Hardware Message Scheduler for Allreduce Overlap
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current machine learning systems face challenges in optimizing Allreduce operation overlap, leading to underutilization of communication and computation resources, particularly in deep learning applications, where non-blocking Allreduce operations require careful scheduling to maximize resource utilization but often result in increased latency and reduced CPU availability for computation.
Innovation Solution
Implementing triggered operations within a hardware-based message scheduler to optimize the order and overlap of collective operations, such as Allreduce, by constructing a directed acyclic graph (DAG) of communication operations and offloading their execution to a Host Fabric Interface (HFI) scheduler, allowing for prioritization and efficient resource management without the need for software agents.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If non-blocking Allreduce operations are used to improve resource utilization, then communication and computation resources are better utilized, but scheduling complexity and latency increase
Solution Approach 1:
The patent introduces a hardware-based message scheduler as an intermediary component between the CPU and communication operations. This scheduler receives collective operation requests from multiple processes, constructs a DAG representing operation dependencies, and automatically determines execution order and overlap opportunities. By offloading the scheduling complexity to this dedicated hardware intermediary, the system achieves better resource utilization without burdening the CPU with complex scheduling logic.
Solution Approach 2:
The patent replaces software-based scheduling mechanisms with a hardware-based message scheduler. This substitution eliminates the overhead of software agent execution and reduces CPU involvement in scheduling decisions. The hardware scheduler directly manages the DAG construction and operation ordering, providing more efficient resource allocation and reduced latency compared to traditional software-based approaches.
2Adaptability or versatility
If software agents are used to manage collective operations, then operation scheduling is flexible, but CPU availability for computation decreases
Solution Approach 1:
The patent extracts the scheduling function from the CPU by implementing a dedicated hardware-based message scheduler. This scheduler is removed from the CPU's responsibility and operates independently to manage collective operations. The CPU no longer needs to allocate resources to software agents for scheduling, freeing up CPU cycles for computation while the hardware scheduler maintains flexible operation management through DAG-based scheduling.
Solution Approach 2:
The hardware-based message scheduler operates autonomously to manage collective operations without requiring continuous CPU intervention. It self-manages the construction of DAGs, determines execution orders, and coordinates communication operations independently. This self-service capability allows the system to maintain scheduling flexibility while minimizing CPU involvement, as the scheduler handles adaptive decision-making on its own.
3Loss of time
If Allreduce operations are overlapped to reduce latency, then communication efficiency improves, but operation scheduling becomes more complex
Solution Approach 1:
The patent implements preliminary action by constructing the DAG representation of collective operations in advance, before actual execution begins. The hardware scheduler analyzes operation dependencies and determines the optimal execution order and overlap opportunities upfront. This preliminary planning allows the system to maximize Allreduce operation overlap and reduce communication latency, as the scheduling decisions are made beforehand rather than dynamically during execution.
Solution Approach 2:
The patent introduces dynamics through the hardware-based message scheduler, which adaptively determines the execution order and overlap of Allreduce operations based on the DAG structure. The scheduler dynamically identifies opportunities for operation overlap and adjusts the execution schedule to minimize communication latency. This dynamic scheduling capability reduces latency while managing complexity through hardware-based automated decision-making.
Data Source
AI summary
An embodiment of a semiconductor package apparatus may include technology to embed one or more trigger operations in one or more messages related to collective operations for a neural network, and issue the one or more messages related to the collective operations to a hardware-based message scheduler in a desired order of execution. Other embodiments are disclosed and claimed.