Fire-Flyer AI-HPC: A Cost-Effective Software-Hardware Co-Design for Deep Learning

• URL: http://arxiv.org/abs/2408.14158v2
• Date: Sat, 31 Aug 2024 13:33:22 GMT
• Title: Fire-Flyer AI-HPC: A Cost-Effective Software-Hardware Co-Design for Deep Learning
• Authors: Wei An, Xiao Bi, Guanting Chen, Shanhuang Chen, Chengqi Deng, Honghui Ding, Kai Dong, Qiushi Du, Wenjun Gao, Kang Guan, Jianzhong Guo, Yongqiang Guo, Zhe Fu, Ying He, Panpan Huang, Jiashi Li, Wenfeng Liang, Xiaodong Liu, Xin Liu, Yiyuan Liu, Yuxuan Liu, Shanghao Lu, Xuan Lu, Xiaotao Nie, Tian Pei, Junjie Qiu, Hui Qu, Zehui Ren, Zhangli Sha, Xuecheng Su, Xiaowen Sun, Yixuan Tan, Minghui Tang, Shiyu Wang, Yaohui Wang, Yongji Wang, Ziwei Xie, Yiliang Xiong, Yanhong Xu, Shengfeng Ye, Shuiping Yu, Yukun Zha, Liyue Zhang, Haowei Zhang, Mingchuan Zhang, Wentao Zhang, Yichao Zhang, Chenggang Zhao, Yao Zhao, Shangyan Zhou, Shunfeng Zhou, Yuheng Zou,
• Abstract summary: We introduce the Fire-Flyer AI- HPC architecture, a synergistic hardware-software co-design framework and its best practices. For Deep Learning (DL) training, we deployed the Fire-Flyer 2 with 10,000 PCIe A100 GPUs, achieved performance approximating the DGX-A100 while reducing costs by half and energy consumption by 40%. Through our software stack, including HaiScale, 3FS, and HAI-Platform, we achieved substantial scalability by overlapping computation and communication.
• Score: 49.997801914237094
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The rapid progress in Deep Learning (DL) and Large Language Models (LLMs) has exponentially increased demands of computational power and bandwidth. This, combined with the high costs of faster computing chips and interconnects, has significantly inflated High Performance Computing (HPC) construction costs. To address these challenges, we introduce the Fire-Flyer AI-HPC architecture, a synergistic hardware-software co-design framework and its best practices. For DL training, we deployed the Fire-Flyer 2 with 10,000 PCIe A100 GPUs, achieved performance approximating the DGX-A100 while reducing costs by half and energy consumption by 40%. We specifically engineered HFReduce to accelerate allreduce communication and implemented numerous measures to keep our Computation-Storage Integrated Network congestion-free. Through our software stack, including HaiScale, 3FS, and HAI-Platform, we achieved substantial scalability by overlapping computation and communication. Our system-oriented experience from DL training provides valuable insights to drive future advancements in AI-HPC.

Related papers

• Efficient and accurate neural field reconstruction using resistive memory [52.68088466453264]
  Traditional signal reconstruction methods on digital computers face both software and hardware challenges. We propose a systematic approach with software-hardware co-optimizations for signal reconstruction from sparse inputs. This work advances the AI-driven signal restoration technology and paves the way for future efficient and robust medical AI and 3D vision applications.
  arXiv  Detail & Related papers  (2024-04-15T09:33:09Z)
• Random resistive memory-based deep extreme point learning machine for unified visual processing [67.51600474104171]
  We propose a novel hardware-software co-design, random resistive memory-based deep extreme point learning machine (DEPLM) Our co-design system achieves huge energy efficiency improvements and training cost reduction when compared to conventional systems.
  arXiv  Detail & Related papers  (2023-12-14T09:46:16Z)
• FusionAI: Decentralized Training and Deploying LLMs with Massive Consumer-Level GPUs [57.12856172329322]
  We envision a decentralized system unlocking the potential vast untapped consumer-level GPU. This system faces critical challenges, including limited CPU and GPU memory, low network bandwidth, the variability of peer and device heterogeneity.
  arXiv  Detail & Related papers  (2023-09-03T13:27:56Z)
• Cross-Layer Design for AI Acceleration with Non-Coherent Optical Computing [5.188712126001397]
  We show how cross-layer design can overcome challenges in non-coherent optical computing platforms. Non-coherent optical computing represents a promising approach for light-speed acceleration of AI workloads.
  arXiv  Detail & Related papers  (2023-03-22T21:03:40Z)
• Heterogeneous Data-Centric Architectures for Modern Data-Intensive Applications: Case Studies in Machine Learning and Databases [9.927754948343326]
  processing-in-memory (PIM) is a promising execution paradigm that alleviates the data movement bottleneck in modern applications. In this paper, we show how to take advantage of the PIM paradigm for two modern data-intensive applications.
  arXiv  Detail & Related papers  (2022-05-29T13:43:17Z)
• FPGA-based AI Smart NICs for Scalable Distributed AI Training Systems [62.20308752994373]
  We propose a new smart network interface card (NIC) for distributed AI training systems using field-programmable gate arrays (FPGAs) Our proposed FPGA-based AI smart NIC enhances overall training performance by 1.6x at 6 nodes, with an estimated 2.5x performance improvement at 32 nodes, compared to the baseline system using conventional NICs.
  arXiv  Detail & Related papers  (2022-04-22T21:57:00Z)
• Scalable Deep-Learning-Accelerated Topology Optimization for Additively Manufactured Materials [4.221095652322005]
  Topology optimization (TO) is a popular and powerful computational approach for designing novel structures, materials, and devices. To address these issues, we propose a general scalable deep-learning (DL) based TO framework, referred to as SDL-TO. Our framework accelerates TO by learning the iterative history data and simultaneously training on the mapping between the given design and its gradient.
  arXiv  Detail & Related papers  (2020-11-28T17:38:31Z)
• Integrating Deep Learning in Domain Sciences at Exascale [2.241545093375334]
  We evaluate existing packages for their ability to run deep learning models and applications on large-scale HPC systems efficiently. We propose new asynchronous parallelization and optimization techniques for current large-scale heterogeneous systems. We present illustrations and potential solutions for enhancing traditional compute- and data-intensive applications with AI.
  arXiv  Detail & Related papers  (2020-11-23T03:09:58Z)
• Optimizing Deep Learning Recommender Systems' Training On CPU Cluster Architectures [56.69373580921888]
  We focus on Recommender Systems which account for most of the AI cycles in cloud computing centers. By enabling it to run on latest CPU hardware and software tailored for HPC, we are able to achieve more than two-orders of magnitude improvement in performance.
  arXiv  Detail & Related papers  (2020-05-10T14:40:16Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.