Fabric-Integrated Data Pulling Engine for HPC Latency Reduction
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Solution Overview
Problem
Existing high-performance computing (HPC) fabrics and communication stacks face inefficiencies in dynamic producer-consumer data transfers, leading to polling overheads, latency, and serialization overheads due to the need for multiple communication operations and synchronization.
Innovation Solution
A Data Pulling Engine (DPE) integrated with the fabric endpoint enables one-sided pull operations, allowing consumers to request data from producers with a single asynchronous message, eliminating polling and minimizing serialization overheads by managing data consumption efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If traditional HPC fabric communication stacks are used for dynamic producer-consumer data transfers, then data can be transferred between nodes, but polling overheads and latency occur due to multiple communication operations and synchronization requirements
Solution Approach 1:
The patent extracts the data pulling functionality from the traditional communication stack and implements it directly in the fabric endpoint logic. This separation removes the need for complex polling and synchronization operations at the application level, as the fabric endpoint autonomously pulls data when consumers request it, thereby reducing latency and simplifying the communication protocol.
Solution Approach 2:
The fabric endpoint acts as an intermediary between producers and consumers. Instead of having producers and consumers directly coordinate through multiple communication operations, the fabric endpoint mediates the data transfer by autonomously pulling data from producers when consumers request it. This intermediary role eliminates the need for complex synchronization and reduces the number of communication steps required.
2Productivity
If traditional HPC fabric communication stacks are used for dynamic producer-consumer data transfers, then data can be transferred between nodes, but serialization overheads occur due to the need for multiple communication operations and synchronization
Solution Approach 1:
The patent extracts the data pulling functionality from the traditional communication stack and implements it directly in the fabric endpoint logic. This separation removes the need for complex polling and synchronization operations at the application level, as the fabric endpoint autonomously pulls data when consumers request it, thereby reducing latency and simplifying the communication protocol.
Solution Approach 2:
The fabric endpoint acts as an intermediary between producers and consumers. Instead of having producers and consumers directly coordinate through multiple communication operations, the fabric endpoint mediates the data transfer by autonomously pulling data from producers when consumers request it. This intermediary role eliminates the need for complex synchronization and reduces the number of communication steps required.
3Loss of time
If one-sided pull operations are enabled through fabric-integrated DPE, then polling overheads and latency are reduced, but additional coordination messages are required
Solution Approach 1:
The patent inverts the traditional push model where producers actively send data to consumers. Instead, consumers express their data needs through pull requests, and the fabric endpoint autonomously pulls data from producers. This inversion eliminates the need for continuous polling by producers and reduces coordination overhead, as the pull request itself serves as the coordination mechanism rather than requiring additional acknowledgment and status check messages.
Data Source
AI summary
In an example, there is disclosed a compute node, comprising: first one or more logic elements comprising a data producer engine to produce a datum; and a host fabric interface to communicatively couple the compute node to a fabric, the host fabric interface comprising second one or more logic elements comprising a data pulling engine, the data pulling engine to: publish the datum as available; receive a pull request for the datum, the pull request comprising a node identifier for a data consumer; and send the datum to the data consumer via the fabric. There is also disclosed a method of providing a data pulling engine.