Add dataset card

README.md

@@ -1,3 +1,367 @@
 ---
 license: cc-by-4.0
+viewer: false
+task_categories:
+  - text-retrieval
+  - feature-extraction
+tags:
+  - approximate-nearest-neighbor-search
+  - vector-search
+  - vector-database
+  - graph-index
+  - graph-reordering
+  - ann-benchmark
+  - cagra
+  - nsg
+  - diskann
+pretty_name: "Plasma: Graph Indices for Layout-Aware ANNS on GPU"
 ---
+
+# Plasma — Prebuilt Graph Indices for Layout-Aware ANNS on GPU
+
+Companion artifacts for **Plasma** (**P**latform for **L**ayout-**A**ware **S**earch and
+**M**emory **A**rrangement), a unified evaluation framework for graph-based Approximate
+Nearest Neighbor Search on GPU that isolates the effects of **graph index topology** from
+those of **memory layout**.
+
+> **TL;DR of the paper** — Decoupling graph topology from memory layout on GPU. Vertex
+> reordering alone yields up to **80% QPS gain at equal recall**.
+
+| | |
+|---|---|
+| 📄 Paper | https://openreview.net/forum?id=tF70hyyM6V |
+| 📚 arXiv | https://arxiv.org/abs/2508.15436 |
+| 🌐 Project page | https://omron-sinicx.github.io/plasma |
+| 💻 Code | https://github.com/omron-sinicx/plasma |
+
+Plasma compares graph-based ANNS indices under differing memory layouts while collecting
+hardware-level metrics, covering both **classical** (SIFT, GIST, Deep) and **modern
+embeddings** (Yandex T2I, OpenAI, Wikipedia, BioASQ, C4). Since index construction is
+computationally expensive, we release the pre-built indices — **adjacency lists and
+reordering maps** — so that layout and topology can be evaluated on your platforms
+without rebuilding.
+
+**This project does not release new vectors; the released files contain graph structure
+only.** The underlying vectors are from their original providers, attributed in full
+under [Source datasets and credits](#source-datasets-and-credits).
+
+> ### ⚠️ No vectors are included
+>
+> Every file in this repository contains **integers only** — graph adjacency lists and
+> vertex-ID permutations. **No embedding, feature vector, or any other element of the
+> source corpora is redistributed here.** Index-implementation binaries that embed the
+> raw vectors (`*.faissindex`, `*.diskann.data`, and similar) are deliberately excluded.
+>
+> To use these indices you must obtain the vectors yourself from their original
+> distributors, under those distributors' own terms. See
+> [Reproducing a usable index](#reproducing-a-usable-index).
+
+Building these graphs is the expensive part of ANN research — a single 40M-point graph
+takes hours on a GPU. Publishing the graphs (and the reordering permutations derived from
+them) lets others reproduce and compare **graph-reordering / memory-layout** results
+without repeating index construction.
+
+---
+
+## Contents
+
+| Index | Description |
+|---|---|
+| `cagra` | GPU-built k-NN graph (NVIDIA CAGRA) |
+| `nsg` | Navigating Spreading-out Graph |
+| `nndescent` | NN-Descent k-NN graph |
+| `diskann` | DiskANN / Vamana graph |
+| `nssg` | Navigating Satellite System Graph — extra, beyond the four evaluated in the paper |
+
+Graph-reordering permutations are provided for seven methods:
+
+| Method | In the paper |
+|---|---|
+| `gorder` | GOrder |
+| `rcm` | RCM (Reverse Cuthill–McKee) |
+| `hubsort` | Hub Sort |
+| `indegree`, `outdegree` | Degree Sort |
+| `hubcluster`, `random` | extra baselines, beyond the paper's headline set |
+
+### Datasets covered
+
+| Dataset | Dim | Metric | Indices | Files | Size |
+|---|---:|---|---|---:|---:|
+| `sift-128-euclidean` | 128 | L2 | cagra, nsg, nssg, nndescent, diskann | 60 | 6.78 GiB |
+| `gist-960-euclidean` | 960 | L2 | cagra, nsg, nssg, nndescent, diskann | 54 | 4.71 GiB |
+| `deep1m-96-euclidean` | 96 | L2 | cagra, nsg, nssg, nndescent, diskann | 54 | 5.32 GiB |
+| `deep10m` | 96 | L2 | cagra, nsg, nndescent, diskann | 18 | 22.10 GiB |
+| `t2i1m-200` | 200 | L2 / IP | cagra, nsg, nssg, nndescent, diskann | 52 | 5.54 GiB |
+| `openai1m-1536-euclidean` | 1536 | L2 | cagra, nsg, nndescent, diskann | 53 | 5.21 GiB |
+| `wikipedia1m-768-ip` | 768 | IP | cagra, nsg, nndescent, diskann | 46 | 4.47 GiB |
+| `wikipedia10m-768-ip` | 768 | IP | nndescent | 11 | 14.95 GiB |
+| `bioasq1m-1024-ip` | 1024 | IP | cagra, nsg, nndescent, diskann | 46 | 4.56 GiB |
+| `bioasq10m-1024-ip` | 1024 | IP | nndescent | 11 | 15.11 GiB |
+| `c45m-1536-ip` | 1536 | IP | nndescent | 11 | 7.34 GiB |
+| `sift_200nn.knng` | 128 | L2 | 200-NN ground-truth graph | 1 | 0.75 GiB |
+| **Phase 1 total** | | | | **417** | **96.84 GiB** |
+| `deep40m-96-euclidean` 🚧 | 96 | L2 | cagra, nndescent | 12 | 66.68 GiB |
+| `deep50m-96-euclidean` 🚧 † | 96 | L2 | nsg | 1 | 9.81 GiB |
+| **Total** | | | | **430** | **173.33 GiB** |
+
+> 🚧 **Upload in progress.** The `deep40m` / `deep50m` indices are being added in a
+> follow-up batch. Everything above them is complete.
+
+These cover the 12 datasets benchmarked in the paper. († `deep50m` is an extra scale
+point not reported in the paper.) Larger configurations are covered by fewer index types
+simply because not every index could be built at that scale within our compute budget.
+
+---
+
+## File naming
+
+```
+<index>_K<degree>_<dataset>_<metric>[_reordered_<method>][_mapping].<ext>
+```
+
+| Part | Values |
+|---|---|
+| `<index>` | `cagra`, `nsg`, `nssg`, `nndescent`, `diskann` |
+| `K<degree>` | `K32` (almost all), `K64` (a few SIFT configurations) |
+| `<dataset>` | e.g. `sift-128-euclidean`, `bioasq1m-1024-ip` |
+| `<metric>` | `l2` (Euclidean) or `ip` (inner product) |
+| `<method>` | `gorder`, `rcm`, `hubsort`, `hubcluster`, `indegree`, `outdegree`, `random` |
+
+Examples:
+
+```
+cagra_K32_deep1m-96-euclidean_l2.adjlist                          # base graph
+cagra_K32_deep1m-96-euclidean_l2_reordered_gorder_mapping.txt     # Gorder permutation
+diskann_K32_bioasq1m-1024-ip_ip_reordered_gorder.adjlist          # reordered graph
+diskann_K32_bioasq1m-1024-ip_ip_mapping_gorder.txt                # Gorder permutation
+```
+
+Note the two mapping-filename spellings: `..._reordered_<method>_mapping.txt` and
+`..._mapping_<method>.txt`. They are the same kind of file; the difference is only which
+build script emitted it.
+
+### `*.adjlist` — adjacency list (ASCII)
+
+```
+<N> <K>                                  # header: number of vertices, out-degree
+<vertex_id> <neighbor_1> ... <neighbor_K>
+...                                      # N lines, vertex_id ascending from 0
+```
+
+### `*_mapping*.txt` — vertex permutation (ASCII)
+
+```
+<N>                                      # header: number of vertices
+<original_id> <new_id>
+...                                      # N lines, original_id ascending from 0
+```
+
+The mapping is `original → new`. Concretely, for a reordering `m`:
+
+```
+reordered_graph[m[u]] == { m[w] : w in original_graph[u] }
+reordered_vectors[m[u]] == original_vectors[u]
+```
+
+### `sift_200nn.knng` — binary k-NN graph
+
+The exact-search 200-NN graph for SIFT1M, used as ground truth for recall evaluation.
+Stored in TEXMEX **`ivecs`** format — little-endian `int32` throughout, one record per
+query point:
+
+```
+[K=200][id_1] ... [id_200]     # repeated N = 1,000,000 times
+```
+
+The file is exactly `1_000_000 * (4 + 200*4) = 804,000,000` bytes.
+
+```python
+knn = np.fromfile("sift_200nn.knng", dtype=np.int32).reshape(-1, 201)[:, 1:]
+```
+
+---
+
+## Downloading
+
+Full download (~173 GiB) is rarely what you want. Fetch only what you need:
+
+```bash
+pip install -U huggingface_hub hf_xet
+```
+
+```bash
+# One index type, one dataset
+hf download omron-sinicx/plasma --repo-type dataset --local-dir ./plasma \
+  --include 'cagra_K32_deep1m-96-euclidean_l2*'
+
+# Base graphs only, no reordering permutations
+hf download omron-sinicx/plasma --repo-type dataset --local-dir ./plasma \
+  --include '*.adjlist' --exclude '*_reordered_*'
+
+# Everything for one dataset, across all index types
+hf download omron-sinicx/plasma --repo-type dataset --local-dir ./plasma \
+  --include '*_sift-128-euclidean_l2*'
+
+# Just the Gorder permutations
+hf download omron-sinicx/plasma --repo-type dataset --local-dir ./plasma \
+  --include '*gorder*.txt'
+```
+
+Check what a pattern would fetch before committing to it with `--dry-run`.
+
+---
+
+## Reproducing a usable index
+
+An adjacency list is only half of an index — you need the vectors it was built over.
+
+**1. Get the vectors from the original distributor** (see
+[Source datasets and credits](#source-datasets-and-credits)). Use the *same* base vectors, in the
+*same* order, as that section specifies; vertex IDs in the adjacency lists are
+positions in that original ordering.
+
+**2. Load the graph.**
+
+```python
+import numpy as np
+
+def load_adjlist(path):
+    with open(path) as f:
+        n, k = map(int, f.readline().split())
+        adj = np.empty((n, k), dtype=np.int32)
+        for line in f:
+            parts = line.split()
+            adj[int(parts[0])] = parts[1:]
+    return adj
+
+def load_mapping(path):
+    with open(path) as f:
+        n = int(f.readline())
+        m = np.empty(n, dtype=np.int32)
+        for line in f:
+            u, v = line.split()
+            m[int(u)] = int(v)
+    return m            # m[original_id] -> new_id
+```
+
+**3. Apply a reordering** to your vectors so they match a reordered graph:
+
+```python
+vectors = np.load("deep1m_base.npy")           # shape (N, D), original order
+m       = load_mapping("cagra_K32_deep1m-96-euclidean_l2_reordered_gorder_mapping.txt")
+adj     = load_adjlist("cagra_K32_deep1m-96-euclidean_l2.adjlist")
+
+reordered_vectors = np.empty_like(vectors)
+reordered_vectors[m] = vectors                 # reordered_vectors[m[u]] = vectors[u]
+reordered_adj = m[adj[np.argsort(m)]]          # relabel and reorder rows
+```
+
+**4. Search.** Feed `reordered_vectors` and `reordered_adj` to your graph-search routine
+(beam search / greedy best-first). The point of the reordering is locality: neighbouring
+vertices land on nearby cache lines and pages — the paper measures the resulting DRAM
+bandwidth utilisation and L1/L2 hit rates alongside recall and QPS.
+
+For the full benchmark harness, see the code repository:
+https://github.com/omron-sinicx/plasma
+
+---
+
+## Source datasets and credits
+
+The graphs in this repository were built over vectors produced by other people. **We
+redistribute none of those vectors.** Each dataset must be obtained from its original
+provider, under that provider's own terms. Credit belongs to the creators below.
+
+### Classical benchmarks
+
+**SIFT1M · GIST1M** — Hervé Jégou, Matthijs Douze, Cordelia Schmid (INRIA / TEXMEX).
+Local SIFT and global GIST descriptors, introduced with *Product Quantization for Nearest
+Neighbor Search* (IEEE TPAMI, 2011).
+Terms: public domain / CC0-equivalent — no usage restrictions stated by the distributor.
+The 1B-scale sibling (BIGANN) is released as **CC0** by Big ANN Benchmarks.
+→ http://corpus-texmex.irisa.fr/
+
+**DEEP (`deep1m`, `deep10m`, `deep40m`, `deep50m`)** — Artem Babenko and Victor Lempitsky
+(Yandex Research). Subsets of **DEEP1B**: image embeddings taken from the last
+fully-connected layer of a GoogLeNet pretrained on ImageNet classification, introduced in
+*Efficient Indexing of Billion-Scale Datasets of Deep Descriptors* (CVPR, 2016).
+License: **CC BY 4.0** (Big ANN Benchmarks, "Release terms").
+→ https://research.yandex.com/blog/benchmarks-for-billion-scale-similarity-search
+→ https://big-ann-benchmarks.com/neurips21.html
+
+### Modern embeddings
+
+**Yandex Text-to-Image (`t2i1m`)** — Yandex Research. Database vectors are image
+embeddings from Se-ResNeXt-101; queries are textual embeddings from a DSSM variant. A
+deliberately *cross-modal* benchmark, where queries and database points come from
+different distributions.
+License: **CC BY 4.0** (Big ANN Benchmarks, "Release terms").
+→ https://big-ann-benchmarks.com/neurips21.html
+
+**OpenAI Embed. (`openai1m`)** — text embeddings of the **WikiText** corpus, generated by
+an **OpenAI** embedding model (1536-dim). Obtained via the Big ANN Benchmarks collection.
+Terms: the underlying WikiText corpus is **CC BY-SA 3.0** (derived from Wikipedia);
+OpenAI's terms for model outputs apply to the embeddings themselves.
+→ https://big-ann-benchmarks.com/
+→ Simhadri et al., *Results of the Big ANN: NeurIPS'23 competition* (2024)
+
+The following three come from **VectorDBBench** (Zilliz, 2023), whose *harness* is
+MIT-licensed. VectorDBBench states no unified license for the datasets themselves, so the
+terms of the underlying corpus **and** of the embedding provider both apply.
+→ https://github.com/zilliztech/VectorDBBench
+
+**Wikipedia (`wikipedia1m`, `wikipedia10m`)** — text embeddings of the **Wikipedia
+corpus**, generated by the **Cohere V2** model (768-dim).
+Terms: Wikipedia text is **CC BY-SA**; Cohere's terms apply to the embeddings.
+
+**BioASQ (`bioasq1m`, `bioasq10m`)** — text embeddings of the **BioASQ question-answering
+corpus**, generated by an **OpenAI** model (1024-dim).
+Terms: BioASQ distributes its corpus under its own terms and requires registration;
+OpenAI's terms apply to the embeddings. Verify BioASQ's conditions before use.
+→ http://bioasq.org/
+
+**C4 (`c45m`)** — text embeddings of the **Colossal Clean Crawled Corpus (C4)**, generated
+by the **Cohere V3** model (1536-dim). C4 was released by AllenAI as a cleaned scrape of
+Common Crawl, introduced with the T5 paper (Raffel et al., JMLR 2020).
+Terms: the C4 corpus is **ODC-BY**, and Common Crawl's terms apply to the scraped content;
+Cohere's terms apply to the embeddings.
+→ https://huggingface.co/datasets/allenai/c4
+
+---
+
+## License and credit
+
+The contents of *this repository* — the adjacency lists and permutations — are released
+under **CC BY 4.0** by:
+
+- **Yutaro Oguri** — The University of Tokyo
+- **Mai Nishimura** — OMRON SINIC X Corporation
+- **Yusuke Matsui** — The University of Tokyo
+
+This license covers only the graph structures we computed. It does **not** grant any
+rights to the source corpora, whose underlying vectors are from their original providers
+and remain under those providers' own terms — attributed in full under
+[Source datasets and credits](#source-datasets-and-credits).
+
+---
+
+## Citation
+
+> Yutaro Oguri, Mai Nishimura, Yusuke Matsui.
+> **Plasma: A Layout-Aware Benchmark Reveals Memory Layout Matters for Graph-based ANNS on GPU.**
+> The 2nd Workshop on Vector Databases (VecDB) at Very Large Data Bases (VLDB), 2026.
+> *The University of Tokyo · OMRON SINIC X Corporation*
+
+```bibtex
+@inproceedings{oguri2026plasma,
+  author    = {Yutaro Oguri and Mai Nishimura and Yusuke Matsui},
+  title     = {Plasma: A Layout-Aware Benchmark Reveals Memory Layout
+               Matters for Graph-based ANNS on GPU},
+  booktitle = {The 2nd Workshop on Vector Databases (VecDB) at
+               Very Large Data Bases (VLDB)},
+  year      = {2026},
+  url       = {https://openreview.net/forum?id=tF70hyyM6V},
+  eprint    = {2508.15436},
+  archivePrefix = {arXiv}
+}
+```