A Unified CPU-GPU Protocol for GNN Training: Improving Resource Utilization

المفاهيم الأساسية

Improving resource utilization in GNN training through a Unified CPU-GPU protocol.

الملخص

The content introduces a novel Unified CPU-GPU protocol for Graph Neural Network (GNN) training to enhance resource utilization. It addresses inefficiencies in existing GNN frameworks by balancing workload between CPUs and GPUs dynamically. The protocol instantiates multiple GNN training processes on both CPUs and GPUs, improving memory bandwidth utilization and reducing data transfer overhead. Key contributions include proposing the protocol, developing a Dynamic Load Balancer, and evaluating performance on various platforms with speedups up to 1.41×. The system design includes a GNN Process Manager, Dynamic Load Balancer, and GPU Feature Caching for optimization.

Abstract:

• Proposes a Unified CPU-GPU protocol for GNN training.
• Addresses inefficiencies in existing GNN frameworks.
• Improves resource utilization by balancing workload dynamically.
• Key contributions include the proposed protocol, Dynamic Load Balancer, and performance evaluation.

Introduction:

• Graph Neural Networks (GNNs) are used in various applications.
• Existing protocols cannot efficiently utilize platform resources.
• Proposed Unified CPU-GPU protocol aims to improve resource utilization.

System Design:

• Introduces the GNN Process Manager for workload assignment.
• Describes the Dynamic Load Balancer for workload balancing.
• Explains GPU Feature Caching to reduce memory access overhead.

Experiments:

• Evaluates performance on different platforms with speedups up to 1.41×.
• Demonstrates impact of optimizations including Dynamic Load Balancer and GPU Feature Caching.

الإحصائيات

Our protocol speeds up GNN training by up to 1.41× on platforms where the GPU moderately outperforms the CPU. On platforms where the GPU significantly outperforms the CPU, our protocol speeds up GNN training by up to 1.26×.

اقتباسات

"Our key contributions are: conducting detailed analysis of state-of-the-art GNN frameworks, proposing a novel Unified CPU-GPU protocol, developing a Dynamic Load Balancer, evaluating work using various platforms."
"Our system consists of several building blocks to execute the Unified CPU-GPU protocol without altering model accuracy or convergence rate."

الرؤى الأساسية المستخلصة من

A Unified CPU-GPU Protocol for GNN Training

by Yi-Chien Lin... في arxiv.org 03-27-2024

https://arxiv.org/pdf/2403.17092.pdf

استفسارات أعمق

How can this Unified CPU-GPU approach be extended to other types of accelerators?

The Unified CPU-GPU approach can be extended to other types of accelerators by leveraging the shared memory model and workload balancing techniques. For instance, for platforms with multiple CPUs and FPGAs, a similar protocol can be implemented where GNN training processes are distributed across both devices. The Dynamic Load Balancer can adjust the workload distribution based on the performance characteristics of each device. Additionally, GPU feature caching techniques can also be applied to reduce communication overhead when fetching data from FPGA memory.

What are the limitations of static load balancing compared to dynamic load balancing in this context?

Static load balancing has limitations in scenarios where the workload distribution among mini-batches is not uniform. In GNN training, different mini-batches may have varying computational complexities or data access patterns, leading to imbalanced workloads if statically assigned. This imbalance can result in bottlenecks on certain devices and degrade overall performance. On the other hand, dynamic load balancing continuously adjusts the workload distribution based on real-time feedback during runtime, ensuring that resources are optimally utilized and preventing bottlenecks.

How does GPU feature caching contribute to reducing communication overhead during data fetching?

GPU feature caching plays a crucial role in reducing communication overhead during data fetching by storing frequently accessed feature vectors in GPU global memory. When loading feature vectors for computations, if a vector is already cached in GPU memory, it eliminates the need for repeated transfers from CPU main memory over PCIe channels. By keeping commonly used features readily available on the GPU itself through caching mechanisms like Least Recently Used (LRU), unnecessary data transfers between CPU and GPU are minimized, leading to faster processing times and reduced communication latency during GNN training operations.