GPU ineqality constraints (RAJA) to OLCF Hackathon branch#39
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kswirydo wants to merge 3 commits intoolcf-hackathon-2026-devfrom
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GPU ineqality constraints (RAJA) to OLCF Hackathon branch#39kswirydo wants to merge 3 commits intoolcf-hackathon-2026-devfrom
kswirydo wants to merge 3 commits intoolcf-hackathon-2026-devfrom
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* Update pybind11 to v2.13.6
Replace PETSc-based inequality Jacobian with GPU RAJA kernels Move the inequality constraint Jacobian computation for the HiOp sparse GPU solver entirely to the device, eliminating the per-iteration host back and forth (copy to host, PETSc compute, MatGetRow extraction, values copy back to device). Elimiate PETSc use from this part of the code. Three RAJA kernels now compute directly into device memory: - Generator set-point constraints (AGC) - Voltage-reactive-power bounds (FIXED_WITHIN_QBOUNDS) - Line flow limits (Sf^2/St^2 derivatives + slack variables) Supporting changes: - Analytical NNZ counting replaces PETSc MatGetInfo at solver setup - New device-side parameter fields (apf, vs, xpdevidx, xslackidx, bus-to-gen mapping) added to *ParamsRajaHiop structs - Sparse position indices assigned at model setup for all three contribution types Includes validation test (test_ineqjac_gpu) that solves with IPOPT, then compares PETSc and GPU Jacobian values at the converged solution. Optional -benchmark flag for performance comparison. Made-with: Cursor
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Closing in favor of #40 |
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As described in the title.