Multi-Tree In-Network Vector Aggregation for Contention Relief
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Solution Overview
Problem
In-network compute operations are degraded due to physical limitations of the network structure and operation requirements, leading to issues such as localized contention, buffer size requirements, and mismatched data types between endpoints and switches, which can result in performance degradation and inefficiencies.
Innovation Solution
The solution involves dividing vector operations among multiple aggregation and reduction trees, with network switches performing elementwise and vector splitting operations to yield intermediate results, which are then processed and combined to produce a final result, transparently managing network topology and data type differences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If vectors are processed through a single aggregation and reduction tree, then the network structure is simple, but localized contention occurs and buffer size requirements increase
Solution Approach 1:
The patent divides the vector processing workload across multiple aggregation and reduction trees instead of using a single tree. Each tree processes a portion of the vectors, segmenting the overall computation task. This segmentation distributes the processing load, reducing localized contention and buffer requirements while maintaining or improving overall processing throughput.
2Quantity of substance
If vectors are processed through a single aggregation and reduction tree, then buffer size requirements are reduced, but contention increases and processing speed decreases
Solution Approach 1:
By segmenting the vector processing across multiple trees, each tree handles a subset of vectors with smaller buffer requirements. The parallel execution of multiple trees maintains high processing speed while each individual tree operates with reduced buffer sizes, avoiding the bottleneck that would occur in a single-tree architecture.
Solution Approach 2:
The patent introduces an additional dimension to the processing architecture by using multiple trees instead of a single tree. This dimensional expansion allows simultaneous processing of multiple vector subsets, increasing throughput and reducing contention without requiring proportionally larger buffers in any single processing path.
3Productivity
If vectors are split across multiple aggregation and reduction trees, then contention is alleviated and processing speed improves, but device complexity increases
Solution Approach 1:
The patent segments the vector processing task across multiple aggregation and reduction trees, with each tree handling a specific portion of the computation. This segmentation enables parallel processing that improves throughput and reduces contention, while the modular nature of the segmented architecture allows for scalable implementation that manages complexity.
4Adaptability or versatility
If endpoint devices and switches use mismatched data types, then adaptability is improved, but performance degradation occurs
Solution Approach 1:
The patent introduces type conversion functionality as an intermediary mechanism between endpoint devices and network switches with different data types. This intermediary capability allows vectors of different data types (e.g., FP16 at endpoints, FP32 in switches) to be seamlessly converted, maintaining adaptability across heterogeneous systems while preventing performance degradation that would occur without proper type matching.
Data Source
AI summary
In one embodiment, a network switch device includes a network interface to receive vectors from endpoint devices, and an aggregation and reduction accelerator to perform elementwise and vector splitting operations with the vectors as input yielding at least two intermediate vector results, wherein the network interface is to send the at least two intermediate vector results to different corresponding network switches in different switch aggregation trees, receive at least two final vector results of an aggregation and reduction process from the different switch aggregation trees, and combine the at least two final vector results to yield a combined final vector result, wherein the network interface is to send the combined final vector result to the endpoint devices.