HPC Workflow Orchestration for Unstructured Computational Data
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Solution Overview
Problem
The challenge lies in effectively processing and integrating unstructured computational and experimental data, such as workflows, scripts, and log files, into computational models, particularly in high-performance computing environments, which are underutilized due to complexity in code generation and resource utilization.
Innovation Solution
A system comprising a supervisor model, HPC agent, and computing agent that generates workflows and code, identifies suitable HPC environments, executes code, and validates outputs to solve computational problems efficiently.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If conventional computational problem solving methods are used, then simplicity of implementation is maintained, but productivity and solution accuracy deteriorate due to inability to effectively process unstructured data and optimize HPC resource utilization
Solution Approach 1:
The system is segmented into distinct functional components: a supervisor model for high-level decision making, domain-specific models for specialized computational tasks, an HPC agent for resource management, and a computing agent for code execution. This segmentation allows each component to specialize in specific functions, improving overall productivity while managing complexity through modular design.
Solution Approach 2:
The supervisor model acts as an intermediary between the user's computational problem and the domain-specific models/HPC resources. It translates high-level problem descriptions into actionable workflows, coordinating between multiple specialized components to achieve efficient computational problem solving without requiring the user to directly manage system complexity.
2Manufacturing precision
If sophisticated code generation and workflow design are implemented to solve computational problems in HPC environments, then solution accuracy is improved, but ease of operation deteriorates due to complexity in code generation and resource utilization
Solution Approach 1:
The system employs self-service mechanisms where the supervisor model automatically generates appropriate workflows and code based on the computational problem description. The HPC agent autonomously identifies suitable HPC environments and allocates resources, eliminating the need for users to manually write complex code or configure HPC settings, thereby maintaining high solution accuracy while improving ease of operation.
Solution Approach 2:
The supervisor model performs preliminary actions by pre-generating workflows and code before actual computation begins. Domain-specific models prepare computational configurations in advance based on the problem type, allowing the system to deliver accurate solutions without requiring users to engage in complex manual setup procedures.
3Manufacturing precision
If multi-fidelity simulations and domain-specific models are integrated, then solution accuracy is improved, but device complexity increases due to need for sophisticated code generation and workflow design
Solution Approach 1:
The supervisor model serves as a universal coordinator that can manage multiple domain-specific models (e.g., physics, chemistry, biology, engineering) through a unified interface. This multi-functional design allows the system to integrate diverse domain-specific simulations and workflows while presenting a consistent, manageable interface, thereby improving solution accuracy across domains without proportionally increasing user-facing complexity.
4Productivity
If HPC resources are fully utilized for computational problems, then productivity is improved, but loss of time increases due to complexity in resource allocation and execution management
Solution Approach 1:
The HPC agent implements feedback mechanisms by continuously monitoring HPC environment capabilities, resource availability, and execution status. This real-time feedback allows the system to dynamically adjust resource allocation and workflow execution, maximizing HPC resource utilization for improved productivity while automatically managing the time required for resource coordination without manual intervention.
Data Source
AI summary
Systems and methods for solving computational problems using high-performance computing (HPC) are described herein. An example system receives a request from a computing device indicating a computational problem. The example system applies a supervisor model to the computational problem to generate (i) a workflow and (ii) a set of code, and an HPC agent of the example system determines a respective HPC environment satisfying computing resource requirements of the set of code. A computing agent of the example system executes the set of code within the respective HPC environment to generate an output associated with solving the computational problem, wherein the HPC agent controls execution of the set of code by the computing agent according to the workflow. The example system also applies the supervisor model to the output to generate a solution to the computational problem and provide the solution to a computing device.