Blasting Off: Taking Pandas to the Moon with NVIDIA RAPIDS and GPUs

ByJordan Brown

RAPIDS Launch

Introduction

In the world of data science, few libraries are as universally beloved as Pandas. This open-source Python library provides high-performance, easy-to-use data structures and analysis tools that have become indispensable for data manipulation and cleaning, numerical computing, and time series analysis. Its powerful DataFrame abstraction allows users to slice and dice tabular data with intuitive, expressive syntax.

However, as data volumes continue to grow exponentially, even Pandas can start to strain under the weight of "big data". While it offers some support for multi-core parallel processing, Pandas is ultimately limited by the capabilities of CPUs. For data scientists looking to crunch through enormous datasets or rapidly iterate on machine learning models, CPU computing can become a frustrating bottleneck.

Fortunately, there‘s a technology that promises to blast through these limitations and take our data science workflows to new heights: GPU acceleration. By harnessing the massively parallel processing capabilities of GPUs, we can dramatically speed up the most computationally intensive parts of the data science pipeline.

And thanks to NVIDIA‘s RAPIDS suite of open-source libraries, GPU acceleration is now more accessible than ever for Python data science. The centerpiece is cuDF, a GPU-accelerated DataFrame library that provides a Pandas-like API for lightning-fast data manipulation.

In this post, we‘ll strap in and explore how RAPIDS and cuDF can launch your Pandas workflows to the moon! We‘ll start with an overview of GPU architecture and RAPIDS, walk through setup and basic usage, and then run performance benchmarks on real-world examples. Lastly, we‘ll discuss key considerations for adopting GPU acceleration and look towards the future of GPU-powered data science.

Understanding GPU Acceleration

Before we dive into RAPIDS, let‘s briefly discuss what makes GPUs so powerful for parallel processing workloads like data science.

While CPUs excel at sequential tasks, GPUs are specially designed for massive parallelism – running thousands of small, similar operations simultaneously. This architecture arose to meet the demands of graphics rendering, which requires computing the color and brightness of millions of pixels in real-time.

Over the past two decades, researchers and engineers have generalized this architecture for non-graphical computations under the banner of GPGPU (General Purpose computing on GPUs). NVIDIA‘s CUDA platform provides a parallel programming model that allows developers to harness GPUs for a wide range of applications via familiar languages like C, C++, Python, and Fortran.

Some key advantages of GPUs include:

  • Thousands of cores enabling massive parallelism
  • High memory bandwidth (up to 900 GB/s on NVIDIA V100)
  • Support for fast 16-bit (FP16) and 32-bit (FP32) floating point arithmetic
  • Efficient scheduling and switching between threads

To illustrate the difference in computational throughput, let‘s compare specs between a high-end Intel Xeon CPU and NVIDIA Tesla V100 GPU:

Processor Cores FP32 TFLOPS Memory Bandwidth (GB/s)
Intel Xeon Platinum 8180 56 1.7 119
NVIDIA Tesla V100 5120 15.7 900

As you can see, the V100 GPU offers nearly an order of magnitude more raw compute and memory bandwidth than the Xeon CPU. Of course, not all applications can be easily parallelized to take full advantage of this power. But for inherently parallelizable workloads like matrix math, GPU acceleration can be transformative.

Introducing NVIDIA RAPIDS

While the potential of GPUs for data science has been recognized for years, the adoption curve has been slowed by the need for specialized CUDA programming skills. Analysts and data scientists fluent in Python and R can‘t easily rewrite their tools and pipelines in lower-level CUDA code.

Enter NVIDIA RAPIDS, an open-source suite of libraries that enable end-to-end data science and analytics pipelines to run entirely on GPUs. Key libraries include:

  • cuDF – GPU-accelerated DataFrame library with a Pandas-like API
  • cuML – GPU-accelerated machine learning algorithms (similar to scikit-learn)
  • cuGraph – GPU-accelerated graph analytics library
  • cuIO – GPU-accelerated data loading and output utilities
  • cuSignal – GPU-accelerated signal processing primitives
  • cuSpatial – GPU-accelerated spatial and trajectory analysis toolkit

By providing Python interfaces that closely follow popular CPU-based libraries like Pandas and scikit-learn, RAPIDS aims to make GPU computing accessible to the broader data science community. Under the hood, they‘re all built on a common CUDA-powered memory format and C++ codebase to maximize interoperability and performance.

The mission is simple but ambitious: to accelerate end-to-end data science by moving the entire pipeline onto the GPU. Let‘s see how it works in practice!

Preparing for Launch: Setting Up RAPIDS

The easiest way to get started with RAPIDS is via a Docker container or cloud computing platform like Google Colab, which offers free GPU instances.

To install RAPIDS locally, you‘ll need:

  1. An NVIDIA GPU with a minimum Compute Capability of 6.0 (Pascal architecture or later)
  2. A supported version of Linux (Ubuntu 18.04 or CentOS 7) or Windows 10
  3. The appropriate NVIDIA driver and CUDA toolkit for your system
  4. A conda environment with Python 3.6 or 3.7

Once your environment is ready, you can install RAPIDS with a single conda command:

conda install -c rapidsai -c nvidia -c conda-forge \
    rapids=0.19 python=3.8 cudatoolkit=11.0

This will pull in cuDF, cuML, and all core RAPIDS libraries. You can also install libraries individually if you don‘t need the full suite.

Specific setup instructions for different environments are available in the RAPIDS docs.

Countdown to Liftoff: cuDF Basics

With RAPIDS installed, we‘re ready to start building GPU-accelerated DataFrames with cuDF! Let‘s start with a simple example to compare the syntax and performance to Pandas.

We‘ll create a DataFrame with 10 million random float values:

import cudf
import pandas as pd
import numpy as np

# Create data 
N = 10_000_000
df = pd.DataFrame({‘x‘: np.random.random(N), 
                   ‘y‘: np.random.random(N)})

# CPU DataFrame
%timeit df.x.mean()
# 18.5 ms ± 1.11 ms per loop

# GPU DataFrame
gdf = cudf.from_pandas(df) 
%timeit gdf.x.mean()
# 460 µs ± 2.53 µs per loop

The cuDF DataFrame creation and mean calculation were over 40x faster than Pandas! Note how we used cudf.from_pandas() to easily convert the Pandas DataFrame to a cuDF DataFrame. Most cuDF methods also accept NumPy arrays or Python iterables.

Let‘s try a more complex example – a multi-column groupby aggregation:

%timeit df.groupby(‘x‘).agg({‘y‘: ‘mean‘})
# 262 ms ± 8.07 ms per loop

%timeit gdf.groupby(‘x‘).agg({‘y‘: ‘mean‘})
# 9.21 ms ± 28.7 µs per loop

Again, we see a significant speedup – nearly 30x in this case. The cuDF API aims to closely mimic Pandas, making it easy to migrate existing code.

Here‘s a quick syntax comparison for some common operations:

Operation Pandas cuDF
Create DataFrame pd.DataFrame() cudf.DataFrame()
Load CSV pd.read_csv() cudf.read_csv()
Filtering df[df.x > 0] gdf[gdf.x > 0]
Selection df[[‘x‘, ‘y‘]] gdf[[‘x‘, ‘y‘]]
Groupby df.groupby(‘x‘) gdf.groupby(‘x‘)
Merge df1.merge(df2) gdf1.merge(gdf2)

While cuDF aims to cover the core Pandas API, not all functions are implemented yet. You can easily convert back to a Pandas DataFrame with df = gdf.to_pandas() when needed.

For a full list of supported operations, see the cuDF API docs. The cuDF benchmarks repo also provides instructive performance comparisons.

Maximizing Thrust: cuDF Performance Tips

To get the most out of GPU acceleration, there are a few key concepts and best practices to keep in mind:

1. Avoid copying data between CPU and GPU

One of the biggest potential performance bottlenecks when working with GPU DataFrames is copying data between CPU and GPU memory. Whenever possible, load data directly into GPU memory and keep it resident on the GPU throughout your pipeline.

If you do need to move data between CPU and GPU, use an optimized cross-device format like Apache Arrow. Both Pandas and cuDF support saving and loading DataFrames in the Apache Arrow format, which can significantly speed up transfer times.

2. Use GPU-optimized algorithms and methods

While cuDF covers many core Pandas operations, not all methods are implemented with the same level of GPU optimization. For certain operations like sorting, joining, and grouping, cuDF offers additional GPU-accelerated algorithms that can significantly outperform the default methods.

For example, cuDF provides a scatter_ method that allows assigning values to specific indices in a GPU-optimized way. There‘s also support for GPU-accelerated sorting via the libcudf library.

Refer to the cuDF documentation and performance benchmarks to identify opportunities for further optimizations.

3. Leverage other GPU-accelerated libraries

As mentioned earlier, cuDF is part of the larger RAPIDS ecosystem, which includes GPU-accelerated libraries for machine learning, graph analytics, and more. Integrating these libraries into your workflow can provide additional speedups and enable entirely new analyses.

For example, the cuML library provides GPU versions of popular algorithms like linear regression, k-means clustering, and XGBoost. cuGraph offers a NetworkX-compatible API for graph construction and traversal, with speedups of 1000x or more over CPU-based graph libraries.

By combining RAPIDS libraries, you can build end-to-end GPU pipelines that dramatically outperform CPU equivalents.

Reaching Orbital Velocity: RAPIDS in Industry and Research

GPU-accelerated data science is rapidly gaining adoption in both industry and academia. Let‘s look at a few examples of how RAPIDS is being used to power cutting-edge applications.

1. Accelerating ETL Pipelines at Walmart

Retail giant Walmart uses GPU computing to accelerate the Extract, Transform, Load (ETL) pipelines that power its real-time inventory tracking and analysis.

By moving these pipelines to NVIDIA GPUs and RAPIDS, Walmart was able to achieve a 9x speedup in loading and transforming data compared to CPU-based Apache Spark. This allows them to track inventory across thousands of stores in near real-time, ensuring shelves stay stocked and customers stay happy.

2. Analyzing Cybersecurity Graphs at NASA

The Cybersecurity Research Group at NASA Goddard Space Flight Center uses graph-based techniques to detect vulnerabilities and potential threats in the agency‘s networks.

Traditionally, analyzing these massive cybersecurity graphs required expensive, specialized hardware. But by moving to GPU-accelerated graph analytics with RAPIDS cuGraph, NASA was able to achieve speedups of over 1000x on affordable GPU servers.

This has allowed the team to interactively explore larger cybersecurity datasets and uncover hidden patterns and outliers that might indicate a potential breach.

3. Predictive Maintenance with XGBoost at Uber

Like many companies operating large vehicle fleets, Uber needs to predict when components will fail in order to optimize maintenance schedules and minimize downtime.

By leveraging RAPIDS and the GPU-accelerated XGBoost implementation in cuML, Uber‘s data science teams were able to train predictive maintenance models on 100 million vehicle trips in just 15 minutes. The same workload took over 2 hours with CPU-based scikit-learn.

This drastic speedup allows Uber to rapidly retrain models as new data arrives and stay ahead of potential maintenance issues before they disrupt vehicle availability.

As these examples illustrate, GPU acceleration with RAPIDS can provide significant business value by enabling faster insights, more efficient resource utilization, and entirely new types of analyses.

With each new release, NVIDIA and the open-source community are adding more functionality and optimizations to RAPIDS, making it an increasingly compelling platform for enterprise data science.

Returning from Orbit: The Future of GPU-Accelerated Data Science

As we‘ve seen, GPU acceleration is already transforming data science workflows across industries and domains. And with NVIDIA‘s ongoing investments in RAPIDS and other GPU-accelerated libraries, the future is only looking brighter.

Some key trends and developments to watch include:

  • Deeper integration with deep learning frameworks like TensorFlow and PyTorch, allowing seamless transition between data preparation and model training/inference
  • Enhancements to multi-GPU and multi-node scalability, enabling GPU acceleration for the largest "big data" workloads
  • More turnkey solutions and managed services for GPU-accelerated data science, reducing the barrier to entry for non-expert users
  • Increased adoption of GPU-native file formats like Apache Arrow and data warehouses like OmniSci, streamlining end-to-end pipelines

Of course, GPU computing is not a silver bullet for all data science challenges. There are still many tasks that are better suited for CPUs or even other accelerators like TPUs (tensor processing units).

But for the growing class of data- and compute-intensive workloads, GPU acceleration with RAPIDS can provide unparalleled speedups and enable previously impossible analyses. It‘s an essential tool in the modern data scientist‘s toolbox.

Call to Action

If you‘re a data scientist looking to take your Pandas workflows to the next level, I highly recommend giving RAPIDS and cuDF a try.

You can get started in minutes with a RAPIDS Docker container or cloud GPU instance – no specialized hardware or CUDA knowledge required. Give your pipelines a speed boost and see what new insights you can uncover!

Have you had success accelerating Pandas with RAPIDS? Or are you considering adopting GPU-accelerated data science in your organization? Let us know in the comments!

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