cuGraph
E890459
cuGraph is a GPU-accelerated graph analytics library in the NVIDIA RAPIDS ecosystem designed to perform large-scale graph processing and algorithms efficiently on NVIDIA GPUs.
All labels observed (1)
| Label | Occurrences |
|---|---|
| cuGraph canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T10882138 — resolving that mention is where its identity was fixed. The disambiguator weighed these candidate entities and picked the highlighted one (or “None”, minting a new entity). This is how homonymy is resolved: the same surface form can point to different entities.
NED1
Entity disambiguation (via context triple)
gpt-5-mini-2025-08-07
Target entity: cuGraph Context triple: [NVIDIA RAPIDS, component, cuGraph]
-
A.
NVIDIA RAPIDS
NVIDIA RAPIDS is an open-source suite of GPU-accelerated data science and analytics libraries designed to speed up end-to-end machine learning and data processing workflows.
-
B.
CUDA libraries
CUDA libraries are a collection of NVIDIA-provided GPU-accelerated software libraries that offer optimized routines for tasks such as linear algebra, deep learning, signal processing, and parallel algorithms on CUDA-enabled hardware.
-
C.
cuSOLVER
cuSOLVER is an NVIDIA GPU-accelerated linear algebra library that provides high-performance routines for solving dense and sparse systems of equations, eigenvalue problems, and related numerical tasks.
-
D.
cuBLAS
cuBLAS is NVIDIA’s GPU-accelerated implementation of the BLAS linear algebra library, providing high-performance matrix and vector operations for CUDA applications.
-
E.
CuPy
CuPy is an open-source array library for Python that accelerates numerical computing by providing a NumPy-compatible interface backed by GPU execution.
- F. None of above. chosen
- G. Unsure - the case is ambiguous/there is not enough information to decide.
NED2
Entity disambiguation (via description)
gpt-5-mini-2025-08-07
Target entity: cuGraph Target entity description: cuGraph is a GPU-accelerated graph analytics library in the NVIDIA RAPIDS ecosystem designed to perform large-scale graph processing and algorithms efficiently on NVIDIA GPUs.
-
A.
NVIDIA RAPIDS
NVIDIA RAPIDS is an open-source suite of GPU-accelerated data science and analytics libraries designed to speed up end-to-end machine learning and data processing workflows.
-
B.
CUDA libraries
CUDA libraries are a collection of NVIDIA-provided GPU-accelerated software libraries that offer optimized routines for tasks such as linear algebra, deep learning, signal processing, and parallel algorithms on CUDA-enabled hardware.
-
C.
cuSOLVER
cuSOLVER is an NVIDIA GPU-accelerated linear algebra library that provides high-performance routines for solving dense and sparse systems of equations, eigenvalue problems, and related numerical tasks.
-
D.
cuBLAS
cuBLAS is NVIDIA’s GPU-accelerated implementation of the BLAS linear algebra library, providing high-performance matrix and vector operations for CUDA applications.
-
E.
CuPy
CuPy is an open-source array library for Python that accelerates numerical computing by providing a NumPy-compatible interface backed by GPU execution.
- F. None of above. chosen
Statements (56)
| Predicate | Object |
|---|---|
| instanceOf |
GPU-accelerated graph analytics library
ⓘ
RAPIDS library ⓘ open-source software ⓘ |
| acceleratedBy | NVIDIA GPU NERFINISHED ⓘ |
| developer | NVIDIA NERFINISHED ⓘ |
| documentation | https://docs.rapids.ai/api/cugraph/stable/ ⓘ |
| domain |
graph analytics
ⓘ
graph processing ⓘ |
| feature |
GPU-accelerated primitives
ⓘ
NetworkX-compatible API ⓘ distributed graph analytics ⓘ multi-GPU support ⓘ single-GPU support ⓘ |
| integratesWith |
Dask
NERFINISHED
ⓘ
NetworkX NERFINISHED ⓘ cuDF NERFINISHED ⓘ |
| license | Apache License 2.0 ⓘ |
| optimizedFor |
high-performance computing
ⓘ
large-scale graphs ⓘ |
| partOf |
RAPIDS
NERFINISHED
ⓘ
RAPIDS AI ecosystem NERFINISHED ⓘ |
| programmingLanguage |
C++
ⓘ
CUDA NERFINISHED ⓘ Python ⓘ |
| repository | https://github.com/rapidsai/cugraph ⓘ |
| supportsAlgorithm |
Betweenness centrality
ⓘ
Breadth-First Search NERFINISHED ⓘ Breadth-First Search-based traversal ⓘ Connected Components ⓘ Eigenvector centrality ⓘ Graph coloring ⓘ HITS ⓘ Jaccard similarity ⓘ K-Core decomposition ⓘ Katz centrality NERFINISHED ⓘ Leiden community detection ⓘ Louvain community detection ⓘ Maximum Weight Matching ⓘ Minimum Spanning Tree ⓘ PageRank NERFINISHED ⓘ Single-Source Shortest Path ⓘ Strongly Connected Components ⓘ Triangle Counting ⓘ Weakly Connected Components ⓘ |
| supportsHardware |
NVIDIA RTX GPUs
NERFINISHED
ⓘ
NVIDIA data center GPUs NERFINISHED ⓘ |
| supportsLanguageBinding |
C++ API
NERFINISHED
ⓘ
Python API NERFINISHED ⓘ |
| useCase |
fraud detection
ⓘ
recommendation systems ⓘ social network analysis ⓘ telecom network analysis ⓘ |
| usesFramework |
CUDA
NERFINISHED
ⓘ
RAFT NERFINISHED ⓘ RAPIDS Memory Manager NERFINISHED ⓘ cuDF NERFINISHED ⓘ |
How these facts were elicited
The pipeline generated the facts above by prompting gpt-5.1 with this entity's name + description and the instruction below.
Instruction
You are a knowledge base construction expert. Given a subject entity and a description of it, return factual statements that you know for the subject as a JSON list of dictionaries(triples), where keys must be "subject", "predicate" and "object". The number of facts may be very high, between 25 to 50 or more, for very popular subjects. For less popular subjects, the number of facts can be very low, like 5 or 10. # Requirements - If you don't know the subject at all, return an empty list. - If the subject is not a named entity, return an empty list. - Include at least one triple where predicate is "instanceOf". - Do not get too wordy. - Separate several objects into multiple triples with one object.
Input
Subject: cuGraph Description of subject: cuGraph is a GPU-accelerated graph analytics library in the NVIDIA RAPIDS ecosystem designed to perform large-scale graph processing and algorithms efficiently on NVIDIA GPUs.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.