An Effective Method of Reducing the Working Memory for General-purpose OS-based Memory Diagnostic Tool

Abstract

Embedded platforms are becoming more sophisticated as smart or IoT devices become popular. As a result, the DRAM mounted on embedded boards or chips has been increased in capacity and density to the same extent as ancient servers and PCs. The stand-alone memory diagnostic tool could be useful for detecting defective memory equipped on devices at shipping or maintenance. We aim at realizing a memory diagnostic tool using a general-purpose OS for supporting multi-platforms and for integrated diagnostic not only for memory but also for other devices. The memory diagnostic tools may have an issue that it would be hard to apply enough numbers of the test pattern into working memory on which the tool itself runs. Especially, using a general-purpose OS could impact the amount of working memory substantially. This paper proposes a tinification approach to effectively reduce the working memory to which especially OS kernel consumes by analyzing utilization and memory consumption of each kernel function. Our experimental result on an ARM platform shows 23.6% reduction in the working memory, compared with the default configured kernel.