ELL (ELLPACK)
E839062
ELL (ELLPACK) is a sparse matrix storage format that stores each row with a fixed number of nonzero elements, enabling efficient and regular memory access patterns on parallel architectures like GPUs.
All labels observed (1)
| Label | Occurrences |
|---|---|
| ELL (ELLPACK) canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T10068651 — 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: ELL (ELLPACK) Context triple: [cuSPARSE, supportsMatrixFormat, ELL (ELLPACK)]
-
A.
LINPACK
LINPACK is a widely used benchmark and software library for performing numerical linear algebra computations, particularly solving systems of linear equations.
-
B.
LAPACK
LAPACK is a widely used software library that provides highly optimized routines for numerical linear algebra operations such as solving systems of equations, eigenvalue problems, and singular value decompositions.
-
C.
arpack
arpack is a numerical software library for efficiently computing a few eigenvalues and eigenvectors of large sparse matrices, commonly used in scientific computing and machine learning.
-
D.
Algol 68 Genie
Algol 68 Genie is a modern, open-source implementation of the Algol 68 programming language designed for contemporary systems and practical use.
-
E.
Algol 68R
Algol 68R is a revised, more practical and implementable version of the Algol 68 programming language, created to simplify and clarify the original language’s complex design.
- 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: ELL (ELLPACK) Target entity description: ELL (ELLPACK) is a sparse matrix storage format that stores each row with a fixed number of nonzero elements, enabling efficient and regular memory access patterns on parallel architectures like GPUs.
-
A.
LINPACK
LINPACK is a widely used benchmark and software library for performing numerical linear algebra computations, particularly solving systems of linear equations.
-
B.
LAPACK
LAPACK is a widely used software library that provides highly optimized routines for numerical linear algebra operations such as solving systems of equations, eigenvalue problems, and singular value decompositions.
-
C.
arpack
arpack is a numerical software library for efficiently computing a few eigenvalues and eigenvectors of large sparse matrices, commonly used in scientific computing and machine learning.
-
D.
Algol 68 Genie
Algol 68 Genie is a modern, open-source implementation of the Algol 68 programming language designed for contemporary systems and practical use.
-
E.
Algol 68R
Algol 68R is a revised, more practical and implementable version of the Algol 68 programming language, created to simplify and clarify the original language’s complex design.
- F. None of above. chosen
Statements (46)
| Predicate | Object |
|---|---|
| instanceOf |
data structure
ⓘ
matrix representation ⓘ sparse matrix storage format ⓘ |
| advantageOver | CSR for regular GPU SpMV due to more regular memory access ⓘ |
| alsoKnownAs |
ELL format
ⓘ
ELLPACK sparse format ⓘ |
| comparedTo |
COO (Coordinate) format
ⓘ
CSR (Compressed Sparse Row) format ⓘ |
| componentOf | hybrid sparse formats such as HYB (ELL+COO) ⓘ |
| dataStored |
column indices of nonzero values
ⓘ
nonzero values ⓘ |
| designedFor | efficient parallel sparse matrix-vector multiplication ⓘ |
| disadvantageComparedTo | CSR for highly irregular sparsity patterns ⓘ |
| fullName | ELLPACK NERFINISHED ⓘ |
| hasProperty |
best suited for matrices with nearly uniform number of nonzeros per row
ⓘ
each thread can process one row independently ⓘ enables coalesced memory accesses on GPUs ⓘ fixed-stride access pattern ⓘ good cache utilization for regular sparsity patterns ⓘ indexing typically uses (row, k) where k is position within fixed row length ⓘ inefficient for matrices with highly variable row lengths ⓘ may incur storage overhead due to padding ⓘ memory layout is typically column-major for the ELL arrays ⓘ no per-row pointer array is required ⓘ often used in GPU-accelerated linear algebra libraries ⓘ padding overhead increases with row length imbalance ⓘ row length equals maximum number of nonzeros in any row ⓘ rows are padded with zeros or dummy entries to a uniform length ⓘ simple indexing arithmetic ⓘ stores each matrix row with a fixed number of nonzero entries ⓘ supports efficient vectorized access across rows ⓘ supports straightforward parallelization over rows ⓘ uses regular memory access patterns ⓘ uses two main arrays: values and column indices ⓘ well-suited for GPU architectures ⓘ well-suited for SIMD architectures ⓘ well-suited for many-core processors ⓘ |
| originField | numerical linear algebra ⓘ |
| requires | knowledge of maximum nonzeros per row at construction time ⓘ |
| usedFor |
SpMV
NERFINISHED
ⓘ
sparse matrix-vector multiplication ⓘ storing sparse matrices ⓘ |
| usedIn |
GPU computing
ⓘ
high-performance computing ⓘ iterative solvers for sparse linear systems ⓘ 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: ELL (ELLPACK) Description of subject: ELL (ELLPACK) is a sparse matrix storage format that stores each row with a fixed number of nonzero elements, enabling efficient and regular memory access patterns on parallel architectures like GPUs.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.