CSR (Compressed Sparse Row)
E839061
CSR (Compressed Sparse Row) is a memory-efficient sparse matrix storage format that stores only nonzero elements and their indices in row-major order to enable fast arithmetic and matrix–vector operations.
All labels observed (1)
| Label | Occurrences |
|---|---|
| CSR (Compressed Sparse Row) canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T10068647 — 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: CSR (Compressed Sparse Row) Context triple: [cuSPARSE, supportsMatrixFormat, CSR (Compressed Sparse Row)]
-
A.
SparseMatrixCSC
SparseMatrixCSC is a Julia data type representing sparse matrices stored in compressed sparse column (CSC) format for efficient memory use and linear algebra operations.
-
B.
SparseArrays
SparseArrays is a Julia standard library module that provides data structures and operations for efficiently working with sparse matrices and related linear algebra.
-
C.
Optimized Row Columnar
Optimized Row Columnar (ORC) is a highly efficient, columnar storage file format commonly used in big data systems like Apache Hive to enable fast query performance and effective data compression.
-
D.
Cauchy matrix
A Cauchy matrix is a structured matrix whose entries are defined by the reciprocals of pairwise differences of two sequences, widely used in numerical analysis, interpolation, and algebra.
-
E.
Vandermonde matrix
A Vandermonde matrix is a structured matrix whose rows (or columns) are geometric progressions of given numbers, widely used in polynomial interpolation, determinant theory, and numerical analysis.
- 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: CSR (Compressed Sparse Row) Target entity description: CSR (Compressed Sparse Row) is a memory-efficient sparse matrix storage format that stores only nonzero elements and their indices in row-major order to enable fast arithmetic and matrix–vector operations.
-
A.
SparseMatrixCSC
SparseMatrixCSC is a Julia data type representing sparse matrices stored in compressed sparse column (CSC) format for efficient memory use and linear algebra operations.
-
B.
SparseArrays
SparseArrays is a Julia standard library module that provides data structures and operations for efficiently working with sparse matrices and related linear algebra.
-
C.
Optimized Row Columnar
Optimized Row Columnar (ORC) is a highly efficient, columnar storage file format commonly used in big data systems like Apache Hive to enable fast query performance and effective data compression.
-
D.
Cauchy matrix
A Cauchy matrix is a structured matrix whose entries are defined by the reciprocals of pairwise differences of two sequences, widely used in numerical analysis, interpolation, and algebra.
-
E.
Vandermonde matrix
A Vandermonde matrix is a structured matrix whose rows (or columns) are geometric progressions of given numbers, widely used in polynomial interpolation, determinant theory, and numerical analysis.
- F. None of above. chosen
Statements (46)
| Predicate | Object |
|---|---|
| instanceOf |
data structure
ⓘ
sparse matrix storage format ⓘ |
| advantageOverDense |
avoids computation on zero entries
ⓘ
reduces memory for highly sparse matrices ⓘ |
| alsoKnownAs |
CRS
ⓘ
CSR format ⓘ Compressed Sparse Row NERFINISHED ⓘ compressed row storage ⓘ |
| complexity |
O(nnz) storage complexity
ⓘ
O(nnz) time for matrix–vector multiplication ⓘ |
| component |
column indices array
ⓘ
row pointer array ⓘ values array ⓘ |
| contrastedWith |
COO (Coordinate format)
ⓘ
CSC (Compressed Sparse Column) ⓘ DIA (Diagonal format) NERFINISHED ⓘ |
| domain |
numerical linear algebra
ⓘ
sparse matrix computation ⓘ |
| limitation |
inefficient for column slicing
ⓘ
insertion of new nonzeros is expensive ⓘ |
| property |
enables fast arithmetic operations
ⓘ
enables fast matrix–vector operations ⓘ good cache locality for row-wise access ⓘ memory-efficient for sparse matrices ⓘ stores only nonzero entries ⓘ supports efficient row slicing ⓘ |
| rowPointerArrayMeaning | prefix sum of nonzeros per row ⓘ |
| storageOrder | row-major ⓘ |
| stores |
column indices of nonzero elements
ⓘ
nonzero matrix elements ⓘ row pointer array ⓘ |
| supportedBy |
Eigen
NERFINISHED
ⓘ
MKL NERFINISHED ⓘ PETSc NERFINISHED ⓘ SciPy NERFINISHED ⓘ cuSPARSE NERFINISHED ⓘ |
| typicalIndexBase | 0-based indices GENERATED ⓘ |
| typicalUseCase |
finite element methods
ⓘ
sparse linear algebra libraries ⓘ |
| usedFor |
graph algorithms
ⓘ
high‑performance computing ⓘ iterative linear solvers ⓘ scientific computing ⓘ sparse matrix–matrix multiplication ⓘ sparse matrix–vector multiplication ⓘ storing sparse matrices ⓘ |
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: CSR (Compressed Sparse Row) Description of subject: CSR (Compressed Sparse Row) is a memory-efficient sparse matrix storage format that stores only nonzero elements and their indices in row-major order to enable fast arithmetic and matrix–vector operations.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.