When In-Memory Computing is Slower than Heavy Disk Usage

TL;DR

This paper demonstrates that in-memory computing can sometimes be slower than disk-based approaches, emphasizing the importance of system optimizations and developer practices for performance gains.

Contribution

It challenges the assumption that in-memory operations are always faster, showing real-world code examples where they cause slowdowns and proposing improvements.

Findings

In-memory operations can be slower than disk-based ones under certain conditions.

Small code modifications can significantly improve performance.

System-level and developer practices are crucial for optimizing in-memory computing.

Abstract

Disk access latency and transfer times are often considered to have a major and detrimental impact on the running time of software. Developers are often advised to favour in-memory operations and minimise disk access. Furthermore, diskless computer architectures are being studied and designed to remove this bottleneck all together, to improve application performance in areas such as High Performance Computing, Big Data, and Business Intelligence. In this paper we use code inspired by real, production software, to show that in-memory operations are not always a guarantee for high performance, and may actually cause a considerable slow-down. We also show how small code changes can have dramatic effects on running times. We argue that a combination of system-level improvements and better developer awareness and coding practices are necessary to ensure in-memory computing can achieve its full potential.