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DATA SCIENCE A quick introduction to cuDF, an NVIDIA framework for accelerating Pandas 6 min read

Pandas remains a crucial tool in data analytics and machine learning endeavors, offering extensive capabilities for tasks such as data reading, transformation, cleaning, and writing. However, its efficiency with large datasets is somewhat limited, hindering its application in production environments or for constructing resilient data pipelines, despite its widespread use in data science projects.

Similar to Apache Spark, Pandas loads the data into memory for computation and transformation. But unlike Spark, Pandas is not a a distributed compute platform, and therefore everything must be done on a single system CPU and memory (single-node processing). This feature limits the use of Pandas in two ways:

The first issue is addressed by frameworks such as Dask. Dask DataFrame helps you process large tabular data by parallelizing Pandas on a distributed cluster of computers. In many ways, Pandas empowered by Dask is similar to Apache Spark (however, still Spark can handle large datasets more efficiently and thats why it is a preffered tool among data engineers).

Although Dask enables parallel processing of large datasets across a cluster of machines, in reality, the data for most machine learning projects can be accommodated within a single systems memory. Consequently, employing a cluster of machines for such projects might be excessive. Thus, there is a need for a tool that efficiently executes Pandas operations in parallel on a single machine, addressing the second issue mentioned earlier.

Whenever someone talks about parallel processing, the first word that comes to most engineers' minds is GPU. For a long time, it was a wish to run Pandas on GPU for efficient parallel computing. The wish came true with the introduction of NVIDIA RAPIDS cuDF. cuDF (pronounced KOO-dee-eff) is a GPU DataFrame library for

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How to Empower Pandas with GPUs. A quick introduction to cuDF, an NVIDIA | by Naser Tamimi | Apr, 2024 - Towards Data Science