Dask-cuDF
E890460
Dask-cuDF is a RAPIDS library that enables distributed, GPU-accelerated DataFrame processing by integrating cuDF with Dask for scalable data analytics.
All labels observed (1)
| Label | Occurrences |
|---|---|
| Dask-cuDF canonical | 1 |
How this entity was disambiguated
This entity first appeared as the object of triple T10882140 — 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: Dask-cuDF Context triple: [NVIDIA RAPIDS, component, Dask-cuDF]
-
A.
Dask
Dask is an open-source parallel computing library for Python that enables scalable, distributed data processing and analytics using familiar interfaces like NumPy, pandas, and scikit-learn.
-
B.
NVIDIA RAPIDS
NVIDIA RAPIDS is an open-source suite of GPU-accelerated data science and analytics libraries designed to speed up end-to-end machine learning and data processing workflows.
-
C.
Databricks
Databricks is a cloud-based data and AI company best known for its unified analytics platform built around Apache Spark, enabling large-scale data engineering, data science, and machine learning workloads.
-
D.
CuPy
CuPy is an open-source array library for Python that accelerates numerical computing by providing a NumPy-compatible interface backed by GPU execution.
-
E.
Apache Spark
Apache Spark is an open-source, distributed data processing engine designed for large-scale data analytics, machine learning, and stream processing.
- 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: Dask-cuDF Target entity description: Dask-cuDF is a RAPIDS library that enables distributed, GPU-accelerated DataFrame processing by integrating cuDF with Dask for scalable data analytics.
-
A.
Dask
Dask is an open-source parallel computing library for Python that enables scalable, distributed data processing and analytics using familiar interfaces like NumPy, pandas, and scikit-learn.
-
B.
NVIDIA RAPIDS
NVIDIA RAPIDS is an open-source suite of GPU-accelerated data science and analytics libraries designed to speed up end-to-end machine learning and data processing workflows.
-
C.
Databricks
Databricks is a cloud-based data and AI company best known for its unified analytics platform built around Apache Spark, enabling large-scale data engineering, data science, and machine learning workloads.
-
D.
CuPy
CuPy is an open-source array library for Python that accelerates numerical computing by providing a NumPy-compatible interface backed by GPU execution.
-
E.
Apache Spark
Apache Spark is an open-source, distributed data processing engine designed for large-scale data analytics, machine learning, and stream processing.
- F. None of above. chosen
Statements (50)
| Predicate | Object |
|---|---|
| instanceOf |
GPU-accelerated data processing framework
ⓘ
Python library ⓘ software library ⓘ |
| basedOn |
Dask
NERFINISHED
ⓘ
cuDF NERFINISHED ⓘ |
| compatibleWith | Pandas-like DataFrame API via cuDF ⓘ |
| developer | NVIDIA NERFINISHED ⓘ |
| documentation | https://docs.rapids.ai/api/dask-cudf/stable ⓘ |
| ecosystem | RAPIDS AI ecosystem NERFINISHED ⓘ |
| genre |
data analytics library
ⓘ
dataframe library ⓘ distributed computing framework ⓘ |
| homepage | https://rapids.ai ⓘ |
| integratesWith |
Dask
NERFINISHED
ⓘ
RAPIDS cuGraph NERFINISHED ⓘ RAPIDS cuML NERFINISHED ⓘ cuDF NERFINISHED ⓘ |
| license | Apache License 2.0 ⓘ |
| operatingSystem | Linux ⓘ |
| optimizedFor | NVIDIA GPU hardware NERFINISHED ⓘ |
| partOf | RAPIDS NERFINISHED ⓘ |
| programmingLanguage | Python ⓘ |
| purpose |
big data processing on GPUs
ⓘ
distributed GPU-accelerated DataFrame processing ⓘ scalable data analytics ⓘ |
| repository | https://github.com/rapidsai/cudf ⓘ |
| requires |
CUDA-capable GPU
ⓘ
Dask NERFINISHED ⓘ cuDF NERFINISHED ⓘ |
| supportsFeature |
lazy evaluation
ⓘ
multi-GPU scaling ⓘ multi-node distributed execution ⓘ out-of-core computation ⓘ parallel I/O ⓘ task scheduling via Dask ⓘ |
| supportsFormat |
CSV
ⓘ
JSON lines ⓘ ORC ⓘ Parquet NERFINISHED ⓘ |
| supportsLanguage | Python ⓘ |
| supportsOperation |
aggregation
ⓘ
filter ⓘ groupby ⓘ join ⓘ window operations ⓘ |
| typicalUseCase |
distributed feature engineering for machine learning
ⓘ
interactive analytics on large tabular datasets ⓘ large-scale ETL on GPUs ⓘ |
| uses |
CUDA
NERFINISHED
ⓘ
NVIDIA GPUs NERFINISHED ⓘ |
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: Dask-cuDF Description of subject: Dask-cuDF is a RAPIDS library that enables distributed, GPU-accelerated DataFrame processing by integrating cuDF with Dask for scalable data analytics.
Referenced by (1)
Full triples — surface form annotated when it differs from this entity's canonical label.