[Paper Review] Deep Learning and Machine Learning with GPGPU and CUDA: Unlocking the Power of Parallel Computing

Ming Li, Ziqian Bi+15 more|arXiv (Cornell University)|Oct 8, 2024

Neural Networks and Applications4 citations

TL;DR

This paper provides a comprehensive guide to leveraging GPGPU and CUDA for deep learning and machine learning workloads, demonstrating how parallel computing architectures accelerate model training and inference. It covers GPU memory hierarchy, CUDA programming models, optimization techniques like memory coalescing and streams, and practical implementations using PyTorch and high-level libraries such as cuDNN and TensorRT, enabling researchers to efficiently deploy and tune deep learning models on GPUs.

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.

Motivation & Objective

• To provide a foundational understanding of GPGPU and CUDA for researchers new to GPU-accelerated machine learning.
• To explain the role of GPU memory hierarchy (VRAM, cache, registers) in optimizing deep learning performance.
• To demonstrate practical implementation of parallel algorithms (e.g., vector addition, matrix multiplication) using CUDA kernels.
• To compare and contrast GPU programming models (CUDA, OpenCL, Vulkan, Metal, OpenGL) for GPGPU workloads.
• To show how high-level deep learning frameworks like PyTorch abstract low-level CUDA complexity for rapid model development and training.

Proposed method

• Utilizes CUDA’s hierarchical thread model (grid, block, thread, warp) to map data-parallel operations to GPU execution units.
• Applies memory coalescing and shared memory optimization to reduce global memory access latency in GPU kernels.
• Employs CUDA streams and dynamic parallelism to overlap computation with data transfers and enable recursive kernel launches.
• Demonstrates performance tuning via profilers to identify bottlenecks in memory access and execution divergence.
• Leverages high-level libraries such as cuDNN, cuBLAS, and TensorRT to accelerate linear algebra and inference workloads.
• Illustrates end-to-end deep learning workflows using PyTorch, including model definition, data loading, loss computation, and backpropagation on GPU.

Experimental results

Research questions

• RQ1 How can GPU architecture and CUDA programming models be effectively utilized to accelerate deep learning training and inference?
• RQ2 What are the key performance bottlenecks in GPU memory access, and how can they be mitigated through optimization strategies like memory coalescing and shared memory usage?
• RQ3 How do different GPGPU programming models (CUDA, OpenCL, Vulkan, Metal, OpenGL) compare in terms of performance, portability, and ease of use for machine learning workloads?
• RQ4 To what extent do high-level deep learning frameworks like PyTorch abstract away low-level CUDA complexity without sacrificing performance?
• RQ5 What role do specialized libraries (e.g., cuDNN, TensorRT) play in accelerating deep learning workloads on GPU hardware?

Key findings

• Proper use of CUDA streams enables overlapping of data transfers and kernel launches, significantly improving training throughput.
• Memory coalescing and shared memory optimization can reduce kernel execution time by up to several times in compute-intensive operations like matrix multiplication.
• PyTorch enables rapid prototyping of deep learning models on GPU with minimal low-level code, abstracting away CUDA kernel management.
• High-level libraries like cuDNN and TensorRT provide highly optimized implementations of common deep learning operations, achieving near-peak GPU utilization.
• The use of advanced optimization techniques such as dynamic parallelism and warp-level primitives can improve kernel efficiency and scalability on modern GPU architectures.
• The memory hierarchy—especially the distinction between CPU RAM and GPU VRAM—plays a critical role in determining overall model training performance and data movement overhead.