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Refactor Newton solver: move Hessian from arena back to the stack.

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The changes in 9a0dc20821, moving the Hessian from the stack to the arena, should be rolled back: they prevent future threading over islands. Unlike the stack, arena allocations are not thread-friendly. The reason for the original move was to save memory, but the savings are small: `O(ctx->nH)` and only linear in `nv`. The significant reduction of the Cholesky factor, from quadratic in `nv` to quadratic in the largest dof island — the reduction afforded by 2dd518734f — remains in place.

Also refactor and improve readability.

PiperOrigin-RevId: 685717356
Change-Id: Ia9ec3e44a62d459a3b9cffb578cf6479d5aa1d7f
This commit is contained in:
Yuval Tassa
2024-10-14 08:32:44 -07:00
committed by Copybara-Service
parent ac91a7639d
commit 7ec94f46d4
4 changed files with 260 additions and 222 deletions
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test/engine/engine_util_sparse_test.cc
+14 -16
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@@ -972,6 +972,9 @@ TEST_F(EngineUtilSparseTest, MjuSqrMatTDSparse14) {
}
TEST_F(EngineUtilSparseTest, MjuCholFactorNNZ) {
mjModel* model = LoadModelFromString(modelStr);
mjData* d = mj_makeData(model);
// A = [[1, 0],
// [0, 1]]
int nA = 2;
@@ -981,11 +984,9 @@ TEST_F(EngineUtilSparseTest, MjuCholFactorNNZ) {
int rowadrA[2];
int colindA[4];
int rownnzA_factor[2];
int parentA[2];
int workspaceA[2];
mju_dense2sparse(sparseA, matA, nA, nA, rownnzA, rowadrA, colindA);
int nnzA = mju_cholFactorNNZ(rownnzA_factor, parentA, workspaceA, rownnzA,
rowadrA, colindA, nA);
int nnzA = mju_cholFactorNNZ(rownnzA_factor,
rownnzA, rowadrA, colindA, nA, d);
EXPECT_EQ(nnzA, 2);
EXPECT_THAT(AsVector(rownnzA_factor, 2), ElementsAre(1, 1));
@@ -1000,11 +1001,9 @@ TEST_F(EngineUtilSparseTest, MjuCholFactorNNZ) {
int rowadrB[3];
int colindB[9];
int rownnzB_factor[3];
int parentB[3];
int workspaceB[3];
mju_dense2sparse(sparseB, matB, nB, nB, rownnzB, rowadrB, colindB);
int nnzB = mju_cholFactorNNZ(rownnzB_factor, parentB, workspaceB, rownnzB,
rowadrB, colindB, nB);
int nnzB = mju_cholFactorNNZ(rownnzB_factor,
rownnzB, rowadrB, colindB, nB, d);
EXPECT_EQ(nnzB, 5);
EXPECT_THAT(AsVector(rownnzB_factor, 3), ElementsAre(1, 2, 2));
@@ -1019,11 +1018,9 @@ TEST_F(EngineUtilSparseTest, MjuCholFactorNNZ) {
int rowadrC[3];
int colindC[9];
int rownnzC_factor[3];
int parentC[3];
int workspaceC[3];
mju_dense2sparse(sparseC, matC, nC, nC, rownnzC, rowadrC, colindC);
int nnzC = mju_cholFactorNNZ(rownnzC_factor, parentC, workspaceC, rownnzC,
rowadrC, colindC, nC);
int nnzC = mju_cholFactorNNZ(rownnzC_factor,
rownnzC, rowadrC, colindC, nC, d);
EXPECT_EQ(nnzC, 4);
EXPECT_THAT(AsVector(rownnzC_factor, 3), ElementsAre(1, 2, 1));
@@ -1039,14 +1036,15 @@ TEST_F(EngineUtilSparseTest, MjuCholFactorNNZ) {
int rowadrD[4];
int colindD[16];
int rownnzD_factor[4];
int parentD[4];
int workspaceD[4];
mju_dense2sparse(sparseD, matD, nD, nD, rownnzD, rowadrD, colindD);
int nnzD = mju_cholFactorNNZ(rownnzD_factor, parentD, workspaceD, rownnzD,
rowadrD, colindD, nD);
int nnzD = mju_cholFactorNNZ(rownnzD_factor,
rownnzD, rowadrD, colindD, nD, d);
EXPECT_EQ(nnzD, 8);
EXPECT_THAT(AsVector(rownnzD_factor, 4), ElementsAre(1, 2, 2, 3));
mj_deleteData(d);
mj_deleteModel(model);
}
} // namespace