cuSOLVER
E760426
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.
All labels observed (1)
| Label | Occurrences |
|---|---|
| cuSOLVER canonical | 2 |
How this entity was disambiguated
This entity first appeared as the object of triple T8823428 — 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: cuSOLVER Context triple: [cuBLAS, relatedLibrary, cuSOLVER]
-
A.
cuSPARSE
cuSPARSE is NVIDIA’s GPU-accelerated library providing high-performance sparse linear algebra routines for CUDA applications.
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B.
cuBLAS
cuBLAS is NVIDIA’s GPU-accelerated implementation of the BLAS linear algebra library, providing high-performance matrix and vector operations for CUDA applications.
-
C.
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.
-
D.
cuDNN
cuDNN is NVIDIA’s GPU-accelerated library of optimized primitives for deep neural networks, widely used to speed up training and inference in frameworks like TensorFlow and PyTorch.
-
E.
BLAS
BLAS (Basic Linear Algebra Subprograms) is a standardized collection of low-level routines for performing common linear algebra operations such as vector and matrix multiplication, widely used as a performance-optimized foundation in scientific computing.
- 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: cuSOLVER Target entity description: 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.
-
A.
cuSPARSE
cuSPARSE is NVIDIA’s GPU-accelerated library providing high-performance sparse linear algebra routines for CUDA applications.
-
B.
cuBLAS
cuBLAS is NVIDIA’s GPU-accelerated implementation of the BLAS linear algebra library, providing high-performance matrix and vector operations for CUDA applications.
-
C.
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.
-
D.
cuDNN
cuDNN is NVIDIA’s GPU-accelerated library of optimized primitives for deep neural networks, widely used to speed up training and inference in frameworks like TensorFlow and PyTorch.
-
E.
BLAS
BLAS (Basic Linear Algebra Subprograms) is a standardized collection of low-level routines for performing common linear algebra operations such as vector and matrix multiplication, widely used as a performance-optimized foundation in scientific computing.
- F. None of above. chosen
Statements (50)
| Predicate | Object |
|---|---|
| instanceOf |
GPU-accelerated linear algebra library
ⓘ
NVIDIA CUDA library ⓘ |
| developer | NVIDIA NERFINISHED ⓘ |
| distributionForm |
shared library
ⓘ
static library ⓘ |
| hasComponent |
cuSOLVER Dense (LAPACK-like) module
ⓘ
cuSOLVER RF (refactorization) module NERFINISHED ⓘ cuSOLVER Sparse module NERFINISHED ⓘ |
| includedIn | NVIDIA HPC SDK NERFINISHED ⓘ |
| integratesWith |
Thrust
NERFINISHED
ⓘ
cuBLAS NERFINISHED ⓘ cuFFT NERFINISHED ⓘ cuSPARSE NERFINISHED ⓘ |
| languageBinding |
C
NERFINISHED
ⓘ
C++ ⓘ |
| license | proprietary NVIDIA license ⓘ |
| optimizedFor |
GPU acceleration
ⓘ
high performance computing ⓘ |
| partOf | CUDA Toolkit NERFINISHED ⓘ |
| programmingModel | CUDA NERFINISHED ⓘ |
| providesFactorization |
Cholesky factorization
ⓘ
LU factorization ⓘ QR factorization ⓘ SVD factorization ⓘ |
| providesRoutineType |
dense linear algebra
ⓘ
eigenvalue problem solvers ⓘ least squares solvers ⓘ linear system solvers ⓘ matrix factorizations ⓘ sparse linear algebra ⓘ |
| requires |
CUDA runtime
NERFINISHED
ⓘ
CUDA-capable GPU ⓘ |
| supportsDataType |
complex numbers
ⓘ
real numbers ⓘ |
| supportsHardware | NVIDIA GPU NERFINISHED ⓘ |
| supportsMatrixFormat |
dense matrix
ⓘ
sparse matrix ⓘ |
| supportsPrecision |
double precision floating point
ⓘ
single precision floating point ⓘ |
| supportsSparseFormat |
CSC format
GENERATED
ⓘ
CSR format GENERATED ⓘ block sparse formats GENERATED ⓘ |
| targetDomain |
data analytics
ⓘ
engineering simulation ⓘ machine learning ⓘ scientific computing ⓘ |
| useCase |
computing eigenvalues and eigenvectors
ⓘ
computing singular value decompositions ⓘ least squares fitting ⓘ solving linear systems Ax=b ⓘ |
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: cuSOLVER Description of subject: 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.
Referenced by (2)
Full triples — surface form annotated when it differs from this entity's canonical label.