Add the HAL implementation for the merge function on RISC-V Vector.#26216
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asmorkalov merged 1 commit intoopencv:4.xfrom Oct 9, 2024
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Add the HAL implementation for the merge function on RISC-V Vector.#26216asmorkalov merged 1 commit intoopencv:4.xfrom
asmorkalov merged 1 commit intoopencv:4.xfrom
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@mshabunin could you take a look? |
mshabunin
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Oct 8, 2024
| __riscv_vsse8_v_u8m1(dst + i*cn, sizeof(uchar)*2, a, vl); | ||
| } | ||
| #if defined(__clang__) | ||
| #pragma clang loop vectorize(disable) |
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Why do yoiu disable vectorization? Was it causing slowdown?
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Yes, since it's actually quite short, vectorizing this loop will cause performance degradation.
This is actually a ”tail“, but the static analysis of compiler does not know that it is the "tail".
mshabunin
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Oct 8, 2024
| if( k == 1 ) | ||
| { | ||
| const uchar* src0 = src[0]; | ||
| for( ; i <= len - vl; i += vl) |
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Did you try scalable vector length in the loop to avoid separate tail processing? Did it perform worse?
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Yes, the scalable vector length loop is worse and I guess the reason is due to the setvl instruction inside the loop body.
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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
| @@ -0,0 +1,363 @@ | |||
| #ifndef OPENCV_HAL_RVV_MERGE_HPP_INCLUDED | |||
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Need to add OpenCV license header
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Do you mean the shorter version below, or another longer version?
// This file is part of OpenCV project.
// It is subject to the license terms in the LICENSE file found in the top-level directory
// of this distribution and at http://opencv.org/license.html.
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Use LMUL=2 in the RISC-V Vector (RVV) backend of Universal Intrinsic. opencv#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` opencv#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
thewoz
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Feb 13, 2025
Use LMUL=2 in the RISC-V Vector (RVV) backend of Universal Intrinsic. opencv#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` opencv#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
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We have seen some performance gains after refactoring the SIMD loop code in OpenCV to be compatible with the RISC-V Vector backend. However, we still see a lot of gaps where we can use the HAL to implement RVV-specific optimizations to further improve performance/eliminate performance losses.
In the Core module, we find that the performance of the
mergeon32F/64Fwhen channel is3or4is reduced in compared to the scalar version, especially on the BPI-F3 board (~0.5x). This patch add the HAL implementation for the merge function on RISC-V Vector.GCC 14.2andClang 19.1.0, tested onk230andBPI-F3Compared to using UI, this patch (named
nativein the figure) eliminates the above performance loss, and the acceleration ratio is greater than the UI versionPull Request Readiness Checklist
See details at https://github.com/opencv/opencv/wiki/How_to_contribute#making-a-good-pull-request
Patch to opencv_extra has the same branch name.