Massively Parallel Construction of Radix Tree Forests for the Efficient Sampling of Discrete Probability Distributions

TL;DR

This paper introduces a new GPU-efficient algorithm for sampling from discrete distributions using radix tree forests, achieving constant average time complexity and reducing operations in parallel settings.

Contribution

It presents a novel massively parallel construction algorithm for radix tree forests that improves sampling efficiency and addresses load balancing issues.

Findings

Achieves constant average time complexity for sampling.

Reduces the number of operations in parallel sampling.

Provides an efficient construction algorithm for radix tree forests.

Abstract

We compare different methods for sampling from discrete probability distributions and introduce a new algorithm which is especially efficient on massively parallel processors, such as GPUs. The scheme preserves the distribution properties of the input sequence, exposes constant time complexity on the average, and significantly lowers the average number of operations for certain distributions when sampling is performed in a parallel algorithm that requires synchronization afterwards. Avoiding load balancing issues of na\"ive approaches, a very efficient massively parallel construction algorithm for the required auxiliary data structure is complemented.