Linux kernel compaction through cold code swapping

There is a growing trend to use general-purpose operating systems like Linux in embedded systems. Previous research focused on using compaction and specialization techniques to adapt a general-purpose OS to the memory-constrained environment, presented by most, embedded systems. However, there is still room for improvement: it has been shown that even after application of the aforementioned techniques more than 50% of the kernel code remains unexecuted under normal system operation. We introduce a new technique that reduces the Linux kernel code memory footprint, through on-demand code loading of infrequently executed code, for systems that support virtual memory. In this paper, we describe our general approach, and we study code placement algorithms to minimize the performance impact of the code loading. A code, size reduction of 68% is achieved, with a 2.2% execution speedup of the system-mode execution time, for a case study based on the MediaBench II benchmark suite

The Cost of Address Translation

Modern computers are not random access machines (RAMs). They have a memory hierarchy, multiple cores, and virtual memory. In this paper, we address the computational cost of address translation in virtual memory. Starting point for our work is the observation that the analysis of some simple algorithms (random scan of an array, binary search, heapsort) in either the RAM model or the EM model (external memory model) does not correctly predict growth rates of actual running times. We propose the VAT model (virtual address translation) to account for the cost of address translations and analyze the algorithms mentioned above and others in the model. The predictions agree with the measurements. We also analyze the VAT-cost of cache-oblivious algorithms.Comment: A extended abstract of this paper was published in the proceedings of ALENEX13, New Orleans, US