NIC-Based Collective Acceleration for Lower-Latency Data Reduction
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Solution Overview
Problem
Traditional in-network reduction systems for complex collective operations like all-reduce increase latency and networking complexity, leading to poor performance in high-performance computing applications.
Innovation Solution
An accelerated compute fabric (ACF) system with multiple reduction processors that parallelizes data reduction operations by distributing input data across multiple processors, using scatter-gather lists to manage data movement and perform operations like summation, maximum, or minimum, thereby reducing data volume and latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If traditional in-network reduction is used for complex collective operations, then data reduction is achieved, but latency increases and networking complexity escalates
Solution Approach 1:
The system segments the monolithic reduction operation into multiple parallel reduction processors (e.g., 4 processors for 4x speedup). Each processor handles a portion of the data independently, allowing concurrent processing that reduces overall latency while maintaining data reduction effectiveness.
Solution Approach 2:
The patent transitions from single-processor sequential reduction to multi-processor parallel reduction, adding the dimension of parallelism. This dimensional change enables simultaneous data processing across multiple processors, dramatically reducing latency without sacrificing data reduction ratio.
2Quantity of substance
If traditional in-network reduction is used for complex collective operations, then data reduction is achieved, but networking complexity escalates
Solution Approach 1:
The reduction operation is segmented across multiple dedicated processors, distributing the computational complexity. Each processor handles a specific portion of the reduction task independently, simplifying the control logic for each individual processor while achieving complex collective operations through their coordinated parallel execution.
Solution Approach 2:
The system uses scatter-gather lists to create simplified data distribution patterns across multiple processors. Instead of managing complex point-to-point routing, the same reduction operation is copied and executed in parallel across multiple processors with standardized data access patterns, reducing networking complexity.
3Productivity
If data is moved to multiple reduction processors in parallel, then processing speed increases, but data movement overhead increases
Solution Approach 1:
The system performs preliminary data distribution using scatter-gather lists before the actual reduction operations begin. This pre-organization of data into processor-specific segments eliminates the need for complex runtime data routing, reducing data movement overhead while enabling parallel processing speedup.
Data Source
AI summary
A system and method for producing and transmitting reduced data is disclosed. In some embodiments, the system comprises an ACF and reduction processors. The reduction processors are configured to perform a data reduction process. The ACF is configured to obtain access to input data from multiple flows, the input data identified by SGLEs included in input SGLs, and move a portion of the input data from each flow of the multiple flows to a respective reduction processor of the multiple reduction processors, such that each reduction processor receives a respective portion of the input data from each flow. The ACF is further configured to obtain access to reduced data produced from the input data using the data reduction process performed by the multiple reduction processors and move the reduced data to one or more destinations, where the reduced data identified by an output SGL.