Supercharge AI/ML: Eliminating Packet Delays TCP+Ethernet Networks

Table of Contents

1. Introduction
2. Clockwork Product Suite Overview
3. Clockwork Agent Installation and Probe Mesh
4. Clockwork's Clock Synchronization
5. Precise One-Way Delay Measurements
6. VM Collocation Detection
7. Packet Rocket: Accurate Sensing and Control
8. Benefits of Clockwork's Software Solution
9. Distributed Training with Clockwork
10. Monitoring and Optimization with Clockwork
11. Evaluating Ray's Collective Communications Library
12. Conclusion

Introduction

Hey everyone! Thank You for joining me today. I'm B, a software engineer at Clockwork. In this article, we're going to dive into Clockwork's product suite and explore how it can be used to observe and control AIML workloads. First, let's start with an overview of Clockwork's tools for edge-Based observation and control. Then, we'll Delve deeper into specific aspects of Clockwork's product suite, such as clock synchronization, precise one-way delay measurements, VM collocation detection, and the Packet Rocket technology for accurate sensing and control. We'll also discuss how Clockwork can optimize distributed training and improve performance. To wrap things up, we'll evaluate Ray's Collective Communications Library and see how it performs with Clockwork's optimizations. So let's get started!

Clockwork Product Suite Overview

Clockwork's product suite offers a range of powerful tools for observing and controlling AIML workloads. The suite consists of several components, each playing a crucial role in optimizing performance. Let's take a closer look at each of these components.

Clockwork Agent Installation and Probe Mesh

The first step in utilizing Clockwork's product suite is to install the Clockwork agent on each virtual machine (VM) in your cluster. These agents establish what we call a "probe mesh" by exchanging small UDP packets, known as probes, with other agents. This probe mesh enables communication and synchronization between the VMs.

Clockwork's Clock Synchronization

One of the key capabilities of Clockwork's product suite is clock synchronization. The probes exchanged between the agents synchronize all of the VM clocks accurately to a common reference. This synchronization allows for the collection of accurate one-way delay measurements across the network.

Precise One-Way Delay Measurements

With Clockwork's precise one-way delay measurements, you can accurately measure how long a packet takes to traverse the network. By comparing send and receive timestamps on different machines, Clockwork can provide insights into network latency and identify any bottlenecks that may be impacting performance.

VM Collocation Detection

Clockwork's probe mesh also enables VM collocation detection. Through frequency corrections measured during clock synchronization, Clockwork can determine whether two or more VMs are located on the same physical host in the cloud. This information is essential for optimizing performance, as VM collocation can lead to resource contention and affect network delays.

Packet Rocket: Accurate Sensing and Control

Clockwork's product suite includes a technology called Packet Rocket, which offers accurate sensing and control capabilities at the edge of the network. Packet Rocket can measure various TCP traffic metrics, such as timeouts, retransmissions, packet drop, and delays. It also provides the abstraction of a zero-drop network to the user, optimizing network performance.

Clockwork's Two-Step Optimization Solution

Clockwork's two-step optimization solution is designed to accelerate workloads and improve training performance. The first step involves determining VM collocation and selecting VMs specifically to avoid collocation. This step optimizes resource utilization and reduces contention, resulting in faster training throughput.

The Second step of Clockwork's optimization solution is turning on Packet Rocket. This congestion control technology further enhances workload optimization by reducing network delays and improving overall network performance. By implementing these two steps, users can experience significant performance improvements and achieve faster completion times for their training tasks.

Distributed Training with Clockwork

Clockwork's product suite is particularly well-suited for distributed training. Traditional cloud tenant practices involve renting virtual machines with GPUs and running workloads without optimization. However, Clockwork brings a new level of optimization to this process.

First, by installing the Clockwork agents on the rented VMs, users gain the ability to monitor the entire VM cluster. Accurate time stamps, precise delay measurements, and VM collocation detection provide insights into network bottlenecks and resource utilization.

By leveraging the information gathered through Clockwork's product suite, users can select VMs that minimize collocation and avoid resource contention. This careful selection improves training throughput and accelerates the overall workload completion.

Additionally, by enabling Packet Rocket, users can further optimize training workloads. Packet Rocket's congestion control capabilities minimize delays and dropped packets, resulting in improved network performance and better utilization of GPUs.

By combining VM selection optimization, VM collocation detection, and the use of Packet Rocket, Clockwork enables users to maximize the potential of their distributed training workloads and achieve significant performance improvements.

Monitoring and Optimization with Clockwork

Clockwork's software solution not only provides accurate monitoring and control, but it also offers valuable insights into network bottlenecks and overall performance. By analyzing Clockwork's measurements and observations, users can identify the specific links and nodes causing bottlenecks in the network.

For example, by examining the communication rings commonly used in distributed training, Clockwork can pinpoint bottleneck links that are significantly slowing down the job completion. With VM collocation detection, users can further understand the root cause of these bottlenecks, such as hypervisor contention.

Clockwork's solution also offers the ability to measure systemwide delays, throughput, and application-level one-way delays. By turning on Packet Rocket, users can experience decreased delays and improved throughput across the entire network. The extensive monitoring and optimization capabilities provided by Clockwork can greatly enhance the performance of AIML workloads.

Evaluating Ray's Collective Communications Library

One popular distributed training framework is Ray, which incorporates Nickel, a collective communications library. To evaluate the impact of Clockwork's optimizations, we ran experiments using Ray on a cluster of 16 hosts, each connected to two GPUs.

By comparing the task completion times with different optimization approaches, we observed significant performance benefits. For instance, simply choosing VMs to avoid collocation resulted in an 8% decrease in task completion time. Adding congestion control using Packet Rocket further improved the performance, reducing the completion time by 26%.

This evaluation demonstrates the compatibility and effectiveness of Clockwork's optimizations with existing distributed training frameworks like Ray. By integrating Clockwork's product suite into your training workflows, you can unlock substantial performance improvements and optimize your training tasks.

Conclusion

In conclusion, Clockwork's product suite offers a comprehensive set of tools for observing and controlling AIML workloads. The suite's features such as clock synchronization, precise delay measurements, VM collocation detection, and Packet Rocket technology provide valuable insights into network bottlenecks and the means to optimize performance.

Clockwork's optimizations bring significant performance improvements to distributed training and inference workloads. By carefully selecting VMs to avoid collocation, minimizing resource contention, and leveraging congestion control capabilities, users can accelerate training tasks and achieve better utilization of GPUs.

Whether you are using Nickel in the Ray framework or other distributed training frameworks, Clockwork's product suite can enhance your workflows and improve performance. Consider integrating Clockwork into your AIML workloads to achieve optimal performance and unlock the full potential of your training tasks.

Highlights

• Clockwork's product suite offers powerful tools for observing and controlling AIML workloads.
• Clockwork's clock synchronization and precise delay measurements provide accurate insights into network performance.
• VM collocation detection helps optimize resource utilization and reduces network bottlenecks.
• Packet Rocket technology enables accurate sensing and control at the edge of the network.
• Clockwork's two-step optimization solution improves distributed training performance and accelerates workload completion.
• Users can maximize the potential of their AI/ML workloads by leveraging Clockwork's monitoring and optimization capabilities.
• Clockwork is compatible with popular distributed training frameworks like Ray, offering performance benefits and enhanced optimization.
• By integrating Clockwork into training workflows, users can achieve significant performance improvements and better GPU utilization.

FAQ

Q: Can Clockwork be used with other cloud providers besides Google Cloud Platform (GCP)? A: Yes, Clockwork is compatible with AWS and Azure as well.

Q: Have you evaluated Clockwork's performance optimization for inference workloads? A: Our tests primarily focused on distributed training workloads. However, the insights and optimizations provided by Clockwork can be applicable to inference workloads as well.

Q: Does Clockwork's optimization solution work with InfiniBand networks? A: Clockwork can provide observability insights on InfiniBand networks. However, the controllability aspect is currently supported only on TCP/IP networks.

Q: Are the performance benefits of Clockwork's optimizations consistent across different types of communication primitives in distributed training? A: Yes, Clockwork's optimizations generally provide benefits across various communication primitives, resulting in improved task completion times.

Q: Can Clockwork be used with other distributed training frameworks besides Ray? A: Yes, Clockwork's product suite can be integrated with other frameworks to enhance performance and optimize distributed training tasks.

Q: How can I become a beta user of Clockwork's technologies? A: If you are running workloads on Google Cloud that use GPUs on TCP/IP networks and are interested in being a beta user, please reach out to us to explore how Clockwork's technology can improve the performance of your workloads.