# PLASMA: A Layout-Aware Benchmark Reveals Memory Layout Matters for Graph-based ANNS on GPU

arXiv: https://arxiv.org/abs/2508.15436 · HTML: https://mlsanity.com/paper/2508.15436/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 17 of its 63 references that are in the map.
- First posted on arXiv 21 Aug 2025.

## Builds on (most cited first, 17 of 17)

- [Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer](https://mlsanity.com/paper/1910.10683/) (2019)
- [Going Deeper with Convolutions](https://mlsanity.com/paper/1409.4842/) (2014)
- [Billion-scale similarity search with GPUs](https://mlsanity.com/paper/1702.08734/) (2017)
- [Efficient and robust approximate nearest neighbor search using Hierarchical Navigable Small World graphs](https://mlsanity.com/paper/1603.09320/) (2016)
- [ANN-Benchmarks: A Benchmarking Tool for Approximate Nearest Neighbor Algorithms](https://mlsanity.com/paper/1807.05614/) (2018)
- [Practical and Optimal LSH for Angular Distance](https://mlsanity.com/paper/1509.02897/) (2015)
- [A Comprehensive Survey and Experimental Comparison of Graph-Based Approximate Nearest Neighbor Search](https://mlsanity.com/paper/2101.12631/) (2021)
- [Residual Quantization with Implicit Neural Codebooks](https://mlsanity.com/paper/2401.14732/) (2024)
- [EFANNA : An Extremely Fast Approximate Nearest Neighbor Search Algorithm Based on kNN Graph](https://mlsanity.com/paper/1609.07228/) (2016)
- [BANG: Billion-Scale Approximate Nearest Neighbor Search using a Single GPU](https://mlsanity.com/paper/2401.11324/) (2024)
- [Graph-Based Vector Search: An Experimental Evaluation of the State-of-the-Art](https://mlsanity.com/paper/2502.05575/) (2025)
- [Hercules Against Data Series Similarity Search](https://mlsanity.com/paper/2212.13297/) (2022)
- [Benchmarking High Bandwidth Memory on FPGAs](https://mlsanity.com/paper/2005.04324/) (2020)
- [Can Graph Reordering Speed Up Graph Neural Network Training? An Experimental Study](https://mlsanity.com/paper/2409.11129/) (2024)
- [General and Practical Tuning Method for Off-the-Shelf Graph-Based Index: SISAP Indexing Challenge Report by Team UTokyo](https://mlsanity.com/paper/2309.00472/) (2023)
- [Enabling full-speed random access to the entire memory on the A100 GPU](https://mlsanity.com/paper/2405.11425/) (2024)
- [LotusFilter: Fast Diverse Nearest Neighbor Search via a Learned Cutoff Table](https://mlsanity.com/paper/2506.04790/) (2025)

## Abstract (arXiv)

We propose a Platform for Layout-Aware Search and Memory Arrangement (PLASMA), a unified evaluation framework for graph-based Approximate Nearest Neighbor Search (ANNS) on GPU that isolates the effects of graph index topology and memory layout. Graph-based ANNS is essential in modern AI applications such as RAG, and GPU utilization is attracting attention for datasets of millions or more vectors. Our framework extracts the topology of arbitrary graph-based indices and enables execution under a unified, GPU-optimized search algorithm, specifying the correspondence between vertex IDs and positions on memory to allow arbitrary vertex orderings. Through comprehensive experiments, we demonstrate that vertex reordering yields up to 80% (typically 10-30%) QPS improvement while preserving search accuracy.