CuPy

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CuPy topic overview

The analysis highlights Applications, Features and Overview as prominent areas in the source structure around CuPy.

Related topics: 28
Source areas: 3
Connected nodes: 31
Extracted relationships: 37
Concept neighborhoods: 13
Bridge connections: 31

What this topic covers Research coverage

Source areas are shown by the number of related topics found in each part of the analysis. Use smaller areas too: they can reveal specialized angles and content gaps.

Overview · 14 topics

Features · 9 topics

Applications · 5 topics

Smaller areas are not necessarily less important. They contain fewer connections in this analysis and can be useful for finding specialized angles or coverage gaps.

Key facts & relationships

High-confidence facts extracted from structured source data. Use them as anchors for further research.

Developers: Community, Preferred Networks, Inc.
License: MIT
Operating system: Linux, Windows
Original author: Seiya Tokui
Platform: Cross-platform
Release: September 2, 2015; 10 years ago (2015-09-02).

Explore all related topics Closing gaps

Browse the complete topic structure, not only the most central items. Less prominent entities and concepts can reveal missing angles, specialized context and useful research gaps. Each item opens a new analysis centered on that subject.

Overview

Open source Open-source software
Python Python (programming language)
NumPy
SciPy
Nvidia
CUDA
AMD
ROCm
Chainer
Summit Summit (supercomputer)
Perlmutter Perlmutter (supercomputer)
EULER Brutus cluster
ABCI AI Bridging Cloud Infrastructure
NumFOCUS NumFOCUS?action=edit&redlink=1

Features

Array Array data type
Linear algebra
Fast Fourier transform
Random number generator Random number generation
Sparse matrices Sparse matrix
Discrete Fourier transform
Image processing Digital image processing
Signal processing
Statistical functions Statistics

Applications

SpaCy
XGBoost
Berkeley SETI Berkeley SETI Research Center
Scikit-learn
MONAI Medical open network for AI

Advanced semantic analysis

Deeper signals for content research, entity SEO and topical coverage. The plain-language headings explain what each technical view is useful for.

How CuPy connects Entity context

The extracted context around CuPy shows recurring relationship patterns in the source. For example, CuPy → APIs, COO, CSC, CSR, DIA, Fourier, SciPy, Sparse, The Another extracted example is CuPy → APIs, Fourier, Multi-dimensional, NumPy, The. Use these groups to spot repeated connection types before inspecting the individual relationships.

Top relations

related to SciPy-compatible APIs · 9

CuPy → APIs, COO, CSC, CSR, DIA, Fourier, SciPy, Sparse, The

related to NumPy-compatible APIs · 5

CuPy → APIs, Fourier, Multi-dimensional, NumPy, The

related to Features · 3

CuPy → APIs, GPU, NumPy/SciPy-compatible APIs

is a · 2

CuPy → open source library for GPU-accelerated computing with Python programming language, part of the NumPy ecosystem array libraries and is widely adopted to utilize GPU with Python

related to External links · 2

CuPy → GitHub, Official

Developers · 1

CuPy → Community, Preferred Networks, Inc.

License · 1

CuPy → MIT

Operating system · 1

CuPy → Linux, Windows

Original author · 1

CuPy → Seiya Tokui

Platform · 1

CuPy → Cross-platform

Important terminology

Use these terms to understand the vocabulary surrounding the topic, not as a checklist for keyword stuffing.

Important terminology

gpu python cuda numpy array computing apis scipy set project chainer numerical platform nvidia package programming providing support multi-dimensional sparse

CuPy relationships Subject–Predicate–Object triples

TTTA extracted 37 structured relationships around CuPy. Examples in this analysis include CuPy → Developers → Community, Preferred Networks, Inc. and CuPy → License → MIT. The table shows each extracted connection, where it came from and its confidence.

Subject	Predicate	Object	Confidence	Src
CuPy	Developers	Community, Preferred Networks, Inc.	1.00	infobox
CuPy	License	MIT	1.00	infobox
CuPy	Operating system	Linux, Windows	1.00	infobox
CuPy	Original author	Seiya Tokui	1.00	infobox
CuPy	Platform	Cross-platform	1.00	infobox
CuPy	Release	September 2, 2015; 10 years ago (2015-09-02).	1.00	infobox
CuPy	Repository	github.com/cupy/cupy	1.00	infobox
CuPy	Stable release	v13.6.0 / August 18, 2025; 12 months ago (2025-08-18)	1.00	infobox
CuPy	Type	Numerical analysis	1.00	infobox
CuPy	Website	cupy.dev	1.00	infobox
CuPy	Written in	Python, Cython, CUDA	1.00	infobox
CuPy	is a	open source library for GPU-accelerated computing with Python programming language	0.90	text
CuPy	is a	part of the NumPy ecosystem array libraries and is widely adopted to utilize GPU with Python	0.90	text

Related concept clusters Concept neighborhoods

The concept neighborhoods around CuPy bring nearby vocabulary together. In this analysis, examples include Gpu, Array and Cuda. Use the clusters to find adjacent concepts and terminology that may deserve separate research.

CuPy
- Gpu
- Array
- Cuda
- Python
- Scipy
- Apis
- Numpy
- Distributed
- Features
- Kernels
- Libraries
- Low-level
cupy
- Gpu
- Array
- Cuda
- Python
- Scipy
- Apis
- Numpy
- Distributed
- Features
- Kernels
- Libraries
- Low-level
python
- Github
- Libraries
- Matrices
- Numerical
- Sparse
- Support
- Website
- Array
- Cuda
- Numpy
- Gpu
- Api
cuda
- Distributed
- Features
- Github
- Gpu
- Kernel
- Kernels
- Low-level
- Nvidia
- Operations
- Platform
- Raw
- Scipy-compatible
array
- Distributed
- Kernel
- Libraries
- Operations
- Raw
- Computing
- Cupy
- Package
- Scipy
- Apis
- Cuda
- Python
sparse matrices
- Programming
- Providing
- Sparse
- Support
- Python
- Api
- Chainer
- Distributed
- Github
- Kernel
- Libraries
- Multi-dimensional
chainer
- Distributed
- Github
- Kernel
- Libraries
- Matrices
- Nvidia
- Operations
- Programming
- Project
- Providing
- Raw
- Sparse
nvidia
- Rocm
- Chainer
- Distributed
- Github
- Kernel
- Libraries
- Operations
- Platform
- Programming
- Providing
- Raw
- Sparse

Connections between topic areas Semantic bridges

For CuPy, one of the stronger structural bridges in this analysis connects CuPy with Overview. Bridges highlight paths between different parts of the map and can reveal research angles that are easy to miss in a flat list.

Min side: 3

CuPy — Overview · splits 17 ⟂ 15

CuPy — Features · splits 22 ⟂ 10

CuPy — Applications · splits 26 ⟂ 6

Map overview Semantic statistics

CuPy

Nodes32

Edges31

Triples37

Avg. degree1.94

Density0.0625

Components1

Source & methodology

TTTA analyzes the structure around CuPy to surface related topics, entities, relationships, concept neighborhoods and bridge connections. Use the map to explore areas such as Applications, Features & Overview, including less central topics that may reveal useful research gaps. Automatically extracted connections are research leads rather than rewritten encyclopedia content.

Source: Wikipedia — CuPy · EN edition · Analysis: TopicsToTalkAbout