Hardware-Based Collective Profiling for Late-Node Workloads
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Solution Overview
Problem
Distributed applications experience significant wait times due to late nodes during collective operations, leading to reduced computing resource utilization and overall efficiency, particularly in neural network training.
Innovation Solution
Implement a system with switching devices equipped with calculation logical units (CLUs) that trace communication packets, generate tracing data, and manage data allocation based on this data to identify and optimize performance by balancing workload among nodes.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If collective operations are performed in distributed applications, then data can be passed between computing nodes, but wait times increase due to late nodes
Solution Approach 1:
The system performs preliminary actions by tracing packets and generating tracing data before collective operations complete, allowing early identification of late nodes. This enables proactive workload redistribution rather than reactive waiting, reducing overall wait time while maintaining data transmission reliability.
Solution Approach 2:
The system implements feedback mechanisms by continuously monitoring packet transmission timing and generating tracing data that identifies which nodes are late. This feedback loop allows the system to dynamically adjust data allocation and rebalance workloads based on real-time performance information, resolving the contradiction between reliable data passage and wait time reduction.
2Productivity
If wait times are reduced through optimization, then computing resource utilization improves, but system complexity increases due to tracing and profiling mechanisms
Solution Approach 1:
The system introduces tracing data as an intermediary element that mediates between packet transmission and performance optimization. Rather than directly complex interactions between nodes, the tracing data serves as a simplified representation that enables optimization decisions, reducing the effective complexity while improving productivity.
Solution Approach 2:
The system replaces direct mechanical coordination between computing nodes with a data-driven approach using tracing data. Instead of nodes directly communicating and coordinating their actions, the tracing data infrastructure substitutes this mechanical interaction with automated profiling and optimization algorithms, reducing system complexity while enhancing resource utilization.
3Productivity
If data allocation is managed dynamically based on tracing data, then efficiency improves, but measurement and detection complexity increases
Solution Approach 1:
The system implements self-service by having the tracing infrastructure automatically generate and process tracing data without requiring complex external measurement systems. The packet tracing and temporal information collection are performed autonomously by the system itself, reducing the difficulty of detection and measurement while enabling dynamic data allocation for improved efficiency.
Data Source
AI summary
A system includes one or more processors to trace one or more packets transmitted by an application distributed among a plurality of computing nodes. The one or more processors are to generate tracing data based at least in part on tracing the one or more packets. The tracing data includes temporal information associated with transmission of the one or more packets. The one or more processors are to manage a data allocation associated with the application based on the tracing data.