Interference-Driven GPU Resource Management in HPC Clusters
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Solution Overview
Problem
In high-performance computing (HPC) environments, coprocessors like GPUs and MICs are underutilized due to idle periods and resource contention when jobs time-share these resources, leading to performance slowdowns and increased costs, as current cluster managers do not support effective GPU-sharing and coprocessor time-sharing.
Innovation Solution
A middleware architecture that includes a job selector, node selector, offline profiler, coprocessor dynamic interference detection module, and interference response module to predict and manage interference between jobs, allowing for efficient sharing of coprocessor resources and optimizing node selection to minimize performance degradation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If coprocessors are dedicated to specific jobs until completion, then job reliability is improved, but coprocessor utilization deteriorates due to idle periods
Solution Approach 1:
The system dynamically transitions coprocessor allocation from static dedication to dynamic time-sharing based on job characteristics and interference predictions. The cluster manager adjusts resource allocation in real-time, allowing coprocessors to switch between jobs during idle periods while maintaining reliability through interference-aware scheduling.
Solution Approach 2:
The system changes the allocation parameter from exclusive dedicated access to controlled time-shared access. By modifying the access mode parameter and using interference prediction models, the system optimizes the balance between job reliability and resource utilization, allowing multiple jobs to share coprocessors when interference is minimal.
2Productivity
If coprocessors are time-shared across jobs, then coprocessor utilization is improved, but job performance deteriorates due to resource contention and interference
Solution Approach 1:
The system performs preliminary interference prediction and analysis before allocating coprocessor time-sharing. The offline profiler and interference prediction model evaluate potential interference scenarios in advance, allowing the cluster manager to make informed scheduling decisions that minimize performance impact while maximizing utilization.
Solution Approach 2:
The system implements runtime interference detection that monitors actual coprocessor usage and job performance. This feedback mechanism allows the cluster manager to adjust time-sharing allocations dynamically, identifying and resolving interference issues that arise during execution to maintain job performance.
3Loss of time
If coprocessors are time-shared across jobs, then idle periods are reduced, but predictability of job completion deteriorates due to interference-related slowdowns
Solution Approach 1:
The system performs preliminary interference analysis and prediction before job scheduling to establish baseline performance expectations. By evaluating interference scenarios in advance using the offline profiler, the system can provide more accurate predictions of job completion times even under time-sharing conditions.
Solution Approach 2:
The runtime interference detection system continuously monitors job performance and compares it against predicted baselines. This feedback allows the system to detect deviations from expected completion times and adjust scheduling decisions to maintain predictability while reducing idle periods.
Data Source
AI summary
Systems and methods are disclosed that share coprocessor resources between two or more applications in a computing cluster using a job selector to receive jobs from a job queue; a node selector coupled to the job selector; an off line profiler with an interference prediction model; a coprocessor dynamic interference detection module; and a coprocessor interference response module.