Extend the current CPU-based data generation code #2 to utilize GPU acceleration. This is motivated by the need for efficient perturbation of load and topology (such as N-1 contingencies) on GPU hardware.
Scope:
- Efficiently support load and topology perturbations at scale using GPU.
- The work should allow exhaustive studies such as N-1, while benefiting from GPU parallelism.
Approach Exploration:
- Investigate using MadNLP + JuMP + GPU for direct problem formulation and solving.
- Explore ExaModelsPower.jl as an alternative implementation and benchmarking tool.
- Compare the capabilities, performance, and ease of integration for both approaches.
Tasks:
- Analyze the feasibility and performance of MadNLP + JuMP + GPU solution for the target use case
- Analyze how ExaModelsPower.jl can be used for GPU-accelerated power grid studies
- Develop or integrate GPU kernels for load/topology perturbation
- Document design decisions, code changes, integration steps, and benchmarking results
- Provide examples demonstrating large-scale N-1 studies on GPU
Motivation:
Accelerating data generation and study for contingency analysis using GPUs will enable near real-time computation and support large-scale, high-fidelity grid modeling applications.
Extend the current CPU-based data generation code #2 to utilize GPU acceleration. This is motivated by the need for efficient perturbation of load and topology (such as N-1 contingencies) on GPU hardware.
Scope:
Approach Exploration:
Tasks:
Motivation:
Accelerating data generation and study for contingency analysis using GPUs will enable near real-time computation and support large-scale, high-fidelity grid modeling applications.