Speed improvement with no AVX. 40% on Linux Intel Xeon and 60% on ARM Mac.
PiperOrigin-RevId: 488360660 Change-Id: I423269cd362fdcc2434393b691638ac6ed778ef2
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Copybara-Service
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@@ -16,6 +16,7 @@
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#define MUJOCO_SRC_ENGINE_ENGINE_UTIL_SPARSE_H_
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#include <mujoco/mjdata.h>
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#include <mujoco/mjexport.h>
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#include <mujoco/mjtnum.h>
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#ifdef __cplusplus
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@@ -25,8 +26,8 @@ extern "C" {
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//------------------------------ sparse operations -------------------------------------------------
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// dot-product, first vector is sparse
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mjtNum mju_dotSparse(const mjtNum* vec1, const mjtNum* vec2,
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const int nnz1, const int* ind1);
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MJAPI mjtNum mju_dotSparse(const mjtNum* vec1, const mjtNum* vec2,
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const int nnz1, const int* ind1);
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// dot-product, both vectors are sparse
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mjtNum mju_dotSparse2(const mjtNum* vec1, const mjtNum* vec2,
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@@ -42,9 +43,9 @@ void mju_sparse2dense(mjtNum* res, const mjtNum* mat, int nr, int nc,
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const int* rownnz, const int* rowadr, const int* colind);
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// multiply sparse matrix and dense vector: res = mat * vec
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void mju_mulMatVecSparse(mjtNum* res, const mjtNum* mat, const mjtNum* vec,
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int nr, const int* rownnz, const int* rowadr,
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const int* colind, const int* rowsuper);
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MJAPI void mju_mulMatVecSparse(mjtNum* res, const mjtNum* mat, const mjtNum* vec,
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int nr, const int* rownnz, const int* rowadr,
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const int* colind, const int* rowsuper);
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// compress layout of sparse matrix
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void mju_compressSparse(mjtNum* mat, int nr, int nc,
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