Scaling Data Processing with RAPIDS cuDF: Handling One Billion Rows

Summary: Processing large datasets efficiently is a significant challenge in data science. RAPIDS cuDF, a GPU-accelerated DataFrame library, offers a solution by accelerating pandas workflows. This article explores how RAPIDS cuDF’s pandas accelerator mode can handle one billion rows of data, highlighting its key features and performance benefits.

Handling Large Datasets: The Challenge

Data scientists often face the daunting task of processing vast amounts of data. Traditional CPU-bound pandas workflows can become bottlenecks as data sizes grow. This is where RAPIDS cuDF comes into play, providing a GPU-accelerated alternative that can significantly speed up data processing.

RAPIDS cuDF: An Overview

RAPIDS cuDF is a Python GPU DataFrame library designed to load, join, aggregate, and filter data. It offers a pandas-like API, making it easy for data scientists to transition from traditional pandas workflows. The library is part of the RAPIDS suite, a collection of open-source GPU-accelerated data science and AI libraries.

Key Features of RAPIDS cuDF 24.08

The latest version of RAPIDS cuDF includes two critical features for efficient data processing:

  • Large String Support: cuDF now supports up to 2.1 billion rows of tabular text data, making it ideal for text-heavy datasets used in large language models (LLMs) and other demanding use cases.
  • Managed Memory with Prefetching: This feature enables improved performance of large data sizes by optimizing memory utilization of the CPU+GPU system.

Processing One Billion Rows: A Performance Test

To demonstrate the capabilities of RAPIDS cuDF, a performance test was conducted using the One Billion Row Challenge. This challenge involves processing a 13.1 GB text file, aggregating numeric data by group, and sorting a table.

Hardware Setup:

  • NVIDIA A100 Tensor Core GPU: With 80 GB of memory, this GPU provides ample resources for processing large datasets.
  • Arm Neoverse-N1 CPU: Paired with 500 GiB of RAM, this setup ensures that both CPU and GPU are utilized efficiently.
  • Samsung MZ1L23T8HBLA SSD: Fast storage is crucial for handling large data files.

Performance Results:

  • cuDF vs. pandas: For 200 million rows, cuDF showed a runtime of approximately 6 seconds, compared to pandas’ 50 seconds. However, for 300 million rows, the string overflow in cuDF 24.06 caused pandas to fall back, increasing runtime to about 240 seconds.
  • libcudf for Lower Overhead: For applications requiring faster runtimes, RAPIDS libcudf offers single-threaded data chunking and multithreaded data pipelining. The brc_pipeline example achieved the fastest runtime, completing the challenge in about 5.2 seconds using 256 chunks and four threads.

Optimizing Data Processing with libcudf

libcudf provides a module of new C++ examples named billion_rows, which includes:

  • brc: Simple, single-batch processing of the 13 GB input file.
  • brc_chunks: Chunking pattern that reads byte ranges from the input file, computes partial aggregations, and combines the final result.
  • brc_pipeline: Pipelining pattern that uses multiple host threads and device CUDA streams to complete the chunked work while saturating copy bandwidth and compute capacity.

Conclusion:

RAPIDS cuDF’s pandas accelerator mode offers a powerful solution for processing large datasets. By leveraging GPU acceleration, data scientists can achieve significant speedups without changing their code. The addition of large string support and managed memory with prefetching in RAPIDS cuDF 24.08 makes it an ideal choice for demanding data processing tasks.

Getting Started:

To experience the benefits of RAPIDS cuDF, follow these steps:

  1. Download RAPIDS 24.08: Visit the RAPIDS Installation Guide for detailed instructions.
  2. Explore Demo Notebooks: Try the Unified Memory demo notebook and the Job Postings demo notebook to learn more about the expanded memory capability and large strings preprocessing workflow.

By integrating RAPIDS cuDF into your data processing workflows, you can unlock the full potential of GPU acceleration and tackle even the most challenging datasets with ease.