Presentation
Heterogeneous HPC Compute Continuum: A Roadmap for Workflow Mapping and Scheduling From Sensor to Supercomputer
DescriptionEfficient workload mapping and scheduling in heterogeneous HPC environments connecting from IoT, edge devices to cloud is essential for optimizing resource use, reducing makespan, and ensuring adaptability. This research explores advanced solutions addressing mapping and scheduling by investigating the gaps in surveying the available tools and techniques that include classical optimization methods, emerging AI-driven models, and hybrid quantum-inspired approaches.
For workflow-based workload mapping and scheduling, the study employs a proper system and workload modeling and evaluates mixed-integer linear programming (MILP) for optimal assignment in smaller scenarios. In larger environments, a graph neural networks and reinforcement learning (GNN-RL) framework scales efficiently by learning adaptive policies reflecting task dependencies and system characteristics.
For task-based workload mapping and scheduling, outlined integrated AI scheduler (IAIS) framework dynamically manages resources in distributed, cloud, and HPC environments. IAIS combines recurrent neural networks (RNNs) and temporal convolutional networks (TCNs) for predicting optimal task allocation. Enhanced with proximal policy optimization (PPO)-based reinforcement learning, IAIS effectively predicts throughput, minimizes latency, and maximizes resource utilization. Complementary machine-learning models (e.g., simpler RNNs) further expedite allocation of independent tasks, notably in cloud contexts.
Comparative evaluations indicate notable tools and techniques for optimization performance, scalability, and resource efficiency applying IAIS, MILP, and GNN-RL. Specifically, IAIS and GNN-RL demonstrate strong adaptability and scalability within heterogeneous compute continuum environments, laying the groundwork for future cognitive scheduling assistants capable of real-time autonomous optimization.
For workflow-based workload mapping and scheduling, the study employs a proper system and workload modeling and evaluates mixed-integer linear programming (MILP) for optimal assignment in smaller scenarios. In larger environments, a graph neural networks and reinforcement learning (GNN-RL) framework scales efficiently by learning adaptive policies reflecting task dependencies and system characteristics.
For task-based workload mapping and scheduling, outlined integrated AI scheduler (IAIS) framework dynamically manages resources in distributed, cloud, and HPC environments. IAIS combines recurrent neural networks (RNNs) and temporal convolutional networks (TCNs) for predicting optimal task allocation. Enhanced with proximal policy optimization (PPO)-based reinforcement learning, IAIS effectively predicts throughput, minimizes latency, and maximizes resource utilization. Complementary machine-learning models (e.g., simpler RNNs) further expedite allocation of independent tasks, notably in cloud contexts.
Comparative evaluations indicate notable tools and techniques for optimization performance, scalability, and resource efficiency applying IAIS, MILP, and GNN-RL. Specifically, IAIS and GNN-RL demonstrate strong adaptability and scalability within heterogeneous compute continuum environments, laying the groundwork for future cognitive scheduling assistants capable of real-time autonomous optimization.
Event Type
Doctoral Showcase
TimeThursday, 20 November 20252:45pm - 3:00pm CST
Location230
Archive
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