Performance Evaluation of Hashing Algorithms on Commodity Hardware

TL;DR

This paper evaluates the performance of Blake3, SHA-256, and SHA-512 hashing algorithms on various hardware platforms, highlighting Blake3's generally superior throughput and latency, with implications for application-specific algorithm selection.

Contribution

It provides a comprehensive performance comparison of three popular hashing algorithms across different hardware setups, informing better algorithm choice based on performance and security needs.

Findings

Blake3 outperforms SHA-256 and SHA-512 in throughput and latency.

Performance varies depending on hardware and input size.

Results guide optimal hashing algorithm selection for applications.

Abstract

Hashing functions, which are created to provide brief and erratic digests for the message entered, are the primary cryptographic primitives used in blockchain networks. Hashing is employed in blockchain networks to create linked block lists, which offer safe and secure distributed repository storage for critical information. Due to the unique nature of the hash search problem in blockchain networks, the most parallelization of calculations is possible. This technical report presents a performance evaluation of three popular hashing algorithms Blake3, SHA-256, and SHA-512. These hashing algorithms are widely used in various applications, such as digital signatures, message authentication, and password storage. It then discusses the performance metrics used to evaluate the algorithms, such as hash rate/throughput and memory usage. The evaluation is conducted on a range of hardware platforms, including desktop and VMs. The evaluation includes synthetic benchmarks. The results of the evaluation show that Blake3 generally outperforms both SHA-256 and SHA-512 in terms of throughput and latency. However, the performance advantage of Blake3 varies depending on the specific hardware platform and the size of the input data. The report concludes with recommendations for selecting the most suitable hashing algorithm for a given application, based on its performance requirements and security needs. The evaluation results can also inform future research and development efforts to improve the performance and security of hashing algorithms.