## 2 Related Work  
There is a study to improve and evaluate the communication performance between nodes in distributed deep learning. [PERSON] et al. [16] propose a new virtual shared memory framework called Soft Memory Box (SMB) that can share the memory of remote nodes between distributed processes in order to reduce the communication overhead of large-scale distributed deep learning. SMB proposed a new communication technology other than MPI by constructing separate servers and virtual devices. Our research and SMB have the same goal of improving data communication performance when deep learning in a distributed environment. However, there is a difference in that we proposed performance improvement based on the utilization of resources of high-performance computing and commonly used Python and MPI communication technologies.  
There are studies to analyze and evaluate the performance of High-Bandwidth Memory (HBM). [PERSON] et al. [17] compare the performance of HBM and DRAM and analyze the impact of HBM on High Performance Computing (HPC) applications. The results of this study show that applications using HBM achieve up to three times the performance of applications using only DRAM. We not only compare HBM and DRAM but also compare and analyze the performance of two memories in a Docker Container environment.  
There is a study to evaluate communication performance in many-core CPU and high bandwidth memory environment. [PERSON] et al. [18] compare the MPI communication performance according to the MVAPICH2 shared memory technology and the location of the many-core CPU processor. In the experiment, intra-node communication and inter-node communication are compared. In addition, this study evaluates experiments according to the location of the memory and processor. Unlike this study, we propose to improve the MPI performance in the Python environment, and we experiment and analyze according to the Cluster mode and Memory mode.  
There is a study to evaluate the performance of the virtualization solution Container and Hypervisor. [PERSON] et al. [19] compare the performance overhead of virtualization in Container-based and Hypervisor-based. The results of this study show that the average performance of Containers is superior to that of virtual machines. Therefore, in our study, we evaluate the performance in the Docker Container environment.  
There are several studies evaluating communication performance using Containers in HPC environment. [PERSON] et al. [20] evaluate the performance of MPI in Singularity-based Containers. Experiments show that Singularity Container technology does not degrade MPI communication performance when using different Memory Modes (Flat, Cache) on Intel Xeon and Intel Xeon Knights Landing (KNL) platforms. However, our study evaluates and analyzes MPI performance in Docker Container. In addition, [PERSON] et al. [21] propose communication through shared memory and Cross Memory Attach (CMA) channels when executing MPI tasks in a multi-container environment. According to this study, communication performance is improved in a large-scale Docker Container environment. Our study compares and analyzes in multiple Docker Containers using Python environment and high-performance memory.  
[PERSON] et al. propose In-Network Computing to Exchange and Process Training Information Of Neural Networks (INCEPTIONN) to optimize communication in distributed deep learning [4]. They propose a compression algorithm to optimize communication and implement the algorithm through hardware. In addition, through experiments, they show that INCEPTIONN can reduce communication time by up to 80.7% and improve speed by up to 3.1 times compared to conventional training systems. Unlike INCEPTIONN, in our study, we propose a software-based method to optimize communication in distributed deep learning by utilizing KNL's HBM and MPI.