CuPy
E97065
CuPy is an open-source array library for Python that accelerates numerical computing by providing a NumPy-compatible interface backed by GPU execution.
All labels observed (1)
| Label | Occurrences |
|---|---|
| CuPy canonical | 3 |
How this entity was disambiguated
This entity first appeared as the object of triple T816376 — 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: CuPy Context triple: [NumPy, influenced, CuPy]
-
A.
NumPy
NumPy is a fundamental Python library that provides efficient multi-dimensional arrays and numerical computing tools widely used in scientific computing and data analysis.
-
B.
NVIDIA CUDA
NVIDIA CUDA is a parallel computing platform and programming model that enables developers to use NVIDIA GPUs for general-purpose high-performance computing.
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C.
PyTorch
PyTorch is an open-source deep learning framework widely used for building and training neural networks, known for its dynamic computation graph and strong support for research and production in Python.
-
D.
TensorFlow
TensorFlow is an open-source, end-to-end machine learning and deep learning framework widely used for building, training, and deploying neural network models at scale.
-
E.
SciPy
SciPy is an open-source Python library that provides advanced scientific and technical computing tools, including modules for optimization, integration, statistics, signal processing, and linear algebra.
- 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: CuPy Target entity description: CuPy is an open-source array library for Python that accelerates numerical computing by providing a NumPy-compatible interface backed by GPU execution.
-
A.
NumPy
NumPy is a fundamental Python library that provides efficient multi-dimensional arrays and numerical computing tools widely used in scientific computing and data analysis.
-
B.
NVIDIA CUDA
NVIDIA CUDA is a parallel computing platform and programming model that enables developers to use NVIDIA GPUs for general-purpose high-performance computing.
-
C.
PyTorch
PyTorch is an open-source deep learning framework widely used for building and training neural networks, known for its dynamic computation graph and strong support for research and production in Python.
-
D.
TensorFlow
TensorFlow is an open-source, end-to-end machine learning and deep learning framework widely used for building, training, and deploying neural network models at scale.
-
E.
SciPy
SciPy is an open-source Python library that provides advanced scientific and technical computing tools, including modules for optimization, integration, statistics, signal processing, and linear algebra.
- F. None of above. chosen
Statements (50)
| Predicate | Object |
|---|---|
| instanceOf |
GPU-accelerated array library
ⓘ
Python library ⓘ open-source software ⓘ |
| category |
GPU computing framework
ⓘ
Python scientific computing library ⓘ |
| compatibleWith | NumPy ⓘ |
| designedFor | numerical computing ⓘ |
| documentation | https://docs.cupy.dev/ ⓘ |
| enables | GPU-accelerated numerical computations ⓘ |
| hasFeature |
NumPy-like linalg module cupy.linalg
ⓘ
NumPy-like random module cupy.random ⓘ |
| integratesWith |
Chainer
ⓘ
Dask ⓘ PyTorch (via array interoperability) ⓘ RAPIDS ecosystem ⓘ
linked to:
NVIDIA RAPIDS
|
| license | MIT License ⓘ |
| openSource | true ⓘ |
| programmingLanguage | Python ⓘ |
| provides |
NumPy-compatible API
ⓘ
drop-in replacement for NumPy on GPU ⓘ |
| repository | https://github.com/cupy/cupy ⓘ |
| supports |
CUDA
ⓘ
linked to:
NVIDIA CUDA
CUDA streams ⓘ Fourier transforms ⓘ GPU acceleration ⓘ JIT compilation of kernels ⓘ NVIDIA GPUs ⓘ
linked to:
Nvidia Maxwell GPU
NumPy broadcasting semantics ⓘ NumPy indexing semantics ⓘ NumPy ufunc semantics ⓘ RawKernel interface ⓘ RawModule interface ⓘ array computing ⓘ broadcasting ⓘ cupy.ndarray core array type ⓘ custom CUDA kernels ⓘ distributed computing via Dask integration ⓘ linear algebra operations ⓘ memory pool for GPU memory management ⓘ multi-GPU computation ⓘ multi-dimensional arrays ⓘ random number generation ⓘ sparse matrices ⓘ universal functions ⓘ |
| targetPlatform | CUDA-enabled systems ⓘ |
| typicalSpeedup | faster than NumPy on compatible GPU workloads ⓘ |
| usedFor |
deep learning workloads
ⓘ
high-performance computing ⓘ machine learning workloads ⓘ scientific computing ⓘ |
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: CuPy Description of subject: CuPy is an open-source array library for Python that accelerates numerical computing by providing a NumPy-compatible interface backed by GPU execution.
Referenced by (3)
Full triples — surface form annotated when it differs from this entity's canonical label.