WMMA API
E790552
The WMMA API is NVIDIA’s programming interface that lets developers perform warp-level matrix multiply-accumulate operations to efficiently leverage Tensor Cores for mixed-precision linear algebra.
All labels observed (1)
| Label | Occurrences |
|---|---|
| WMMA API canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T9298150 — 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: WMMA API Context triple: [Tensor Cores, exposedThrough, WMMA API]
-
A.
MMArena
MMArena is a modern football stadium in Le Mans, France, primarily used for hosting Le Mans FC’s home matches and other sporting events.
-
B.
WWA
WWA is the National Rail station code for Woolwich Arsenal railway station in southeast London.
-
C.
WSM
WSM is the three-letter ISO 3166-1 alpha-3 country code assigned to Samoa.
-
D.
WWC
WWC is a U.S. Department of Education initiative that reviews and summarizes research evidence on educational programs, practices, and policies to inform educators and policymakers.
-
E.
WMN
WMN is the National Rail station code for Warminster railway station in Wiltshire, England.
- 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: WMMA API Target entity description: The WMMA API is NVIDIA’s programming interface that lets developers perform warp-level matrix multiply-accumulate operations to efficiently leverage Tensor Cores for mixed-precision linear algebra.
-
A.
MMArena
MMArena is a modern football stadium in Le Mans, France, primarily used for hosting Le Mans FC’s home matches and other sporting events.
-
B.
WWA
WWA is the National Rail station code for Woolwich Arsenal railway station in southeast London.
-
C.
WSM
WSM is the three-letter ISO 3166-1 alpha-3 country code assigned to Samoa.
-
D.
WWC
WWC is a U.S. Department of Education initiative that reviews and summarizes research evidence on educational programs, practices, and policies to inform educators and policymakers.
-
E.
WMN
WMN is the National Rail station code for Warminster railway station in Wiltshire, England.
- F. None of above. chosen
Statements (48)
| Predicate | Object |
|---|---|
| instanceOf |
CUDA API feature
ⓘ
programming interface ⓘ warp-level matrix multiply-accumulate API ⓘ |
| abbreviationFor | Warp Matrix Multiply-Accumulate API NERFINISHED ⓘ |
| developedBy | NVIDIA NERFINISHED ⓘ |
| documentationPublisher | NVIDIA NERFINISHED ⓘ |
| documentedIn |
CUDA C++ Programming Guide
NERFINISHED
ⓘ
CUDA Toolkit documentation ⓘ |
| executionModel | SIMT warp execution ⓘ |
| exposedVia | CUDA C++ headers ⓘ |
| granularity | warp-level ⓘ |
| introducedFor | Volta architecture Tensor Cores NERFINISHED ⓘ |
| levelOfAbstraction | low-level Tensor Core access ⓘ |
| namespace | nvcuda::wmma NERFINISHED ⓘ |
| optimizationGoal |
efficient Tensor Core utilization
ⓘ
high throughput matrix operations ⓘ |
| partOf | CUDA Toolkit NERFINISHED ⓘ |
| primaryLanguage | C++ ⓘ |
| programmingModelLevel | device-level API ⓘ |
| providesFunction |
fill_fragment
ⓘ
load_matrix_sync ⓘ mma_sync ⓘ store_matrix_sync ⓘ |
| providesType | fragment ⓘ |
| relatedTo |
CUDA core matrix operations
ⓘ
CUTLASS NERFINISHED ⓘ Tensor Core programming ⓘ cuBLAS NERFINISHED ⓘ |
| requires | CUDA-capable GPU with Tensor Cores ⓘ |
| requiresConcept |
CUDA warps
NERFINISHED
ⓘ
shared memory tiling ⓘ thread blocks ⓘ |
| supportsDataType |
half precision floating point
ⓘ
mixed precision ⓘ single precision floating point accumulation ⓘ |
| supportsFeature |
layout specification for matrices
ⓘ
row-major and column-major layouts ⓘ tile-based matrix operations ⓘ |
| supportsOperation |
matrix multiply-accumulate
ⓘ
mixed-precision linear algebra ⓘ |
| targetHardware | NVIDIA GPUs NERFINISHED ⓘ |
| targetHardwareFeature | Tensor Cores ⓘ |
| typicalDomain |
GPU-accelerated linear algebra
ⓘ
neural network inference ⓘ neural network training ⓘ |
| useCase |
GEMM acceleration
ⓘ
deep learning workloads ⓘ high-performance 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: WMMA API Description of subject: The WMMA API is NVIDIA’s programming interface that lets developers perform warp-level matrix multiply-accumulate operations to efficiently leverage Tensor Cores for mixed-precision linear algebra.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.