Work

Generating Permutations at Scale

Year: 2025

Type: conference-paper

Abstract: In this paper, we introduce a new algorithm for generating large-scale permutations on distributed systems. Permutations get used in many applications, including statistical analysis, machine learning, sampling, graph neural networks, matching, crypto-analysis, and bootstrapping. In data science the permutation is also commonly referred to as a shuffle operation as it reorganizes elements in an entirely random manner by applying the permutation. Our algorithm is computationally efficient, easy to understand, and scales to large systems. We measure the performance of our new permutation generation scheme on a cluster of NVIDIA DGX-A100s, using up to 256 NVIDIA A100 GPUs. We show that we can generate a permutation of 137 billion values in approximately 1.1 seconds, with a throughput of 124 billion elements per second.

Source: International Conference for High Performance Computing, Networking, Storage and Analysis (SC)

DOI: 10.1145/3731599.3767436

Authors: Oded Green, Joe Eaton, Alok Tripathy, Corey Nolet, Justin Luitjens

Institutions: Nvidia (United States), University of California, Berkeley

Language: English

Topic: Parallel Computing and Optimization Techniques

Subfield: Hardware and Architecture

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

OpenAlex ID: W4416004158

FWCI:

Cites: 25

Cited by:

Related to: 10