OpenCL
E163108
OpenCL is an open, cross-platform framework for writing programs that execute across heterogeneous systems including CPUs, GPUs, and other processors.
All labels observed (15)
| Label | Occurrences |
|---|---|
| OpenCL canonical | 21 |
| DirectCompute | 1 |
| OpenCL 2.0 | 1 |
| OpenCL 2.1 | 1 |
| OpenCL 3.0 | 1 |
| OpenCL C | 1 |
| OpenCL C 1.0 | 1 |
| OpenCL C 1.1 | 1 |
| OpenCL C 3.0 | 1 |
| OpenCL C language | 1 |
| OpenCL execution model | 1 |
| OpenCL memory model | 1 |
| OpenCL platform model | 1 |
| OpenCL programming model | 1 |
| OpenCL runtime API | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T1422903 — 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: OpenCL Context triple: [Intel Iris Xe, supportsAPI, OpenCL]
-
A.
NVIDIA CUDA
NVIDIA CUDA is a parallel computing platform and programming model that enables developers to use NVIDIA GPUs for general-purpose high-performance computing.
-
B.
OpenACC
OpenACC is a directive-based parallel programming standard designed to simplify the development of portable, high-performance code on heterogeneous systems such as GPUs and multicore CPUs.
-
C.
OpenMP
OpenMP is an application programming interface that supports multi-platform shared-memory parallel programming in C, C++, and Fortran.
-
D.
PlaidML
PlaidML is an open-source, hardware-agnostic deep learning engine designed to accelerate neural network computation on a wide range of GPUs and other devices.
-
E.
OpenGL
OpenGL is a cross-language, cross-platform application programming interface (API) for rendering 2D and 3D vector graphics, widely used in games, simulations, and professional visualization.
- 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: OpenCL Target entity description: OpenCL is an open, cross-platform framework for writing programs that execute across heterogeneous systems including CPUs, GPUs, and other processors.
-
A.
NVIDIA CUDA
NVIDIA CUDA is a parallel computing platform and programming model that enables developers to use NVIDIA GPUs for general-purpose high-performance computing.
-
B.
OpenACC
OpenACC is a directive-based parallel programming standard designed to simplify the development of portable, high-performance code on heterogeneous systems such as GPUs and multicore CPUs.
-
C.
OpenMP
OpenMP is an application programming interface that supports multi-platform shared-memory parallel programming in C, C++, and Fortran.
-
D.
PlaidML
PlaidML is an open-source, hardware-agnostic deep learning engine designed to accelerate neural network computation on a wide range of GPUs and other devices.
-
E.
OpenGL
OpenGL is a cross-language, cross-platform application programming interface (API) for rendering 2D and 3D vector graphics, widely used in games, simulations, and professional visualization.
- F. None of above. chosen
Statements (84)
| Predicate | Object |
|---|---|
| instanceOf |
open standard
ⓘ
parallel computing framework ⓘ |
| abbreviation | OpenCL self-link ⓘ |
| backedBy |
Advanced Micro Devices
ⓘ
surface form:
AMD
ARM ⓘ Apple Inc. ⓘ
surface form:
Apple (historically)
Intel Corporation ⓘ
surface form:
Intel
NVIDIA Corporation ⓘ
surface form:
NVIDIA
Qualcomm ⓘ |
| basedOn |
C
ⓘ
surface form:
C programming language
|
| competesWith |
NVIDIA CUDA
ⓘ
surface form:
CUDA
DirectX ⓘ
surface form:
DirectCompute
HIP ⓘ SYCL ⓘ |
| defines |
NDRange
ⓘ
command queues ⓘ contexts ⓘ kernel functions ⓘ memory objects ⓘ work-groups ⓘ work-items ⓘ |
| designedFor | heterogeneous computing ⓘ |
| developer | Khronos Group ⓘ |
| fullName | Open Computing Language ⓘ |
| governingBody | Khronos Group ⓘ |
| hasComponent |
OpenCL
self-linksurface differs
ⓘ
surface form:
OpenCL C language
OpenCL self-linksurface differs ⓘ
surface form:
OpenCL execution model
OpenCL self-linksurface differs ⓘ
surface form:
OpenCL memory model
OpenCL self-linksurface differs ⓘ
surface form:
OpenCL platform model
OpenCL self-linksurface differs ⓘ
surface form:
OpenCL programming model
OpenCL self-linksurface differs ⓘ
surface form:
OpenCL runtime API
|
| hasVersion |
Open Computing Language
ⓘ
surface form:
OpenCL 1.0
OpenCL 1.1 ⓘ OpenCL 1.2 ⓘ OpenCL self-linksurface differs ⓘ
surface form:
OpenCL 2.0
OpenCL self-linksurface differs ⓘ
surface form:
OpenCL 2.1
OpenCL 2.2 ⓘ OpenCL self-linksurface differs ⓘ
surface form:
OpenCL 3.0
|
| initialReleaseYear | 2009 ⓘ |
| is |
cross-platform
ⓘ
vendor-neutral ⓘ |
| license | royalty-free ⓘ |
| memoryHierarchy |
constant memory
ⓘ
global memory ⓘ local memory ⓘ private memory ⓘ |
| runsOn |
Android
ⓘ
Linux ⓘ Windows ⓘ macOS ⓘ |
| standardizedBy | Khronos Group ⓘ |
| supportsExecutionOn |
APU
ⓘ
CPU ⓘ DSP ⓘ FPGA ⓘ GPU ⓘ accelerator ⓘ |
| supportsFeature |
SPIR intermediate representation
ⓘ
SPIR-V intermediate representation ⓘ buffer and image objects ⓘ device querying ⓘ event-based synchronization ⓘ generic address space (from 2.0) ⓘ just-in-time compilation of kernels ⓘ pipes (from 2.0) ⓘ platform-independent source code ⓘ shared virtual memory (from 2.0) ⓘ |
| supportsLanguageVersion |
OpenCL
self-linksurface differs
ⓘ
surface form:
OpenCL C 1.0
OpenCL self-linksurface differs ⓘ
surface form:
OpenCL C 1.1
OpenCL C 1.2 ⓘ OpenCL C 2.0 ⓘ OpenCL self-linksurface differs ⓘ
surface form:
OpenCL C 3.0
|
| supportsProgrammingModel |
SIMD
ⓘ
SPMD ⓘ data parallelism ⓘ task parallelism ⓘ |
| targetDomain |
embedded computing
ⓘ
general-purpose computing on GPUs ⓘ high-performance computing ⓘ mobile computing ⓘ |
| usedFor |
GPGPU programming
ⓘ
image processing ⓘ machine learning workloads ⓘ 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: OpenCL Description of subject: OpenCL is an open, cross-platform framework for writing programs that execute across heterogeneous systems including CPUs, GPUs, and other processors.
Referenced by (35)
Full triples — surface form annotated when it differs from this entity's canonical label.
this entity surface form:
OpenCL C
this entity surface form:
OpenCL C language
this entity surface form:
OpenCL runtime API
this entity surface form:
OpenCL platform model
this entity surface form:
OpenCL execution model
this entity surface form:
OpenCL memory model
this entity surface form:
OpenCL programming model
this entity surface form:
OpenCL C 1.0
this entity surface form:
OpenCL C 1.1
this entity surface form:
OpenCL C 3.0
this entity surface form:
OpenCL 2.0
this entity surface form:
OpenCL 2.1
this entity surface form:
OpenCL 3.0
subject surface form:
Tiger Lake
subject surface form:
Adreno
subject surface form:
AMD Instinct