Accelerating Data Preprocessing with GPU-Accelerated Dataframes

GPU-accelerated dataframes, like NVIDIA’s RAPIDS cuDF, significantly speed up data processing tasks, completing operations up to 150 times faster than traditional CPU methods. This efficiency is essential as global data volumes grow, allowing data professionals to prioritize high-value activities.

Table of contents

1. What are GPU-Accelerated Dataframes and Why Do They Matter?

GPU-accelerated dataframes utilize GPUs to significantly boost computational power. Traditional dataframes, such as those used in pandas for data processing, rely solely on the CPU, which limits their maximum load, read, and write speeds. In 2024, we now understand that GPUs are not only for computationally intensive tasks like video gaming, physics-enabled weather simulations, or deep learning. They can also be harnessed for more common tasks like data processing, providing an extraordinary performance boost.

GPU-accelerated frameworks, such as NVIDIA’s RAPIDS cuDF, can speed up operations on a commonly used dataframe library like pandas by nearly 150 times! Imagine a data scientist spending a cumulative 30 minutes each day on routine data processing—RAPIDS cuDF could reduce that time to just 12 seconds. Best of all, it’s free.

Data scientists and analysts spend about 80% of their time on data preparation tasks, such as data loading, cleaning, wrangling, and feature engineering (Study). Accelerating these processes can dramatically improve efficiency, especially as data volumes grow at unprecedented rates. For example, global data grew from just 2 zettabytes in 2010 to 147 zettabytes by 2024 (Study). Optimizing data preparation through GPU acceleration not only saves time and money but also helps data professionals focus on high-value tasks like business intelligence, visualization, and machine learning much faster.

2. How to Do It?

After installing some prerequisites - you can easily run GPU-accelerated dataframes on any GPU-enabled workstation with just a couple of lines of code — no changes to your existing code required.

Just run:

%load_ext cudf.pandas
import pandas as pd

# If you can use command lines
python -m cudf.pandas script.py

# OR if you cannot use command lines
import cudf.pandas
cudf.pandas.install()

import pandas as pd

3. How it Works?

1. Import Interception:

   • The cuDF pandas intercepts when a pandas import happens and loads a custom module that wraps around both cuDF (GPU) and pandas (CPU) implementations, subsequently allowing for GPU acceleration.

2. Dual-state Proxy Objects:

   • Each DataFrame or Series is a proxy object that can exist in two states:
     • GPU state: Backed by a cuDF object in GPU memory.
     • CPU state: Backed by a pandas object in CPU memory.
   • The proxy maintains a reference to the current state and can switch between them as needed.

3. Operation Attempt:

   • When an operation is called on a proxy object:
     • It first attempts execution on the GPU using cuDF acceleration.
     • If GPU execution fails, it automatically switches to CPU execution using pandas.

Essentially, cuDF allows for data to be on both the GPU and CPU and automatically chooses the most efficient way to handle the data without manual intervention.

4. Future Outlook on Data Processing:

Data will grow to 181 zettabytes by 2025, up 34 zettabytes or 23% in just a year. This is up 8,950% since 2010 (Study)

Growth is inevitable, especially with the rising data produced by generative AI models. As the volume of data increases, how we process and manage it will become even more important.