Work

Eliminating Bank Conflicts in GPU Mergesort

Year: 2025

Type: conference-paper

Abstract: Graphics Processing Units (GPUs) have emerged as popular architectures for high-performance computing due to their parallelism and low latency context switching capabilities. However, optimizing GPU implementations can be challenging due to the complexity of the architecture, such as the diverse characteristics of memory units. While most optimization efforts focus on parallelism and global memory access, for some algorithms memory conflicts in shared memory, known as bank conflicts, can significantly impact performance. This affects the accuracy of theoretical runtime analysis of GPU algorithms.

Source: ACM Symposium on Parallelism in Algorithms and Architectures (SPAA)

DOI: 10.1145/3694906.3743337

Authors: Kyle Berney, Nodari Sitchinava

Institution: University of Hawaiʻi at Mānoa

Language: English

Topic: Parallel Computing and Optimization Techniques

Subfield: Hardware and Architecture

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

Funders: National Science Foundation, Universitas Brawijaya

Awards: AF: Small: Toward A Unified Model of Parallelism And Locality, AF: SMALL: Parallel Cache-efficient Data Structures, G6554605010