# Accelerate Bitmap Scanning with Neon and SVE Instructions on Arm servers

## In this learning path

- [Introduction](https://learn.arm.com/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/)
- [Optimize bitmap scanning in databases with SVE and Neon on Arm servers](https://learn.arm.com/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/01-introduction/)
- [Build and manage a bit vector in C](https://learn.arm.com/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/02-bitmap-data-structure/)
- [Implement scalar bitmap scanning in C](https://learn.arm.com/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/03-scalar-implementations/)
- [Vectorized bitmap scanning with Neon and SVE](https://learn.arm.com/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/04-vector-implementations/)
- [Benchmarking bitmap scanning across implementations](https://learn.arm.com/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/05-benchmarking-and-results/)
- [Applications and optimization best practices](https://learn.arm.com/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/06-application-and-best-practices/)
- [Next Steps](https://learn.arm.com/learning-paths/servers-and-cloud-computing/bitmap_scan_sve2/_next-steps/)

## About this Learning Path

|               |                           |
|---------------|---------------------------|
| Skill level:  | Introductory              |
| Reading time: | 20 min                    |
| Last updated: | 09 Sep 2026               |
| Author:        | Pareena Verma, Arm [GitHub](https://github.com/pareenaverma) [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), [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), [Oracle Cloud Infrastructure (OCI) Ampere Compute](https://learn.arm.com/tag/oracle-cloud-infrastructure-oci-ampere-compute), [Linux](https://learn.arm.com/tag/linux), [SVE](https://learn.arm.com/tag/sve), [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 interested in 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 bitmap scanning operations in database systems
- Implement bitmap scanning with scalar, Neon, and SVE instructions
- Compare performance between different implementations
- Measure performance improvements on Graviton4 instances

### Prerequisites
Before starting, you'll need the following prerequisites:
- An [Arm-based instance](https://learn.arm.com/learning-paths/servers-and-cloud-computing/csp/) from an appropriate cloud service provider.

### Summary
You’ll implement and benchmark bitmap scanning for database-style workloads on Arm Neoverse V2–based servers, such as AWS Graviton4. First, you’ll build a compact bit vector in C and add baseline and improved scalar scanning routines. Then, you’ll implement Neon and SVE vectorized versions to process data in wider chunks. You’ll use a benchmarking harness that measures each approach so the relative behavior of scalar, Neon, and SVE implementations can be compared on an Arm-based Linux instance. By the end, you’ll run a single C program that exercises all variants and produces timing results suitable for side-by-side evaluation.

### Frequently asked questions

**Where should I place the code as I follow the steps?**

Use a single source file named `bitvector_scan_benchmark.c`. Add the bit vector type, helper functions, scalar scan routines, Neon and SVE implementations, and the benchmarking code into this file as directed.

**What must the bitmap data structure contain before I can add the scan functions?**

The data structure must include a byte array that holds the bits, the physical size in bytes, and the logical size in bits. The same file must also add helpers to generate and analyze test bitmaps.

**In what order should I implement and test the scanning approaches?**

Start with the per-bit scalar baseline, then the optimized scalar version, followed by the Neon implementation, and finally SVE. After each addition, run the benchmark to compare against the previous versions.

**What result should I expect from the benchmarking step?**

The framework measures elapsed time for each scan function over a chosen number of iterations and tracks how many set-bit positions were found. Use the same input bitmap and iteration count when comparing implementations.

**How can I exercise different workload characteristics when benchmarking?**

Use the provided bitmap generation helpers to create datasets with varying densities. Sparse and dense bitmaps highlight different behaviors across the scalar, Neon, and SVE implementations.