sve2-match/implement-search/)
- [Benchmark scalar and SVE2 MATCH search](https://learn.arm.com/learning-paths/servers-and-cloud-computing/sve2-match/benchmark-search/)
- [Next Steps](https://learn.arm.com/learning-paths/servers-and-cloud-computing/sve2-match/_next-steps/)

## About this Learning Path

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| Skill level: | Introductory |
| Reading time: | 20 min |
| Last updated: | 12 Oct 2026 |

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| Author: | Pareena Verma , Arm<br>[GitHub](https://github.com/pareenaverma)<br>[LinkedIn](https://linkedin.com/in/pareena-verma-7853607) |
| Arm IP: | [Neoverse](https://support.arm.com/?tab=compute-ip&Product%20Type=Infrastructure%20Processors) |
| Tags: | [Performance and Architecture](https://learn.arm.com/tag/performance-and-architecture) [Arm AGI CPU](https://learn.arm.com/tag/arm-agi-cpu) [AWS Graviton](https://learn.arm.com/tag/aws-graviton) [Microsoft Azure Cobalt](https://learn.arm.com/tag/microsoft-azure-cobalt) [Google Axion](https://learn.arm.com/tag/google-axion) [Linux](https://learn.arm.com/tag/linux) [SVE2](https://learn.arm.com/tag/sve2) [Neon](https://learn.arm.com/tag/neon) [Runbook](https://learn.arm.com/tag/runbook) |

### Who is this for?
This is an introductory topic for database developers, performance engineers, and anyone optimizing data processing workloads on Arm-based cloud instances.

### What will you learn?
After completing this Learning Path, you'll be able to perform the following tasks:
- Understand the purpose and function of SVE2 MATCH instructions.
- Implement a search algorithm using both scalar and SVE2-based MATCH approaches.
- Benchmark and compare performance between scalar and vectorized implementations.
- Analyze speedups and efficiency gains on Arm Neoverse-based instances with SVE2.

### Prerequisites
Before starting, you'll need the following prerequisites:
- Access to an [AWS Graviton, Google Axion, or Azure Cobalt 100 virtual machine](https://learn.arm.com/learning-paths/servers-and-cloud-computing/csp/) from a cloud service provider, or an Arm AGI CPU platform.

### Summary
You’ll implement and benchmark scalar and SVE2 MATCH multi-key searches for 8-bit and 16-bit arrays on an Arm server. First, you’ll learn how MATCH compares vector elements with search keys. Then, you’ll write scalar, vectorized, and unrolled search functions. Finally, you’ll add a benchmark harness, compile the program, and compare latency, throughput, and speedups at different hit rates.