GPU APPARATUS AND METHOD FOR OPTIMAL SPLIT SIZE DECISION IN DEEP LEARNING USING MULTI-GPU AND METHOD FOR LEARNING DEEP LEARNING MODEL USING THE SAME

摘要

Disclosed are an apparatus and method for determining an optimal split size for training a deep learning model using multi-GPU and a method for learning a deep learning model using the same, and determining an optimal split size for training a deep learning model using multi-GPU according to an embodiment of the present application The method includes the steps of (a) calculating an initial split size based on the number of GPUs included in the multi-GPU and the memory size of the multi-GPU, (b) a preset number of times performed based on the initial split size calculating an initial execution time required for iterative learning, (c) between the initial split size and the initial execution time, and between (n-1)-th split size, n-th split size, and (n+1)-th split size Based on the relationship set in , the nth split size, the nth execution time required for iterative learning for a preset number of times performed based on the nth split size, the (n+1)th split size, and the (n+ 1) obtaining an (n+1)-th execution time required for iterative learning for a preset number of times performed based on the size of the th split, and (d) the n-th execution time and the (n+1)-th execution If the time difference between times is within a preset time difference, determining the (n+1)th split size as an optimal split size may include.