Content-defined Chunking (CDC) algorithms dictate the overall space savings that deduplication systems achieve. However, due to their need to scan each file in its entirety, they are slow and often the main performance bottleneck within data deduplication. We present VectorCDC, a method to accelerate hashless CDC algorithms using vector CPU instructions, such as SSE / AVX. We analyzed the state-of-the-art chunking algorithms and discovered that hashless algorithms primarily use two data processing patterns to identify chunk boundaries: Extreme Byte Searches and Range Scans. VectorCDC presents a vector-friendly approach to accelerate these two patterns. Using VectorCDC, we accelerated three state-of-the-art hashless chunking algorithms: RAM, AE, and MAXP. Our evaluation shows that VectorCDC is effective on Intel, AMD, ARM, and IBM CPUs, achieving 8.35x - 26.2x higher throughput than existing vector-accelerated algorithms, and 15.3x - 207.2x higher throughput than existing unaccelerated algorithms. VectorCDC achieves this without affecting the deduplication space savings.
@article{arxiv.2508.05797,
title = {Accelerating Data Chunking in Deduplication Systems using Vector Instructions},
author = {Sreeharsha Udayashankar and Abdelrahman Baba and Samer Al-Kiswany},
journal= {arXiv preprint arXiv:2508.05797},
year = {2026}
}
Comments
This paper has been accepted for publication with the ACM Transactions on Storage journal. It is the follow-up work to our FAST 2025 paper, "VectorCDC: Accelerating Data Deduplication with Vector Instructions". The associated code is available at https://github.com/UWASL/dedup-bench