HPC Application Mapping via Characterization and Benchmarking
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Solution Overview
Problem
High-Performance Computing (HPC) users face challenges in selecting the optimal platform for running data-intensive applications due to varying resources, different platform capabilities, and user preferences such as cost and sustainability, which existing scheduling systems are not designed to handle effectively.
Innovation Solution
An HPC Application Management Component that characterizes applications and platforms, benchmarks performance and cost criteria, and selects an optimal platform based on user preferences, with monitoring for potential adjustments, using modules like HPC Application Characterization, Mapping, and Monitoring to facilitate intelligent platform selection and resource management.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If HPC users manually select platforms based on limited knowledge, then platform selection is simple, but application performance and cost-effectiveness deteriorate
Solution Approach 1:
The patent introduces an intermediary system that includes a characterization module and a mapping module. The characterization module automatically analyzes application characteristics and platform capabilities, while the mapping module uses machine learning models to recommend optimal platform mappings. This intermediary system bridges the gap between users with limited platform knowledge and the complex multi-platform environment, enabling informed decisions without requiring users to become experts in platform selection.
Solution Approach 2:
The system implements feedback mechanisms where application performance data, platform utilization metrics, and cost information are continuously collected and fed back into the machine learning models. This feedback loop allows the mapping recommendations to improve over time, adapting to actual performance outcomes and changing platform conditions, thereby enhancing both application performance and cost-effectiveness dynamically.
2Adaptability or versatility
If existing scheduling systems are used, then system simplicity is maintained, but ability to handle multiple platform choices deteriorates
Solution Approach 1:
The patent creates a universal characterization framework that can assess diverse application types (scientific simulations, data analytics, machine learning workloads) and various platform configurations (cloud providers, on-premise systems, hybrid environments) using a common set of metrics and models. This multi-functional approach enables the system to adapt to new application types and platforms without requiring completely new scheduling mechanisms, thereby enhancing versatility while controlling complexity through standardization.
Solution Approach 2:
The system dynamically adjusts scheduling parameters based on characterized application requirements and current platform states. Instead of using fixed scheduling rules, the machine learning models modify mapping parameters in real-time based on workload characteristics, platform availability, cost rates, and performance metrics, enabling flexible adaptation to multiple platform choices without overwhelming system complexity.
3Ease of operation
If platform selection ignores user preferences, then selection process is simplified, but user satisfaction and cost-effectiveness deteriorate
Solution Approach 1:
The system performs preliminary characterization of user preferences, cost constraints, and performance requirements during the application submission phase. By capturing these parameters in advance and integrating them into the machine learning mapping models, the system pre-configures the decision-making framework to automatically consider user preferences during platform selection, eliminating the need for complex interactive queries while ensuring preference alignment in the final recommendations.
Data Source
AI summary
The mapping of High Performance Computing (“HPC”) applications to platforms is provided. An HPC application characterization module determines an HPC application signature to characterize the HPC application. An HPC application mapping module selects a platform from a plurality of platforms to execute the HPC application based on the HPC application signature and a set of benchmarks. An HPC application monitoring module monitors the execution of the HPC application on the selected platform.