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A Data Imbalance Minimization Strategy for Scalable Deep Learning Training
Sanha Maeng, Euhyun Moon, Sungyong Park
http://doi.org/10.5626/JOK.2023.50.10.836
As deep neural network training is compute-intensive and takes a very long time, distributed training using clusters with multiple graphics processing units (GPUs) has been widely adopted. The distributed training of deep neural networks is severely slowed due to straggler, i.e., the slowest worker. Hence, previous studies have proposed solutions to the straggler problem. The existing approaches assume that all data samples, such as images, have a constant size, and they do not recognize data imbalance issues, caused by data samples with different sizes, such as videos and audios, while solving the straggler problem. In this paper, we propose a data imbalance minimization (DIM) strategy that considers data imbalance problems to solve the straggler problem caused by imbalanced data samples. Our evaluation on eight NVIDIA Tesla T4 GPUs shows that DIM outperforms the state-of-the-art systems by up to 1.77x speedup with comparable scalability.
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.
Host-Level I/O Scheduler for Achieving Performance Isolation with Open-Channel SSDs
Sooyun Lee, Kyuhwa Han, Dongkun Shin
http://doi.org/10.5626/JOK.2020.47.2.119
As Solid State Drives (SSDs) provide higher I/O performance and lower energy consumption compared to Hard Disk Drives (HDDs), SSDs are currently widening its adoption in areas such as datacenters and cloud computing where multiple users share resources. Based on this trend, there is currently greater research effort being made on ensuring Quality of Service (QoS) in environments where resources are shared. The previously proposed Workload-Aware Budget Compensation (WA-BC) scheduler aims to ensure QoS among multiple Virtual Machines (VMs) sharing an NVMe SSD. However, the WA-BC scheduler has a weakness in that it misuses multi-stream SSDs for identifying workload characteristics. In this paper, we propose a new host-level I/O scheduler, which complements this vulnerability of the WA-BC scheduler. It aims to eliminate performance interference between different users that share an Open-Channel SSD. The proposed scheduler identifies workload characteristics without having to allocate separate SSD streams by observing the sequentiality of I/O requests. Although the proposed scheduler exists within the host, it can reflect the status of device internals by exploiting the characteristics of Open-Channel SSDs. We show that by identifying those that attribute more to garbage collection, a source of I/O interference within SSDs, using workload characteristics and penalizing such users helps to achieve performance isolation amongst different users sharing storage resources.
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