Column-Oriented Datalog on the GPU

TL;DR

This paper introduces VFLog, a column-oriented Datalog engine optimized for GPUs, achieving significant performance improvements over CPU-based and existing GPU Datalog engines in knowledge representation and reasoning tasks.

Contribution

It presents the first GPU-optimized column-oriented Datalog engine, leveraging modern GPU capabilities for enhanced performance.

Findings

Over 200x faster than state-of-the-art CPU engines

2.5x faster than existing GPU Datalog engines

Effective for knowledge representation and reasoning workloads

Abstract

Datalog is a logic programming language widely used in knowledge representation and reasoning (KRR), program analysis, and social media mining due to its expressiveness and high performance. Traditionally, Datalog engines use either row-oriented or column-oriented storage. Engines like VLog and Nemo favor column-oriented storage for efficiency on limited-resource machines, while row-oriented engines like Souffle use advanced data structures with locking to perform better on multi-core CPUs. The advent of modern datacenter GPUs, such as the NVIDIA H100 with its ability to run over 16k threads simultaneously and high memory bandwidth, has reopened the debate on which storage layout is more effective. This paper presents the first column-oriented Datalog engines tailored to the strengths of modern GPUs. We present VFLog, a CUDA-based Datalog runtime library with a column-oriented GPU datastructure that supports all necessary relational algebra operations. Our results demonstrate over 200x performance gains over SOTA CPU-based column-oriented Datalog engines and a 2.5x speedup over GPU Datalog engines in various workloads, including KRR.