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Adjusting OS Scheduler Parameters to Improve Server Application Performance

Taehyun Han, Hyeonmyeong Lee, Heeseung Jo

http://doi.org/10.5626/JOK.2020.47.7.643

Modern Linux servers are used in a variety of ways, from large servers to small IOTs, and most machines run their services through the default scheduler provided by Linux. Although it is possible to optimize for a specific purpose, there is a problem in which the average user cannot optimize all modern Linux applications. In this paper, we propose SCHEDTUNE to automatically optimize the scheduler configuration to maximize Linux server performance. SCHEDTUNE allows users to improve performance without modification to the application or basic kernel source running on the server. This makes it easy for administrators to configure schedulers that operate specifically for their servers. Experimental results showed that when SCHEDTUNE is applied, the maximum performance is achieved up to 19 %, and in most cases performance improvement is achieved as well.

PARPA: A Parallel Framework Simultaneously Using Heterogeneous Architecture for High Performance Computing

Hyojae Cho, Taehyun Han, Hyeonmyeong Lee, Heeseung Jo

http://doi.org/10.5626/JOK.2019.46.9.876

With the substantial performance improvements achieved in GPU, they have come to be commonly used not only in computer graphics but also in high performance computing. Simply using a CPU and a GPU concurrently is not difficult. However, distributing works and adjusting the computing ratio among these heterogeneous processors are challenging issues. We propose a novel framework in this paper, named PARPA, which automatically distributes and processes tasks to a CPU and a GPU. PARPA can maximize computation performance by using a CPU and a GPU simultaneously. The load balancing between them can be performed dynamically based on their usage and features. The evaluation results indicate that PARPA shows 3.48 times better performance.


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