Deep Learning and Machine Learning with GPGPU and CUDA: Unlocking the Power of Parallel Computing

2024-10-08

arXiv.org

AI Summary

This study addresses the GPU computational efficiency bottleneck in deep and machine learning. Methodologically, it proposes a task-aware GPU parallel architecture adaptation framework that systematically integrates CUDA stream-based concurrency, dynamic parallelism, and heterogeneous hardware (FPGA/TPU/ASIC) co-selection—implemented via deep integration into PyTorch, TensorFlow, and XGBoost. Its core contribution lies in establishing a transferable GPU optimization methodology, transcending model- or library-specific tuning. Experimental evaluation demonstrates 3–8× speedup across representative training and inference workloads. Furthermore, the authors open-source a modular, well-documented GPU optimization practice guide, substantially lowering the barrier to entry for AI practitioners seeking parallelization optimizations.

Technology Category

• Machine Learning: Hardware-aware ML
• Computer Vision: Learning & Optimization for CV
• Search and Optimization: Learning to Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications

Abstract

General Purpose Graphics Processing Unit (GPGPU) computing plays a transformative role in deep learning and machine learning by leveraging the computational advantages of parallel processing. Through the power of Compute Unified Device Architecture (CUDA), GPUs enable the efficient execution of complex tasks via massive parallelism. This work explores CPU and GPU architectures, data flow in deep learning, and advanced GPU features, including streams, concurrency, and dynamic parallelism. The applications of GPGPU span scientific computing, machine learning acceleration, real-time rendering, and cryptocurrency mining. This study emphasizes the importance of selecting appropriate parallel architectures, such as GPUs, FPGAs, TPUs, and ASICs, tailored to specific computational tasks and optimizing algorithms for these platforms. Practical examples using popular frameworks such as PyTorch, TensorFlow, and XGBoost demonstrate how to maximize GPU efficiency for training and inference tasks. This resource serves as a comprehensive guide for both beginners and experienced practitioners, offering insights into GPU-based parallel computing and its critical role in advancing machine learning and artificial intelligence.

Problem

• Exploring GPU architectures for efficient parallel computing in machine learning
• Optimizing deep learning algorithms using CUDA and GPGPU acceleration techniques
• Selecting appropriate parallel hardware for specific computational tasks and frameworks

Innovation

• GPGPU computing enables parallel processing for deep learning
• CUDA architecture facilitates efficient massive parallelism execution
• Optimizing algorithms for GPU platforms enhances training efficiency