Communication-Efficient Algorithms and Infrastructures for Large-Scale Distributed Deep Learning

Kernekoncepter

Efficient communication algorithms and infrastructures are crucial for achieving high performance in large-scale distributed deep learning, addressing challenges such as model synchronization, communication data compression, resource allocation, and task scheduling.

Resumé

The article surveys the literature on communication-efficient technologies for large-scale distributed deep learning. It first introduces efficient algorithms for model synchronization and communication data compression in the context of large-scale distributed training.
Key highlights:

Synchronous, asynchronous, and other variants of distributed SGD algorithms are discussed, focusing on their trade-offs between communication efficiency and model consistency.
Theoretical analyses on the convergence guarantees of distributed SGD algorithms are presented.
Communication-efficient algorithms for model synchronization in large-scale federated learning environments are explored, addressing challenges posed by heterogeneity in data, models, and resources.
The article then examines various communication-efficient strategies for resource allocation and task scheduling in large-scale distributed training and inference. It also introduces state-of-the-art communication infrastructure technologies at different system layers for high-performance communication in large-scale deep learning clusters.
Finally, a case study on the distributed training of large language models is presented to illustrate how these communication-efficient solutions can be applied in real-world scenarios.

Statistik

"With the rapid growth in the volume of data sets, models, and devices in the domain of deep learning, there is increasing attention on large-scale distributed deep learning."
"Due to intensive synchronization of models and sharing of data across GPUs and computing nodes during distributed training and inference processes, communication efficiency becomes the bottleneck for achieving high performance at a large scale."

Citater

"Efficient communication is crucial for achieving high performance at different levels in distributed DL."
"Addressing these communication challenges at various levels in diverse environments is crucial for high-performance large-scale distributed DL."

Vigtigste indsigter udtrukket fra

Communication-Efficient Large-Scale Distributed Deep Learning

by Feng Liang,Z... kl. arxiv.org 04-10-2024

https://arxiv.org/pdf/2404.06114.pdf

Dybere Forespørgsler

How can communication-efficient technologies be applied to distributed deep learning in edge computing and Internet of Things (IoT) scenarios, where devices have heterogeneous computational and communication capabilities

In edge computing and IoT scenarios, where devices have varying computational and communication capabilities, communication-efficient technologies play a crucial role in optimizing distributed deep learning processes. One approach is to leverage edge servers to offload computational tasks from resource-constrained devices, reducing the burden on individual devices and improving overall efficiency. By distributing the workload strategically across edge servers based on their capabilities, tasks can be allocated to the most suitable resources, balancing computational and communication requirements. Additionally, implementing edge caching mechanisms can reduce data transmission and latency by storing frequently accessed data closer to the devices, minimizing the need for extensive communication.
Furthermore, the use of federated learning in these scenarios can enhance privacy and security by allowing devices to train locally on their data and only share model updates with a central server. This approach reduces the amount of data transmitted over the network, addressing communication challenges in heterogeneous environments. By optimizing communication protocols, data compression techniques, and resource allocation strategies tailored to the specific capabilities of edge devices, communication-efficient technologies can significantly improve the performance of distributed deep learning in edge computing and IoT settings.

What are the potential challenges and trade-offs in designing communication-efficient algorithms that can handle both model parallelism and data parallelism simultaneously for large-scale distributed deep learning

Designing communication-efficient algorithms that can handle both model parallelism and data parallelism simultaneously for large-scale distributed deep learning involves addressing several potential challenges and trade-offs. One challenge is balancing the communication overhead associated with exchanging model parameters across nodes in model parallelism with the data transfer requirements in data parallelism. Efficient synchronization mechanisms need to be implemented to ensure that model updates are propagated accurately while minimizing communication latency and congestion.
Trade-offs may arise in determining the optimal partitioning of the model and data across nodes to maximize parallelism without compromising communication efficiency. Strategies such as overlapping computation and communication, asynchronous updates, and adaptive synchronization frequencies can help mitigate these trade-offs. Additionally, optimizing the network topology, communication protocols, and data compression techniques can further enhance the performance of communication-efficient algorithms handling both parallelism modes in large-scale distributed deep learning systems.

How can advances in hardware technologies, such as programmable network devices and silicon photonic interconnects, further improve the communication efficiency and scalability of large-scale distributed deep learning systems in the future

Advances in hardware technologies, such as programmable network devices and silicon photonic interconnects, hold significant potential to improve the communication efficiency and scalability of large-scale distributed deep learning systems in the future. Programmable network devices enable the customization of communication protocols and data processing at the network level, allowing for tailored solutions to optimize data transfer and reduce latency in distributed environments.
Silicon photonic interconnects offer high-speed, low-latency communication between nodes, enhancing the overall performance of distributed deep learning tasks. By leveraging these advanced hardware technologies, large-scale distributed deep learning systems can achieve faster model synchronization, reduced communication overhead, and improved scalability. Additionally, the integration of these hardware advancements with efficient algorithms and communication strategies can further enhance the overall efficiency and effectiveness of distributed deep learning in complex and heterogeneous environments.