Workload Model Translation Across AI Cluster Testbeds
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Solution Overview
Problem
The challenge of architecting large-scale AI clusters is compounded by the need to support diverse transports, network topologies, programming models, and machine learning frameworks, which have different performance characteristics and trade-offs, leading to complex testing requirements that involve significant resource investment and inter-company collaboration.
Innovation Solution
A multi-mode test system that generates test environment-agnostic workload models from monitored traffic in distributed computing systems, translating them into testbed-specific models for performance evaluation, using a workload abstractor and test controller to execute test cases and report metrics across simulated, emulated, and physical testbeds.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If diverse transports and network topologies are supported to increase system versatility, then adaptability improves, but device complexity increases
Solution Approach 1:
The patent implements a universal testbed architecture that can operate in multiple modes (simulation, emulation, physical) to support diverse transports and network topologies. The test controller dynamically configures the testbed to match different runtime environments, allowing the same testing infrastructure to handle InfiniBand, Ethernet, ROCE, and various network topologies without requiring separate specialized systems for each configuration.
Solution Approach 2:
The workload abstractor serves as an intermediary that decouples workload definitions from specific testbed configurations. It generates environment-agnostic workload models that can be executed across different transports and topologies, mediating between the diverse runtime environments and the testing framework to reduce architectural complexity.
2Measurement precision
If extensive testing across multiple environments is performed to improve measurement precision, then performance metric accuracy improves, but loss of time increases
Solution Approach 1:
The system performs preliminary actions by generating environment-agnostic workload models that capture essential performance characteristics before actual testing. These pre-defined workload models can be quickly instantiated and executed across different testbed modes, eliminating the need to create and validate separate test configurations for each environment, thus reducing testing time while maintaining measurement precision.
Solution Approach 2:
The patent creates virtual copies of workload behavior through workload models that can be replicated across simulation, emulation, and physical testbeds. Instead of conducting separate extensive tests in each environment, the same workload model is copied and executed across multiple testbed modes, enabling parallel testing that achieves comprehensive performance measurement without proportional time increase.
3Adaptability or versatility
If multiple testbed modes are used to improve adaptability, then versatility improves, but device complexity increases
Solution Approach 1:
The testbed is designed with dynamic reconfigurability, allowing it to switch between simulation, emulation, and physical modes based on testing requirements. The test controller dynamically adjusts the testbed configuration and workload model instantiation to match the selected mode, enabling a single flexible system to replace multiple static testbeds and reducing overall system complexity.
Data Source
AI summary
Methods, systems, and computer readable media for emulating and testing data flows in distributed computing systems. An example system includes a workload abstractor configured for receiving monitored traffic in a distributed computing system performing a machine learning task and generating, using the monitored traffic, a test environment-agnostic workload model for the machine learning task and storing the test environment-agnostic workload model in a workload model repository with one or more other workload models. The system includes a test controller configured for selecting a test case for the machine learning task and a testbed mode for the test case; executing the test case by translating the test environment-agnostic workload model into a testbed-specific workload model for the testbed mode; and reporting, based on executing the test case, one or more performance metrics for the machine learning task.