diff options
Diffstat (limited to 'src/kernels/level3/pad.opencl')
-rw-r--r-- | src/kernels/level3/pad.opencl | 349 |
1 files changed, 349 insertions, 0 deletions
diff --git a/src/kernels/level3/pad.opencl b/src/kernels/level3/pad.opencl new file mode 100644 index 00000000..69324f20 --- /dev/null +++ b/src/kernels/level3/pad.opencl @@ -0,0 +1,349 @@ + +// ================================================================================================= +// This file is part of the CLBlast project. The project is licensed under Apache Version 2.0. This +// project loosely follows the Google C++ styleguide and uses a tab-size of two spaces and a max- +// width of 100 characters per line. +// +// Author(s): +// Cedric Nugteren <www.cedricnugteren.nl> +// +// This file contains the common kernels shared among different BLAS routines. This file contains +// kernels to copy and pad matrices in various ways, including: +// 1) copying into a larger matrix by adding padding +// 2) copying into a smaller matrix by removing padding +// 3) from upper/lower triangle into a full matrix +// +// ================================================================================================= + +// Enables loading of this file using the C++ pre-processor's #include (C++11 standard raw string +// literal). Comment-out this line for syntax-highlighting when developing. +R"( + +// ================================================================================================= + +// Parameters set by the tuner or by the database. Here they are given a basic default value in case +// this kernel file is used outside of the CLBlast library. +#ifndef PAD_DIMX + #define PAD_DIMX 8 // Local workgroup size in the first dimension (x) +#endif +#ifndef PAD_DIMY + #define PAD_DIMY 8 // Local workgroup size in the second dimension (y) +#endif +#ifndef PAD_WPTX + #define PAD_WPTX 1 // Work per thread in the first dimension (x) +#endif +#ifndef PAD_WPTY + #define PAD_WPTY 1 // Work per thread in the second dimension (y) +#endif + +// ================================================================================================= + +// Copies a matrix from source to destination. The output is padded with zero values in case the +// destination matrix dimensions are larger than the source matrix dimensions. Additionally, the ld +// value and offset can be different. +__attribute__((reqd_work_group_size(PAD_DIMX, PAD_DIMY, 1))) +__kernel void PadMatrix(const int src_one, const int src_two, + const int src_ld, const int src_offset, + __global const real* restrict src, + const int dest_one, const int dest_two, + const int dest_ld, const int dest_offset, + __global real* dest, + const int do_conjugate) { + + // Loops over the work per thread in both dimensions + #pragma unroll + for (int w_one=0; w_one<PAD_WPTX; ++w_one) { + const int id_one = (get_group_id(0)*PAD_WPTX + w_one) * PAD_DIMX + get_local_id(0); + #pragma unroll + for (int w_two=0; w_two<PAD_WPTY; ++w_two) { + const int id_two = (get_group_id(1)*PAD_WPTY + w_two) * PAD_DIMY + get_local_id(1); + if (id_two < dest_two && id_one < dest_one) { + + // Loads data if the thread IDs are within bounds of the source matrix. Otherwise, set the + // value to be written to zero. + real value; + SetToZero(value); + if (id_two < src_two && id_one < src_one) { + value = src[id_two*src_ld + id_one + src_offset]; + } + + // Stores the value in the destination matrix + if (do_conjugate == 1) { COMPLEX_CONJUGATE(value); } + dest[id_two*dest_ld + id_one + dest_offset] = value; + } + } + } +} + +// ================================================================================================= + +// Same as above, but now un-pads a matrix. This kernel reads data from a padded source matrix, but +// writes only the actual data back to the destination matrix. Again, the ld value and offset can +// be different. +__attribute__((reqd_work_group_size(PAD_DIMX, PAD_DIMY, 1))) +__kernel void UnPadMatrix(const int src_one, const int src_two, + const int src_ld, const int src_offset, + __global const real* restrict src, + const int dest_one, const int dest_two, + const int dest_ld, const int dest_offset, + __global real* dest, + const int upper, const int lower, + const int diagonal_imag_zero) { + + // Loops over the work per thread in both dimensions + #pragma unroll + for (int w_one=0; w_one<PAD_WPTX; ++w_one) { + const int id_one = (get_group_id(0)*PAD_WPTX + w_one) * PAD_DIMX + get_local_id(0); + #pragma unroll + for (int w_two=0; w_two<PAD_WPTY; ++w_two) { + const int id_two = (get_group_id(1)*PAD_WPTY + w_two) * PAD_DIMY + get_local_id(1); + + // Masking in case of triangular matrices: updates only the upper or lower part + bool condition = true; + if (upper == 1) { condition = (id_two >= id_one); } + else if (lower == 1) { condition = (id_two <= id_one); } + if (condition) { + + // Copies the value into the destination matrix. This is always within bounds of the source + // matrix, as we know that the destination matrix is smaller than the source. + if (id_two < dest_two && id_one < dest_one) { + real value = src[id_two*src_ld + id_one + src_offset]; + if (diagonal_imag_zero == 1 && id_one == id_two) { ImagToZero(value); } + dest[id_two*dest_ld + id_one + dest_offset] = value; + } + } + } + } +} + +// ================================================================================================= + +// Kernel to populate a squared symmetric matrix, given that the triangle which holds the data is +// stored as the lower-triangle of the input matrix. This uses the padding kernel's parameters. +__attribute__((reqd_work_group_size(PAD_DIMX, PAD_DIMY, 1))) +__kernel void SymmLowerToSquared(const int src_dim, + const int src_ld, const int src_offset, + __global const real* restrict src, + const int dest_dim, + const int dest_ld, const int dest_offset, + __global real* dest) { + + // Loops over the work per thread in both dimensions + #pragma unroll + for (int w_one=0; w_one<PAD_WPTX; ++w_one) { + const int id_one = (get_group_id(0)*PAD_WPTX + w_one) * PAD_DIMX + get_local_id(0); + #pragma unroll + for (int w_two=0; w_two<PAD_WPTY; ++w_two) { + const int id_two = (get_group_id(1)*PAD_WPTY + w_two) * PAD_DIMY + get_local_id(1); + if (id_two < dest_dim && id_one < dest_dim) { + + // Loads data from the lower-symmetric matrix + real result; + SetToZero(result); + if (id_two < src_dim && id_one < src_dim) { + if (id_two <= id_one) { result = src[id_two*src_ld + id_one + src_offset]; } + else { result = src[id_one*src_ld + id_two + src_offset]; } + } + + // Stores the result in the destination matrix + dest[id_two*dest_ld + id_one + dest_offset] = result; + } + } + } +} + +// Same as above, but now the matrix' data is stored in the upper-triangle +__attribute__((reqd_work_group_size(PAD_DIMX, PAD_DIMY, 1))) +__kernel void SymmUpperToSquared(const int src_dim, + const int src_ld, const int src_offset, + __global const real* restrict src, + const int dest_dim, + const int dest_ld, const int dest_offset, + __global real* dest) { + + // Loops over the work per thread in both dimensions + #pragma unroll + for (int w_one=0; w_one<PAD_WPTX; ++w_one) { + const int id_one = (get_group_id(0)*PAD_WPTX + w_one) * PAD_DIMX + get_local_id(0); + #pragma unroll + for (int w_two=0; w_two<PAD_WPTY; ++w_two) { + const int id_two = (get_group_id(1)*PAD_WPTY + w_two) * PAD_DIMY + get_local_id(1); + if (id_two < dest_dim && id_one < dest_dim) { + + // Loads data from the upper-symmetric matrix + real result; + SetToZero(result); + if (id_two < src_dim && id_one < src_dim) { + if (id_one <= id_two) { result = src[id_two*src_ld + id_one + src_offset]; } + else { result = src[id_one*src_ld + id_two + src_offset]; } + } + + // Stores the result in the destination matrix + dest[id_two*dest_ld + id_one + dest_offset] = result; + } + } + } +} + +// ================================================================================================= +#if PRECISION == 3232 || PRECISION == 6464 + +// Kernel to populate a squared hermitian matrix, given that the triangle which holds the data is +// stored as the lower-triangle of the input matrix. This uses the padding kernel's parameters. +__attribute__((reqd_work_group_size(PAD_DIMX, PAD_DIMY, 1))) +__kernel void HermLowerToSquared(const int src_dim, + const int src_ld, const int src_offset, + __global const real* restrict src, + const int dest_dim, + const int dest_ld, const int dest_offset, + __global real* dest) { + + // Loops over the work per thread in both dimensions + #pragma unroll + for (int w_one=0; w_one<PAD_WPTX; ++w_one) { + const int id_one = (get_group_id(0)*PAD_WPTX + w_one) * PAD_DIMX + get_local_id(0); + #pragma unroll + for (int w_two=0; w_two<PAD_WPTY; ++w_two) { + const int id_two = (get_group_id(1)*PAD_WPTY + w_two) * PAD_DIMY + get_local_id(1); + if (id_two < dest_dim && id_one < dest_dim) { + + // Loads data from the lower-hermitian matrix + real result; + SetToZero(result); + if (id_two < src_dim && id_one < src_dim) { + if (id_two <= id_one) { + result = src[id_two*src_ld + id_one + src_offset]; + if (id_one == id_two) { result.y = ZERO; } + } + else { + result = src[id_one*src_ld + id_two + src_offset]; + COMPLEX_CONJUGATE(result); + } + } + + // Stores the result in the destination matrix + dest[id_two*dest_ld + id_one + dest_offset] = result; + } + } + } +} + +// Same as above, but now the matrix' data is stored in the upper-triangle +__attribute__((reqd_work_group_size(PAD_DIMX, PAD_DIMY, 1))) +__kernel void HermUpperToSquared(const int src_dim, + const int src_ld, const int src_offset, + __global const real* restrict src, + const int dest_dim, + const int dest_ld, const int dest_offset, + __global real* dest) { + + // Loops over the work per thread in both dimensions + #pragma unroll + for (int w_one=0; w_one<PAD_WPTX; ++w_one) { + const int id_one = (get_group_id(0)*PAD_WPTX + w_one) * PAD_DIMX + get_local_id(0); + #pragma unroll + for (int w_two=0; w_two<PAD_WPTY; ++w_two) { + const int id_two = (get_group_id(1)*PAD_WPTY + w_two) * PAD_DIMY + get_local_id(1); + if (id_two < dest_dim && id_one < dest_dim) { + + // Loads data from the upper-hermitian matrix + real result; + SetToZero(result); + if (id_two < src_dim && id_one < src_dim) { + if (id_one <= id_two) { + result = src[id_two*src_ld + id_one + src_offset]; + if (id_one == id_two) { result.y = ZERO; } + } + else { + result = src[id_one*src_ld + id_two + src_offset]; + COMPLEX_CONJUGATE(result); + } + } + + // Stores the result in the destination matrix + dest[id_two*dest_ld + id_one + dest_offset] = result; + } + } + } +} + +#endif +// ================================================================================================= + +// Kernel to populate a squared triangular matrix, given that the triangle which holds the data is +// stored as the lower-triangle of the input matrix. This uses the padding kernel's parameters. +__attribute__((reqd_work_group_size(PAD_DIMX, PAD_DIMY, 1))) +__kernel void TrmmLowerToSquared(const int src_dim, + const int src_ld, const int src_offset, + __global const real* restrict src, + const int dest_dim, + const int dest_ld, const int dest_offset, + __global real* dest, + const int unit_diagonal) { + + // Loops over the work per thread in both dimensions + #pragma unroll + for (int w_one=0; w_one<PAD_WPTX; ++w_one) { + const int id_one = (get_group_id(0)*PAD_WPTX + w_one) * PAD_DIMX + get_local_id(0); + #pragma unroll + for (int w_two=0; w_two<PAD_WPTY; ++w_two) { + const int id_two = (get_group_id(1)*PAD_WPTY + w_two) * PAD_DIMY + get_local_id(1); + if (id_two < dest_dim && id_one < dest_dim) { + + // Loads data from the lower-triangular matrix + real result; + SetToZero(result); + if (id_two < src_dim && id_one < src_dim) { + if (id_two <= id_one) { result = src[id_two*src_ld + id_one + src_offset]; } + if (id_two == id_one && unit_diagonal) { SetToOne(result); } + // Else: result is zero + } + + // Stores the result in the destination matrix + dest[id_two*dest_ld + id_one + dest_offset] = result; + } + } + } +} + +// Same as above, but now the matrix' data is stored in the upper-triangle +__attribute__((reqd_work_group_size(PAD_DIMX, PAD_DIMY, 1))) +__kernel void TrmmUpperToSquared(const int src_dim, + const int src_ld, const int src_offset, + __global const real* restrict src, + const int dest_dim, + const int dest_ld, const int dest_offset, + __global real* dest, + const int unit_diagonal) { + + // Loops over the work per thread in both dimensions + #pragma unroll + for (int w_one=0; w_one<PAD_WPTX; ++w_one) { + const int id_one = (get_group_id(0)*PAD_WPTX + w_one) * PAD_DIMX + get_local_id(0); + #pragma unroll + for (int w_two=0; w_two<PAD_WPTY; ++w_two) { + const int id_two = (get_group_id(1)*PAD_WPTY + w_two) * PAD_DIMY + get_local_id(1); + if (id_two < dest_dim && id_one < dest_dim) { + + // Loads data from the upper-triangular matrix + real result; + SetToZero(result); + if (id_two < src_dim && id_one < src_dim) { + if (id_one <= id_two) { result = src[id_two*src_ld + id_one + src_offset]; } + if (id_one == id_two && unit_diagonal) { SetToOne(result); } + // Else: result is zero + } + + // Stores the result in the destination matrix + dest[id_two*dest_ld + id_one + dest_offset] = result; + } + } + } +} + +// ================================================================================================= + +// End of the C++11 raw string literal +)" + +// ================================================================================================= |