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Managing DISCARD Commands in F2FS File System for Improving Lifespan and Performance of SSD Devices
Jinwoong Kim, Donghyun Kang, Young Ik Eom
http://doi.org/10.5626/JOK.2024.51.8.669
The DISCARD command is an interface that helps improve the lifespan and performance of SSDs by informing the SSD devices about invalid file system blocks. However, in the F2FS file system, the DISCARD command is only sent to the SSD during idle time, which limits the potential for improving lifespan and performance. In this paper, we propose an EPD scheme to efficiently transfer DISCARD commands during short idle times, as well as a seg-ment allocation scheme called PSA, which replaces DISCARD commands with overwrite commands. To evaluate the effectiveness of these proposed schemes, we conducted several experiments using various workloads to verify the lifespan and performance of real SSD devices. The results showed that the proposed schemes can improve the write amplification factor (WAF) by up to 40% and throughput by up to 160%, when compared to the traditional F2FS file system.
A Parallel Processing Scheme on TensorFlow for Improving Training and Validation Performance
http://doi.org/10.5626/JOK.2022.49.6.407
Most deep learning systems spend a lot of time on model training and validation. However, they sometimes tend to waste GPU and CPU resources because the pre-processing and batch processes based on a single thread result in a wait time. In this paper, we propose a new scheme that efficiently handles training and validation processes based on multi-threads. The proposed scheme can overlap the training and validation processes as much as possible by using a model copy operation that extends the processes with multi-threads. As a result, it improves the overall utilization of CPU and GPU. For evaluation, we implemented a convolutional neural network (CNN) using the TensorFlow framework. As a result, we clearly confirm that the proposed scheme saves the total training and validation time by up to 22.4% compared with the traditional schemes.
L2LRU: Learning-based Page Movement Policy for LRU Page Replacement Policy
http://doi.org/10.5626/JOK.2021.48.9.981
The LRU (least-recently used) page replacement policy has been designed to enhance the cache hit ratio by moving the page that is repeatedly accessed on the cache, to the head of the list. However, the LRU policy sometimes incurs a situation of system stall (or wait) because it requires lock-unlock commands to move each page. In this paper, we propose a new page replacement policy, called L2LRU(Learning-based Lock-free LRU), that determines whether to move or not a page by learning the reuse distance of the page with deep-learning techniques. Unlike LRU, L2LRU moves the page to the position with a high possibility of access in the near future. For evaluation, we implemented L2LRU based on trace-driven simulation and used Microsoft Research Cambridge Trace as the input of the simulation. The results clearly confirmed that L2LRU reduced the number of lock-unlock commands by up to 91% compared to the traditional LRU policy.
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