Dual-pronged deep learning preprocessing on heterogeneous platforms with CPU, Accelerator and CSD

TL;DR

This paper introduces DDLP, a novel deep learning preprocessing framework utilizing Computable Storage Devices to alleviate bottlenecks, improve speed, and reduce energy consumption across heterogeneous CPU, accelerator, and CSD platforms.

Contribution

DDLP is the first system to efficiently leverage CSDs for deep learning preprocessing, enabling parallel data handling and transfer, and balancing consistency and efficiency with adaptive strategies.

Findings

Improves ImageNet training speed by up to 23.5%.

Reduces energy consumption by 19.7%.

Decreases CPU and DRAM usage by 37.6%.

Abstract

For image-related deep learning tasks, the first step often involves reading data from external storage and performing preprocessing on the CPU. As accelerator speed increases and the number of single compute node accelerators increases, the computing and data transfer capabilities gap between accelerators and CPUs gradually increases. Data reading and preprocessing become progressively the bottleneck of these tasks. Our work, DDLP, addresses the data computing and transfer bottleneck of deep learning preprocessing using Computable Storage Devices (CSDs). DDLP allows the CPU and CSD to efficiently parallelize preprocessing from both ends of the datasets, respectively. To this end, we propose two adaptive dynamic selection strategies to make DDLP control the accelerator to automatically read data from different sources. The two strategies trade-off between consistency and efficiency. DDLP achieves sufficient computational overlap between CSD data preprocessing and CPU preprocessing, accelerator computation, and accelerator data reading. In addition, DDLP leverages direct storage technology to enable efficient SSD-to-accelerator data transfer. In addition, DDLP reduces the use of expensive CPU and DRAM resources with more energy-efficient CSDs, alleviating preprocessing bottlenecks while significantly reducing power consumption. Extensive experimental results show that DDLP can improve learning speed by up to 23.5% on ImageNet Dataset while reducing energy consumption by 19.7% and CPU and DRAM usage by 37.6%. DDLP also improves the learning speed by up to 27.6% on the Cifar-10 dataset.