Merge pull request #26318 from hanliutong:rvv-intrin-m2

Use LMUL=2 in the RISC-V Vector (RVV) backend of Universal Intrinsic. #26318

The modification of this patch involves the RVV backend of Universal Intrinsic, replacing `LMUL=1` with `LMUL=2`.

Now each Universal Intrinsic type actually corresponds to two RVV vector registers, and each Intrinsic function also operates two vector registers. Considering that algorithms written using Universal Intrinsic usually do not use the maximum number of registers, this can help the RVV backend utilize more register resources without modifying the algorithm implementation

This patch is generally beneficial in performance.

We compiled OpenCV with `Clang-19.1.1` and `GCC-14.2.0` , ran it on `CanMV-k230` and `Banana-Pi F3`. Then we have four scenarios on combinations of compilers and devices. In `opencv_perf_core`, there are 3363 cases, of which:
- 901 (26.8%) cases achieved more than `5%` performance improvement in all four scenarios, and the average speedup of these test cases (compared to scalar) increased from `3.35x` to `4.35x`
- 75 (2.2%) cases had more than `5%` performance loss in all four scenarios, indicating that these cases are better with `LMUL=1` instead of `LMUL=2`. This involves `Mat_Transform`, `hasNonZero`, `KMeans`, `meanStdDev`, `merge` and `norm2`. Among them, `Mat_Transform` only has performance degradation in a few cases (`8UC3`), and the actual execution time of `hasNonZero` is so short that it can be ignored. For `KMeans`, `meanStdDev`, `merge` and `norm2`, we should be able to use the HAL to optimize/restore their performance. (In fact, we have already done this for `merge`  #26216 )

### Pull Request Readiness Checklist

See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request

- [x] I agree to contribute to the project under Apache 2 License.
- [x] To the best of my knowledge, the proposed patch is not based on a code under GPL or another license that is incompatible with OpenCV
- [ ] The PR is proposed to the proper branch
- [ ] There is a reference to the original bug report and related work
- [ ] There is accuracy test, performance test and test data in opencv_extra repository, if applicable
      Patch to opencv_extra has the same branch name.
- [ ] The feature is well documented and sample code can be built with the project CMake

modules/core/src/matmul.simd.hpp

@@ -1595,7 +1595,10 @@ transform_16u( const ushort* src, ushort* dst, const float* m, int len, int scn,
 static void
 transform_32f( const float* src, float* dst, const float* m, int len, int scn, int dcn )
 {
-#if (CV_SIMD || CV_SIMD_SCALABLE) && !defined(__aarch64__) && !defined(_M_ARM64)
+// Disabled for RISC-V Vector (scalable), because of:
+// 1. v_matmuladd for RVV is 128-bit only but not scalable, this will fail the test `Core_Transform.accuracy`.
+// 2. Both gcc and clang can autovectorize this, with better performance than using Universal intrinsic.
+#if (CV_SIMD || CV_SIMD_SCALABLE) && !defined(__aarch64__) && !defined(_M_ARM64) && !(CV_TRY_RVV && CV_RVV)
     int x = 0;
     if( scn == 3 && dcn == 3 )
     {