Near-Data Processing for differentiable machine learning models

Hyeokjun Choe, Seil Lee, Hyunha Nam, Seongsik Park, Seijoon Kim, Eui Young Chung, Sungroh Yoon

Research output: Contribution to conferencePaper

Abstract

Near-data processing (NDP) refers to augmenting memory or storage with processing power. Despite its potential for acceleration computing and reducing power requirements, only limited progress has been made in popularizing NDP for various reasons. Recently, two major changes have occurred that have ignited renewed interest and caused a resurgence of NDP. The first is the success of machine learning (ML), which often demands a great deal of computation for training, requiring frequent transfers of big data. The second is the popularity of NAND flash-based solid-state drives (SSDs) containing multicore processors that can accommodate extra computation for data processing. In this paper, we evaluate the potential of NDP for ML using a new SSD platform that allows us to simulate instorage processing (ISP) of ML workloads. Our platform (named ISP-ML) is a full-fledged simulator of a realistic multi-channel SSD that can execute various ML algorithms using data stored in the SSD. To conduct a thorough performance analysis and an in-depth comparison with alternative techniques, we focus on a specific algorithm: stochastic gradient descent (SGD), which is the de facto standard for training differentiable models such as logistic regression and neural networks. We implement and compare three SGD variants (synchronous, Downpour, and elastic averaging) using ISP-ML, exploiting the multiple NAND channels to parallelize SGD. In addition, we compare the performance of ISP and that of conventional in-host processing, revealing the advantages of ISP. Based on the advantages and limitations identified through our experiments, we further discuss directions for future research on ISP for accelerating ML.

Original languageEnglish
Publication statusPublished - 2017 Jan 1
Event33rd International Conference on Massive Storage Systems and Technology, MSST 2017 - Santa Clara, United States
Duration: 2017 May 152017 May 19

Conference

Conference33rd International Conference on Massive Storage Systems and Technology, MSST 2017
CountryUnited States
CitySanta Clara
Period17/5/1517/5/19

All Science Journal Classification (ASJC) codes

  • Hardware and Architecture
  • Electrical and Electronic Engineering

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  • Cite this

    Choe, H., Lee, S., Nam, H., Park, S., Kim, S., Chung, E. Y., & Yoon, S. (2017). Near-Data Processing for differentiable machine learning models. Paper presented at 33rd International Conference on Massive Storage Systems and Technology, MSST 2017, Santa Clara, United States.