How to Work with Data Exceeding VRAM in the Polars GPU Engine

Jamil Semaan

In high-stakes fields such as quant finance, algorithmic trading, and fraud detection, data practitioners frequently need to process hundreds of gigabytes (GB)…

NVIDIA

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Jamil Semaan

•4 min read•advanced•

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Overview

This article discusses strategies for processing large datasets that exceed GPU VRAM using the Polars GPU engine, specifically focusing on Unified Virtual Memory (UVM) and multi-GPU streaming execution. These techniques enable data practitioners in fields like quant finance and algorithmic trading to efficiently handle hundreds of gigabytes to terabytes of data.

What You'll Learn

1

How to leverage Unified Virtual Memory for datasets larger than GPU VRAM

2

How to implement multi-GPU streaming execution for large-scale data processing

3

When to choose UVM over multi-GPU streaming execution

Prerequisites & Requirements

Understanding of GPU architecture and memory management
Familiarity with the Polars GPU engine and NVIDIA cuDF(optional)

Key Questions Answered

What is Unified Virtual Memory and how does it work?

Unified Virtual Memory (UVM) creates a shared memory space between system RAM and GPU VRAM, allowing data to spill over to system RAM when VRAM is full. This prevents out-of-memory errors and enables processing of larger datasets by automatically managing data transfers between RAM and VRAM.

How does multi-GPU streaming execution improve performance?

Multi-GPU streaming execution allows for distributing workloads across multiple GPUs, enabling parallel processing of large datasets. It partitions data and rewrites the internal representation for batched execution, significantly enhancing performance for datasets ranging from hundreds of gigabytes to terabytes.

When should I use UVM versus multi-GPU streaming execution?

UVM is ideal for datasets that are moderately larger than the available VRAM, while multi-GPU streaming execution is best suited for very large datasets that require distributed processing across multiple GPUs. The choice depends on the scale of the data and available hardware.

Key Statistics & Figures

Performance on PDS-H benchmark

Processing all 22 queries in seconds at 3 TB scale

This demonstrates the effectiveness of the multi-GPU streaming execution in handling large datasets.

Technologies & Tools

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Data Processing Library

Polars

Used for data manipulation and processing with GPU acceleration.

GPU Computing Framework

Nvidia Cudf

Powers the Polars GPU engine for accelerated data processing.

Key Actionable Insights

1
Utilize Unified Virtual Memory to handle datasets larger than your GPU's VRAM seamlessly.
This approach allows data practitioners to avoid out-of-memory errors while leveraging GPU acceleration, making it suitable for moderately large datasets.

2
Experiment with multi-GPU streaming execution for processing terabyte-scale datasets.
This experimental feature can significantly improve performance by distributing workloads across multiple GPUs, making it ideal for high-stakes fields like algorithmic trading.

3
Fine-tune the RAPIDS Memory Manager (RMM) to optimize performance when using UVM.
Smart configurations can help mitigate the performance overhead associated with data migration between system RAM and VRAM, ensuring efficient data processing.

Common Pitfalls

1

Underestimating the performance overhead of data migration in UVM.

While UVM allows for larger datasets, the automatic data transfer between system RAM and VRAM can introduce latency. Proper configuration of the RAPIDS Memory Manager can help minimize this issue.

Related Concepts

GPU Memory Management

Data Processing Techniques

Parallel Computing Strategies