eq constraints porting to RAJA#45
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kswirydo wants to merge 3 commits intoolcf-hackathon-2026-devfrom
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Replace the PETSc-based equality constraint Jacobian computation in the PBPOLRAJAHIOPSPARSE model with direct GPU kernels using RAJA, eliminating the D2H-compute-H2D round trip. The sparsity pattern is now computed on the host during setup and the values are computed entirely on device. Key changes: - Add ComputeEqJacValuesGPU_PBPOLRAJAHIOPSPARSE in new gpu.cpp/hpp files - Add device arrays for flat-array indices (bus eqjacsp_selfidx, line eqjacsp_idx/eqjacsp_diag_idx/isdcline, gen xpdevidx/xpsetidx) - Fix nnz counting bugs (missing gen/load entries, off-by-one in line loop) and populate flat-array indices during model setup - Replace PETSc MatGetRow extraction in sparsity and values phases - Handle parallel lines by sharing off-diagonal positions with atomicAdd - Use pre-computed nnz in get_sparse_blocks_info instead of PETSc query - Add correctness test (test_eqjac_compare) and performance benchmark (test_eqjac_perf) Made-with: Cursor
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Closing in favor of #48 |
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Summary
Ported equality constraints from PETSc to RAJA. Supporting infractructure (i.e. new variables, code for counting number of elements etcetc) added too.