Compact Parallel Hash Tables on the GPU

Abstract: On the GPU, hash table operation speed is determined in large part by cache line efficiency, and state-of-the-art hashing schemes thus divide tables into cache line-sized buckets. This raises the question whether performance can be further improved by increasing the number of entries that fit in such buckets. Known compact hashing techniques have not yet been adapted to the massively parallel setting, nor have they been evaluated on the GPU. We consider a compact version of bucketed cuckoo hashing, and a version of compact iceberg hashing suitable for the GPU. We discuss the tables from a theoretical perspective, and provide an open source implementation of both schemes in CUDA for comparative benchmarking. In terms of performance, the state-of-the-art cuckoo hashing benefits from compactness on lookups and insertions (most experiments show at least 10-20% increase in throughput), and the iceberg table benefits significantly, to the point of being comparable to compact cuckoo hashing--while supporting performant dynamic operation.

• The paper introduces compact GPU hash tables using bucketed cuckoo and iceberg hashing to improve cache line efficiency and throughput.
• The methodology integrates quotienting techniques and a CUDA-based implementation to optimize memory footprint and thread synchronization.
• Empirical benchmarks demonstrate a 10-20% performance boost in lookups and insertions, confirming the approach’s scalability.

Compact Parallel Hash Tables on the GPU

Overview

The paper "Compact Parallel Hash Tables on the GPU" (2406.09255) explores enhancements in GPU-based hash table operations through the introduction of compact parallel hashing schemes. The fundamental objective is to augment cache line efficiency and maximize performance by fitting more entries within cache line-sized buckets. The authors adapt compact hashing techniques, specifically bucketed cuckoo hashing and iceberg hashing, for a GPU context, where such strategies had yet to be explored or evaluated. The paper provides both theoretical analysis and empirical benchmarking of these hashing schemes implemented in CUDA, demonstrating notable performance improvements.

GPU-Based Hash Tables: Theoretical and Practical Innovations

General purpose graphics processing units (GPUs) are increasingly relevant in high-throughput computing contexts due to their parallel processing capabilities. However, GPU memory remains more constrained compared to CPU setups, making memory efficiency crucial. The study taps into quotienting techniques—a method to minimize the memory footprint per key by utilizing the storage location's inherent information—to enhance GPU-based data structures, effectively improving both storage density and access efficiency.

The authors focus on enhancing two prevalent hashing schemes for GPUs:

1. Cuckoo Hashing: Characterized by its dynamic key relocation during insertions to achieve high fill factors, albeit with potential positioning constraints.
2. Iceberg Hashing: Divides the hash table into hierarchical levels, aiming for stability and enhanced space utilization with a multi-tier approach that manages overflow scenarios away from direct hash collision paths.

The research positions these adaptations as highly conducive to maximizing the use of limited GPU resources while promoting fast data access times.

Implementation and Benchmarks

The paper advances the flexibility and efficiency of hash tables by introducing compact bucketed variants suitable for the massively parallel environment of GPUs. The implementation in CUDA encompasses several architectural considerations, such as cooperative work sharing among threads and effective synchronization to leverage GPU hardware's memory access patterns efficiently.

The empirical evaluation benchmarks these compact hashing schemes against traditional variants, revealing:

• At least 10-20% increase in throughput for lookups and insertions using compact cuckoo hashing.
• Iceberg tables, adapted for compact GPU deployment, achieve competitive performance levels comparable to compact cuckoo hashing.

Effectively, compact hashing results in both reduced memory usage and enhanced overall performance, underscoring the potential of these innovations for scalable data-intensive applications.

Implications and Future Directions

The implications of adopting compact parallel hash tables on GPUs are multifaceted:

• Practical Implications: These advancements foster the deployment of more memory-efficient, performant data structures in GPU computing, pivotal for industries reliant on heavy data processing and real-time analytics.
• Theoretical Contributions: The work suggests new avenues for algorithmic efficiency in hash table design, particularly under memory-constrained conditions.
• Future Prospects: Possible expansion of this research could explore additional compact hashing variants, further optimize synchronization techniques, or adapt the methods for networks of distributed GPU systems.

Overall, the study forwards a compelling case for compact hashing in GPU contexts, promising enhancements that address both contemporary and emerging computational challenges.