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Performance Analysis of Concurrent Multitasking for Efficient Resource Utilization of GPUs
Sejin Kim, Qichen Chen, HeonYoung Yeom, Yoonhee Kim
http://doi.org/10.5626/JOK.2021.48.6.604
As Graphics Processing Units (GPUs) are widely utilized to accelerate compute-intensive applications, their application has expanded especially in data centers and clouds. However, the existing resource sharing methods within GPU are limited and cannot efficiently handle several requests of concurrent cloud users’ executions on GPU while effectively utilizing the available system resources. In addition, it is challenging to effectively partition resources within GPU without understanding and assimilating application execution patterns. This paper proposes an execution pattern-based application classification method and analyzes run-time characteristics: why the performance of an application is saturated at a point regardless of the allocated resources. In addition, we analyze the multitasking performance of the co-allocated applications using smCompactor, a thread block-based scheduling framework. We identify near-best co-allocated application sets, which effectively utilize the available system resources. Based on our results, there was a performance improvement of approximately 28% compared to NVIDIA MPS.
An Execution Planning Scheme for Computational Applications using Profiling of a CPU-GPU Container Cluster
Jisun Oh, Sejin Kim, Yoonhee Kim
http://doi.org/10.5626/JOK.2019.46.10.975
As heterogeneous clusters and cloud environments have gained popularity, selection of appropriate resources in an integrated environment is considered essential for enhancement in application performance with reasonable resource utilization. Application characteristics may require specific resource in a certain order during its execution and ask smart job deployment among diverse nodes. Especially, it is necessary to have an execution plan in advance for better performance of CPU and GPU container clusters. In this paper, we propose an execution planning scheme based on runtime profiling history to place jobs on CPU-GPU nodes. Computational applications usually show good performance in GPU. However, the lack of GPU sharing methods leads to failure of co-locating jobs on a GPU node. Based on the profile information, the scheme provides the combination of applications to run at the same time in GPU container clusters and estimate the performance of workload before executing application among CPU-GPU container clusters. We have also demonstrated adjustment of the order of application execution using profile history in order to reduce the execution time of total workload by monitoring GPU memory usage.
Design of Video Advertisement Analysis via Analysis of Internet Term Sensitivity
Sejin Kim, Jieun Kim, Wonyoung Seong, Yoonhee Kim
http://doi.org/10.5626/JOK.2019.46.9.919
Analysis of the increasing influence of video advertisements via Social Networking Service (SNS) is important in identifying their effects. However, the traditional methods of survey-based analysis are not suitable for measurement of the effectiveness of SNS advertisements that are distributed rapidly via smartphone use and the current system does not consider the sensitivity of users expressed in various forms, such as slang, and emoticons. This study proposes an automated system for the analysis of the effects of video ads via video comments, reflecting the characteristics of short Korean sentences.
This system uses machine learning for the interpretation of Internet terms and compilation of a sentiment dictionary specializing in SNS short sentences. Emoticon, which is used to emphasize the sensitivity of users in comments, is used for sentiment analysis when applied to Korean syntax rules, and the system is designed and implemented for more sophisticated emotional analysis by calculating the emotional values of nouns that are subject to sentiment.
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