Efficient Scheduling and Parallelization for Large Model Training in Heterogeneous Clusters

Konsep Inti

Joint consideration of scheduling and adaptive parallelism can significantly improve training efficiency in heterogeneous GPU clusters.

Abstrak

The content discusses the challenges of integrating adaptive parallelism into cluster scheduling to optimize large model training. It introduces Crius, a system that efficiently schedules multiple large models with adaptive parallelism in heterogeneous clusters. Crius proposes a novel scheduling granularity called Cell, which allows for accurate performance estimation and efficient job scheduling. Experimental results show significant improvements in job completion time and cluster throughput.
Directory:

Introduction

Challenges of integrating adaptive parallelism into cluster scheduling.

Crius System Overview

Introduction of Crius and its novel scheduling granularity, Cell.

Data Extraction Techniques

The exponentially enlarged scheduling space hinders performance data acquisition.

Experimental Results

Evaluation of Crius on physical testbed with 64 GPUs.

Performance Analysis

Comparison of Crius with baselines on a real testbed.

Statistik

Experimental results show that Crius reduces job completion time by up to 48.9%.
Crius achieves up to 1.49× cluster throughput improvement on the real testbed.

Kutipan

"Integrating adaptive parallelism into a cluster scheduler expands the cluster scheduling space."
"Crius reduces job completion time by up to 48.9%."

Wawasan Utama Disaring Dari

A Codesign of Scheduling and Parallelization for Large Model Training in Heterogeneous Clusters

by Chunyu Xue,W... pada arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.16125.pdf

Pertanyaan yang Lebih Dalam

How can the integration of adaptive parallelism be optimized further

To optimize the integration of adaptive parallelism further, several strategies can be implemented:

Dynamic Resource Allocation: Implementing a dynamic resource allocation strategy that adjusts resources based on real-time job requirements and cluster conditions can enhance the efficiency of adaptive parallelism. This approach ensures that jobs are allocated optimal resources at all times, maximizing throughput and minimizing job completion time.

Advanced Estimation Techniques: Utilizing more advanced estimation techniques, such as machine learning algorithms or predictive modeling, can improve the accuracy of performance predictions for different parallelism plans. By leveraging historical data and patterns, these techniques can provide more precise estimations, leading to better scheduling decisions.

Fine-Grained Parallelism Exploration: Conducting a more fine-grained exploration of the parallelism space within Cells can help identify even more optimized parallelism plans. By narrowing down the search space and considering additional factors like inter-stage communication overhead, Crius can find near-optimal solutions efficiently.

Adaptive Learning Algorithms: Incorporating adaptive learning algorithms that continuously adapt to changing workload characteristics and cluster configurations can enhance the adaptability of adaptive parallelism. These algorithms can learn from past scheduling decisions and adjust future strategies accordingly for improved performance.

What are the potential drawbacks or limitations of using Cell as a scheduling granularity

While Cell serves as an effective scheduling granularity in systems like Crius, there are potential drawbacks or limitations associated with its use:

Complexity in Stage Determination: The process of determining pipeline stages within Cells may introduce complexity, especially when dealing with models that have varying computational requirements across stages. Ensuring accurate stage partitioning while considering communication overhead between stages could be challenging.

Limited Flexibility: Using Cell as a fixed granularity may limit the flexibility in exploring alternative scheduling options beyond what is predefined within each Cell. This rigidity could restrict the system's ability to adapt to unforeseen changes or optimizations in resource allocation strategies.

Increased Overhead: As Cells increase the granularity of scheduling choices by introducing additional dimensions (pipeline stages), it may lead to increased computational overhead during performance estimation and tuning processes due to a larger search space being considered.

How might advancements in hardware technology impact the efficiency of systems like Crius in the future

Advancements in hardware technology are likely to impact the efficiency of systems like Crius in several ways:

Improved Performance Capabilities: With advancements such as faster GPUs, higher memory bandwidths, and enhanced interconnect technologies (e.g., NVLink), systems like Crius will be able to leverage these improvements for faster computation and communication between GPUs.

2Enhanced Scalability: Future hardware advancements might enable larger-scale clusters with increased GPU counts per server or node without compromising performance or increasing latency significantly.
3Energy Efficiency: More energy-efficient hardware designs would result in reduced power consumption for training large models on heterogeneous clusters using systems like Crius.
4Specialized Hardware Accelerators: The emergence of specialized AI accelerators tailored for deep learning workloads could further boost system efficiency by offloading specific tasks from general-purpose GPUs.