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Posted to commits@mahout.apache.org by ap...@apache.org on 2016/06/08 21:40:31 UTC
[34/51] [partial] mahout git commit: (nojira) add native-viennaCL
module to codebase. closes apache/mahout#241
http://git-wip-us.apache.org/repos/asf/mahout/blob/f7c1f802/native-viennaCL/src/main/cpp/viennacl/linalg/cuda/matrix_operations_col.hpp
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diff --git a/native-viennaCL/src/main/cpp/viennacl/linalg/cuda/matrix_operations_col.hpp b/native-viennaCL/src/main/cpp/viennacl/linalg/cuda/matrix_operations_col.hpp
new file mode 100644
index 0000000..44684ce
--- /dev/null
+++ b/native-viennaCL/src/main/cpp/viennacl/linalg/cuda/matrix_operations_col.hpp
@@ -0,0 +1,1847 @@
+#ifndef VIENNACL_LINALG_CUDA_MATRIX_OPERATIONS_COL_HPP_
+#define VIENNACL_LINALG_CUDA_MATRIX_OPERATIONS_COL_HPP_
+
+/* =========================================================================
+ Copyright (c) 2010-2016, Institute for Microelectronics,
+ Institute for Analysis and Scientific Computing,
+ TU Wien.
+ Portions of this software are copyright by UChicago Argonne, LLC.
+
+ -----------------
+ ViennaCL - The Vienna Computing Library
+ -----------------
+
+ Project Head: Karl Rupp rupp@iue.tuwien.ac.at
+
+ (A list of authors and contributors can be found in the manual)
+
+ License: MIT (X11), see file LICENSE in the base directory
+============================================================================= */
+
+/** @file viennacl/linalg/cuda/matrix_operations_col.hpp
+ @brief Implementations of column-major dense matrix related operations, including matrix-vector products, using CUDA.
+*/
+
+
+namespace viennacl
+{
+namespace linalg
+{
+namespace cuda
+{
+
+template<typename DestNumericT, typename SrcNumericT>
+__global__ void convert_col_kernel(DestNumericT * A,
+ unsigned int A_start1, unsigned int A_start2,
+ unsigned int A_inc1, unsigned int A_inc2,
+ unsigned int A_size1, unsigned int A_size2,
+ unsigned int A_internal_size1, unsigned int A_internal_size2,
+
+ const SrcNumericT * B,
+ unsigned int B_start1, unsigned int B_start2,
+ unsigned int B_inc1, unsigned int B_inc2,
+ unsigned int B_internal_size1, unsigned int B_internal_size2)
+{
+ unsigned int row_gid = (blockIdx.x * blockDim.x + threadIdx.x) / blockDim.x;
+ unsigned int col_gid = (blockIdx.x * blockDim.x + threadIdx.x) % blockDim.x;
+
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1] = B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1];
+}
+
+//
+// am
+//
+
+// alpha on CPU
+template<typename NumericT>
+__global__ void am_col_kernel(NumericT * A,
+ unsigned int A_start1, unsigned int A_start2,
+ unsigned int A_inc1, unsigned int A_inc2,
+ unsigned int A_size1, unsigned int A_size2,
+ unsigned int A_internal_size1, unsigned int A_internal_size2,
+
+ NumericT fac2,
+ unsigned int options2,
+ const NumericT * B,
+ unsigned int B_start1, unsigned int B_start2,
+ unsigned int B_inc1, unsigned int B_inc2,
+ unsigned int B_internal_size1, unsigned int B_internal_size2)
+{
+ NumericT alpha = fac2;
+ if (options2 & (1 << 0))
+ alpha = -alpha;
+
+ unsigned int row_gid = (blockIdx.x * blockDim.x + threadIdx.x) / blockDim.x;
+ unsigned int col_gid = (blockIdx.x * blockDim.x + threadIdx.x) % blockDim.x;
+
+ if (options2 & (1 << 1))
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1] = B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] / alpha;
+ }
+ else
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1] = B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] * alpha;
+ }
+}
+
+// alpha on GPU
+template<typename NumericT>
+__global__ void am_col_kernel(NumericT * A,
+ unsigned int A_start1, unsigned int A_start2,
+ unsigned int A_inc1, unsigned int A_inc2,
+ unsigned int A_size1, unsigned int A_size2,
+ unsigned int A_internal_size1, unsigned int A_internal_size2,
+
+ const NumericT * fac2,
+ unsigned int options2,
+ const NumericT * B,
+ unsigned int B_start1, unsigned int B_start2,
+ unsigned int B_inc1, unsigned int B_inc2,
+ unsigned int B_internal_size1, unsigned int B_internal_size2)
+{
+ NumericT alpha = *fac2;
+ if (options2 & (1 << 0))
+ alpha = -alpha;
+
+ unsigned int row_gid = (blockIdx.x * blockDim.x + threadIdx.x) % blockDim.x;
+ unsigned int col_gid = (blockIdx.x * blockDim.x + threadIdx.x) / blockDim.x;
+
+ if (options2 & (1 << 1))
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1] = B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] / alpha;
+ }
+ else
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1] = B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] * alpha;
+ }
+}
+
+
+//
+// ambm
+//
+
+// alpha and beta on CPU
+template<typename NumericT>
+__global__ void ambm_col_kernel(NumericT * A,
+ unsigned int A_start1, unsigned int A_start2,
+ unsigned int A_inc1, unsigned int A_inc2,
+ unsigned int A_size1, unsigned int A_size2,
+ unsigned int A_internal_size1, unsigned int A_internal_size2,
+
+ NumericT fac2,
+ unsigned int options2,
+ const NumericT * B,
+ unsigned int B_start1, unsigned int B_start2,
+ unsigned int B_inc1, unsigned int B_inc2,
+ unsigned int B_internal_size1, unsigned int B_internal_size2,
+
+ NumericT fac3,
+ unsigned int options3,
+ const NumericT * C,
+ unsigned int C_start1, unsigned int C_start2,
+ unsigned int C_inc1, unsigned int C_inc2,
+ unsigned int C_internal_size1, unsigned int C_internal_size2)
+{
+ NumericT alpha = fac2;
+ if (options2 & (1 << 0))
+ alpha = -alpha;
+
+ NumericT beta = fac3;
+ if (options3 & (1 << 0))
+ beta = -beta;
+
+ unsigned int row_gid = (blockIdx.x * blockDim.x + threadIdx.x) % blockDim.x;
+ unsigned int col_gid = (blockIdx.x * blockDim.x + threadIdx.x) / blockDim.x;
+
+ if (options2 & (1 << 1))
+ {
+ if (options3 & (1 << 1))
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1]
+ = B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] / alpha
+ + C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1] / beta;
+ }
+ else
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1]
+ = B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] / alpha
+ + C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1] * beta;
+ }
+ }
+ else
+ {
+ if (options3 & (1 << 1))
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1]
+ = B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] * alpha
+ + C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1] / beta;
+ }
+ else
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1]
+ = B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] * alpha
+ + C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1] * beta;
+ }
+ }
+}
+
+
+// alpha on CPU, beta on GPU
+template<typename NumericT>
+__global__ void ambm_col_kernel(NumericT * A,
+ unsigned int A_start1, unsigned int A_start2,
+ unsigned int A_inc1, unsigned int A_inc2,
+ unsigned int A_size1, unsigned int A_size2,
+ unsigned int A_internal_size1, unsigned int A_internal_size2,
+
+ NumericT fac2,
+ unsigned int options2,
+ const NumericT * B,
+ unsigned int B_start1, unsigned int B_start2,
+ unsigned int B_inc1, unsigned int B_inc2,
+ unsigned int B_internal_size1, unsigned int B_internal_size2,
+
+ const NumericT * fac3,
+ unsigned int options3,
+ const NumericT * C,
+ unsigned int C_start1, unsigned int C_start2,
+ unsigned int C_inc1, unsigned int C_inc2,
+ unsigned int C_internal_size1, unsigned int C_internal_size2)
+{
+ NumericT alpha = fac2;
+ if (options2 & (1 << 0))
+ alpha = -alpha;
+
+ NumericT beta = *fac3;
+ if (options3 & (1 << 0))
+ beta = -beta;
+
+ unsigned int row_gid = (blockIdx.x * blockDim.x + threadIdx.x) % blockDim.x;
+ unsigned int col_gid = (blockIdx.x * blockDim.x + threadIdx.x) / blockDim.x;
+
+ if (options2 & (1 << 1))
+ {
+ if (options3 & (1 << 1))
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1]
+ = B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] / alpha
+ + C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1] / beta;
+ }
+ else
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1]
+ = B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] / alpha
+ + C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1] * beta;
+ }
+ }
+ else
+ {
+ if (options3 & (1 << 1))
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1]
+ = B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] * alpha
+ + C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1] / beta;
+ }
+ else
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1]
+ = B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] * alpha
+ + C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1] * beta;
+ }
+ }
+}
+
+// alpha on GPU, beta on CPU
+template<typename NumericT>
+__global__ void ambm_col_kernel(NumericT * A,
+ unsigned int A_start1, unsigned int A_start2,
+ unsigned int A_inc1, unsigned int A_inc2,
+ unsigned int A_size1, unsigned int A_size2,
+ unsigned int A_internal_size1, unsigned int A_internal_size2,
+
+ const NumericT * fac2,
+ unsigned int options2,
+ const NumericT * B,
+ unsigned int B_start1, unsigned int B_start2,
+ unsigned int B_inc1, unsigned int B_inc2,
+ unsigned int B_internal_size1, unsigned int B_internal_size2,
+
+ NumericT fac3,
+ unsigned int options3,
+ const NumericT * C,
+ unsigned int C_start1, unsigned int C_start2,
+ unsigned int C_inc1, unsigned int C_inc2,
+ unsigned int C_internal_size1, unsigned int C_internal_size2)
+{
+ NumericT alpha = *fac2;
+ if (options2 & (1 << 0))
+ alpha = -alpha;
+
+ NumericT beta = fac3;
+ if (options3 & (1 << 0))
+ beta = -beta;
+
+ unsigned int row_gid = (blockIdx.x * blockDim.x + threadIdx.x) % blockDim.x;
+ unsigned int col_gid = (blockIdx.x * blockDim.x + threadIdx.x) / blockDim.x;
+
+ if (options2 & (1 << 1))
+ {
+ if (options3 & (1 << 1))
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1]
+ = B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] / alpha
+ + C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1] / beta;
+ }
+ else
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1]
+ = B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] / alpha
+ + C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1] * beta;
+ }
+ }
+ else
+ {
+ if (options3 & (1 << 1))
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1]
+ = B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] * alpha
+ + C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1] / beta;
+ }
+ else
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1]
+ = B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] * alpha
+ + C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1] * beta;
+ }
+ }
+}
+
+
+// alpha and beta on GPU
+template<typename NumericT>
+__global__ void ambm_col_kernel(
+ NumericT * A,
+ unsigned int A_start1, unsigned int A_start2,
+ unsigned int A_inc1, unsigned int A_inc2,
+ unsigned int A_size1, unsigned int A_size2,
+ unsigned int A_internal_size1, unsigned int A_internal_size2,
+
+ const NumericT * fac2,
+ unsigned int options2,
+ const NumericT * B,
+ unsigned int B_start1, unsigned int B_start2,
+ unsigned int B_inc1, unsigned int B_inc2,
+ unsigned int B_internal_size1, unsigned int B_internal_size2,
+
+ const NumericT * fac3,
+ unsigned int options3,
+ const NumericT * C,
+ unsigned int C_start1, unsigned int C_start2,
+ unsigned int C_inc1, unsigned int C_inc2,
+ unsigned int C_internal_size1, unsigned int C_internal_size2)
+{
+ NumericT alpha = *fac2;
+ if (options2 & (1 << 0))
+ alpha = -alpha;
+
+ NumericT beta = *fac3;
+ if (options3 & (1 << 0))
+ beta = -beta;
+
+ unsigned int row_gid = (blockIdx.x * blockDim.x + threadIdx.x) % blockDim.x;
+ unsigned int col_gid = (blockIdx.x * blockDim.x + threadIdx.x) / blockDim.x;
+
+ if (options2 & (1 << 1))
+ {
+ if (options3 & (1 << 1))
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1]
+ = B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] / alpha
+ + C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1] / beta;
+ }
+ else
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1]
+ = B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] / alpha
+ + C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1] * beta;
+ }
+ }
+ else
+ {
+ if (options3 & (1 << 1))
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1]
+ = B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] * alpha
+ + C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1] / beta;
+ }
+ else
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1]
+ = B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] * alpha
+ + C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1] * beta;
+ }
+ }
+}
+
+
+//
+// ambm_m
+//
+
+// alpha and beta on CPU
+template<typename NumericT>
+__global__ void ambm_m_col_kernel(
+ NumericT * A,
+ unsigned int A_start1, unsigned int A_start2,
+ unsigned int A_inc1, unsigned int A_inc2,
+ unsigned int A_size1, unsigned int A_size2,
+ unsigned int A_internal_size1, unsigned int A_internal_size2,
+
+ NumericT fac2,
+ unsigned int options2,
+ const NumericT * B,
+ unsigned int B_start1, unsigned int B_start2,
+ unsigned int B_inc1, unsigned int B_inc2,
+ unsigned int B_internal_size1, unsigned int B_internal_size2,
+
+ NumericT fac3,
+ unsigned int options3,
+ const NumericT * C,
+ unsigned int C_start1, unsigned int C_start2,
+ unsigned int C_inc1, unsigned int C_inc2,
+ unsigned int C_internal_size1, unsigned int C_internal_size2)
+{
+ NumericT alpha = fac2;
+ if (options2 & (1 << 0))
+ alpha = -alpha;
+
+ NumericT beta = fac3;
+ if (options3 & (1 << 0))
+ beta = -beta;
+
+ unsigned int row_gid = (blockIdx.x * blockDim.x + threadIdx.x) % blockDim.x;
+ unsigned int col_gid = (blockIdx.x * blockDim.x + threadIdx.x) / blockDim.x;
+
+ if (options2 & (1 << 1))
+ {
+ if (options3 & (1 << 1))
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1]
+ += B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] / alpha
+ + C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1] / beta;
+ }
+ else
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1]
+ += B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] / alpha
+ + C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1] * beta;
+ }
+ }
+ else
+ {
+ if (options3 & (1 << 1))
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1]
+ += B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] * alpha
+ + C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1] / beta;
+ }
+ else
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1]
+ += B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] * alpha
+ + C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1] * beta;
+ }
+ }
+}
+
+
+// alpha on CPU, beta on GPU
+template<typename NumericT>
+__global__ void ambm_m_col_kernel(
+ NumericT * A,
+ unsigned int A_start1, unsigned int A_start2,
+ unsigned int A_inc1, unsigned int A_inc2,
+ unsigned int A_size1, unsigned int A_size2,
+ unsigned int A_internal_size1, unsigned int A_internal_size2,
+
+ NumericT fac2,
+ unsigned int options2,
+ const NumericT * B,
+ unsigned int B_start1, unsigned int B_start2,
+ unsigned int B_inc1, unsigned int B_inc2,
+ unsigned int B_internal_size1, unsigned int B_internal_size2,
+
+ const NumericT * fac3,
+ unsigned int options3,
+ const NumericT * C,
+ unsigned int C_start1, unsigned int C_start2,
+ unsigned int C_inc1, unsigned int C_inc2,
+ unsigned int C_internal_size1, unsigned int C_internal_size2)
+{
+ NumericT alpha = fac2;
+ if (options2 & (1 << 0))
+ alpha = -alpha;
+
+ NumericT beta = *fac3;
+ if (options3 & (1 << 0))
+ beta = -beta;
+
+ unsigned int row_gid = (blockIdx.x * blockDim.x + threadIdx.x) % blockDim.x;
+ unsigned int col_gid = (blockIdx.x * blockDim.x + threadIdx.x) / blockDim.x;
+
+ if (options2 & (1 << 1))
+ {
+ if (options3 & (1 << 1))
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1]
+ = B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] / alpha
+ + C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1] / beta;
+ }
+ else
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1]
+ = B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] / alpha
+ + C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1] * beta;
+ }
+ }
+ else
+ {
+ if (options3 & (1 << 1))
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1]
+ = B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] * alpha
+ + C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1] / beta;
+ }
+ else
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1]
+ = B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] * alpha
+ + C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1] * beta;
+ }
+ }
+}
+
+// alpha on GPU, beta on CPU
+template<typename NumericT>
+__global__ void ambm_m_col_kernel(
+ NumericT * A,
+ unsigned int A_start1, unsigned int A_start2,
+ unsigned int A_inc1, unsigned int A_inc2,
+ unsigned int A_size1, unsigned int A_size2,
+ unsigned int A_internal_size1, unsigned int A_internal_size2,
+
+ const NumericT * fac2,
+ unsigned int options2,
+ const NumericT * B,
+ unsigned int B_start1, unsigned int B_start2,
+ unsigned int B_inc1, unsigned int B_inc2,
+ unsigned int B_internal_size1, unsigned int B_internal_size2,
+
+ NumericT fac3,
+ unsigned int options3,
+ const NumericT * C,
+ unsigned int C_start1, unsigned int C_start2,
+ unsigned int C_inc1, unsigned int C_inc2,
+ unsigned int C_internal_size1, unsigned int C_internal_size2)
+{
+ NumericT alpha = *fac2;
+ if (options2 & (1 << 0))
+ alpha = -alpha;
+
+ NumericT beta = fac3;
+ if (options3 & (1 << 0))
+ beta = -beta;
+
+ unsigned int row_gid = (blockIdx.x * blockDim.x + threadIdx.x) % blockDim.x;
+ unsigned int col_gid = (blockIdx.x * blockDim.x + threadIdx.x) / blockDim.x;
+
+ if (options2 & (1 << 1))
+ {
+ if (options3 & (1 << 1))
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1]
+ = B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] / alpha
+ + C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1] / beta;
+ }
+ else
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1]
+ = B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] / alpha
+ + C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1] * beta;
+ }
+ }
+ else
+ {
+ if (options3 & (1 << 1))
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1]
+ = B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] * alpha
+ + C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1] / beta;
+ }
+ else
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1]
+ = B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] * alpha
+ + C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1] * beta;
+ }
+ }
+}
+
+
+// alpha and beta on GPU
+template<typename NumericT>
+__global__ void ambm_m_col_kernel(
+ NumericT * A,
+ unsigned int A_start1, unsigned int A_start2,
+ unsigned int A_inc1, unsigned int A_inc2,
+ unsigned int A_size1, unsigned int A_size2,
+ unsigned int A_internal_size1, unsigned int A_internal_size2,
+
+ const NumericT * fac2,
+ unsigned int options2,
+ const NumericT * B,
+ unsigned int B_start1, unsigned int B_start2,
+ unsigned int B_inc1, unsigned int B_inc2,
+ unsigned int B_internal_size1, unsigned int B_internal_size2,
+
+ const NumericT * fac3,
+ unsigned int options3,
+ const NumericT * C,
+ unsigned int C_start1, unsigned int C_start2,
+ unsigned int C_inc1, unsigned int C_inc2,
+ unsigned int C_internal_size1, unsigned int C_internal_size2)
+{
+ NumericT alpha = *fac2;
+ if (options2 & (1 << 0))
+ alpha = -alpha;
+
+ NumericT beta = *fac3;
+ if (options3 & (1 << 0))
+ beta = -beta;
+
+ unsigned int row_gid = (blockIdx.x * blockDim.x + threadIdx.x) % blockDim.x;
+ unsigned int col_gid = (blockIdx.x * blockDim.x + threadIdx.x) / blockDim.x;
+
+ if (options2 & (1 << 1))
+ {
+ if (options3 & (1 << 1))
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1]
+ = B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] / alpha
+ + C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1] / beta;
+ }
+ else
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1]
+ = B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] / alpha
+ + C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1] * beta;
+ }
+ }
+ else
+ {
+ if (options3 & (1 << 1))
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1]
+ = B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] * alpha
+ + C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1] / beta;
+ }
+ else
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1]
+ = B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1] * alpha
+ + C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1] * beta;
+ }
+ }
+}
+
+
+
+//
+// assignments
+//
+
+template<typename NumericT>
+__global__ void matrix_col_assign_kernel(
+ NumericT * A,
+ unsigned int A_start1, unsigned int A_start2,
+ unsigned int A_inc1, unsigned int A_inc2,
+ unsigned int A_size1, unsigned int A_size2,
+ unsigned int A_internal_size1, unsigned int A_internal_size2,
+ NumericT alpha)
+{
+ unsigned int row_gid = (blockIdx.x * blockDim.x + threadIdx.x) % blockDim.x;
+ unsigned int col_gid = (blockIdx.x * blockDim.x + threadIdx.x) / blockDim.x;
+
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1] = alpha;
+}
+
+
+template<typename NumericT>
+__global__ void matrix_col_diagonal_assign_kernel(
+ NumericT * A,
+ unsigned int A_start1, unsigned int A_start2,
+ unsigned int A_inc1, unsigned int A_inc2,
+ unsigned int A_size1, unsigned int A_size2,
+ unsigned int A_internal_size1, unsigned int A_internal_size2,
+ NumericT alpha)
+{
+ unsigned int gid = (blockIdx.x * blockDim.x + threadIdx.x);
+
+ for (unsigned int row = gid; row < A_size1; row += blockDim.x * gridDim.x)
+ A[(row * A_inc1 + A_start1) + (row * A_inc2 + A_start2) * A_internal_size1] = alpha;
+}
+
+//
+// binary element-wise operations
+//
+
+template<typename NumericT>
+__global__ void element_op_col_kernel(
+ NumericT * A,
+ unsigned int A_start1, unsigned int A_start2,
+ unsigned int A_inc1, unsigned int A_inc2,
+ unsigned int A_size1, unsigned int A_size2,
+ unsigned int A_internal_size1, unsigned int A_internal_size2,
+
+ const NumericT * B,
+ unsigned int B_start1, unsigned int B_start2,
+ unsigned int B_inc1, unsigned int B_inc2,
+ unsigned int B_internal_size1, unsigned int B_internal_size2,
+
+ const NumericT * C,
+ unsigned int C_start1, unsigned int C_start2,
+ unsigned int C_inc1, unsigned int C_inc2,
+ unsigned int C_internal_size1, unsigned int C_internal_size2,
+
+ unsigned int op_type) //0: product, 1: division, 2: pow
+{
+ unsigned int row_gid = (blockIdx.x * blockDim.x + threadIdx.x) % blockDim.x;
+ unsigned int col_gid = (blockIdx.x * blockDim.x + threadIdx.x) / blockDim.x;
+
+ if (op_type == 2)
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1]
+ = pow(B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1],
+ C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1]);
+ }
+ else if (op_type == 1)
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1]
+ = B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1]
+ / C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1];
+ }
+ else if (op_type == 0)
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1]
+ = B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1]
+ * C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1];
+ }
+}
+
+template<typename NumericT>
+__global__ void element_op_int_col_kernel(
+ NumericT * A,
+ unsigned int A_start1, unsigned int A_start2,
+ unsigned int A_inc1, unsigned int A_inc2,
+ unsigned int A_size1, unsigned int A_size2,
+ unsigned int A_internal_size1, unsigned int A_internal_size2,
+
+ const NumericT * B,
+ unsigned int B_start1, unsigned int B_start2,
+ unsigned int B_inc1, unsigned int B_inc2,
+ unsigned int B_internal_size1, unsigned int B_internal_size2,
+
+ const NumericT * C,
+ unsigned int C_start1, unsigned int C_start2,
+ unsigned int C_inc1, unsigned int C_inc2,
+ unsigned int C_internal_size1, unsigned int C_internal_size2,
+
+ unsigned int op_type) //0: product, 1: division, 2: pow
+{
+ unsigned int row_gid = (blockIdx.x * blockDim.x + threadIdx.x) % blockDim.x;
+ unsigned int col_gid = (blockIdx.x * blockDim.x + threadIdx.x) / blockDim.x;
+
+ if (op_type == 1)
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1]
+ = B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1]
+ / C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1];
+ }
+ else if (op_type == 0)
+ {
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1]
+ = B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1]
+ * C[(row * C_inc1 + C_start1) + (col * C_inc2 + C_start2) * C_internal_size1];
+ }
+}
+
+
+//
+// unary element-wise operations
+//
+
+// abs
+template<typename NumericT>
+__global__ void matrix_col_element_abs_kernel(
+ NumericT * A,
+ unsigned int A_start1, unsigned int A_start2,
+ unsigned int A_inc1, unsigned int A_inc2,
+ unsigned int A_size1, unsigned int A_size2,
+ unsigned int A_internal_size1, unsigned int A_internal_size2,
+
+ const NumericT * B,
+ unsigned int B_start1, unsigned int B_start2,
+ unsigned int B_inc1, unsigned int B_inc2,
+ unsigned int B_internal_size1, unsigned int B_internal_size2)
+{
+ unsigned int row_gid = (blockIdx.x * blockDim.x + threadIdx.x) / blockDim.x;
+ unsigned int col_gid = (blockIdx.x * blockDim.x + threadIdx.x) % blockDim.x;
+
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1] = abs(B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1]);
+}
+
+
+// acos
+template<typename NumericT>
+__global__ void matrix_col_element_acos_kernel(
+ NumericT * A,
+ unsigned int A_start1, unsigned int A_start2,
+ unsigned int A_inc1, unsigned int A_inc2,
+ unsigned int A_size1, unsigned int A_size2,
+ unsigned int A_internal_size1, unsigned int A_internal_size2,
+
+ const NumericT * B,
+ unsigned int B_start1, unsigned int B_start2,
+ unsigned int B_inc1, unsigned int B_inc2,
+ unsigned int B_internal_size1, unsigned int B_internal_size2)
+{
+ unsigned int row_gid = (blockIdx.x * blockDim.x + threadIdx.x) / blockDim.x;
+ unsigned int col_gid = (blockIdx.x * blockDim.x + threadIdx.x) % blockDim.x;
+
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1] = acos(B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1]);
+}
+
+
+// asin
+template<typename NumericT>
+__global__ void matrix_col_element_asin_kernel(
+ NumericT * A,
+ unsigned int A_start1, unsigned int A_start2,
+ unsigned int A_inc1, unsigned int A_inc2,
+ unsigned int A_size1, unsigned int A_size2,
+ unsigned int A_internal_size1, unsigned int A_internal_size2,
+
+ const NumericT * B,
+ unsigned int B_start1, unsigned int B_start2,
+ unsigned int B_inc1, unsigned int B_inc2,
+ unsigned int B_internal_size1, unsigned int B_internal_size2)
+{
+ unsigned int row_gid = (blockIdx.x * blockDim.x + threadIdx.x) / blockDim.x;
+ unsigned int col_gid = (blockIdx.x * blockDim.x + threadIdx.x) % blockDim.x;
+
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1] = asin(B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1]);
+}
+
+
+// atan
+template<typename NumericT>
+__global__ void matrix_col_element_atan_kernel(
+ NumericT * A,
+ unsigned int A_start1, unsigned int A_start2,
+ unsigned int A_inc1, unsigned int A_inc2,
+ unsigned int A_size1, unsigned int A_size2,
+ unsigned int A_internal_size1, unsigned int A_internal_size2,
+
+ const NumericT * B,
+ unsigned int B_start1, unsigned int B_start2,
+ unsigned int B_inc1, unsigned int B_inc2,
+ unsigned int B_internal_size1, unsigned int B_internal_size2)
+{
+ unsigned int row_gid = (blockIdx.x * blockDim.x + threadIdx.x) / blockDim.x;
+ unsigned int col_gid = (blockIdx.x * blockDim.x + threadIdx.x) % blockDim.x;
+
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1] = atan(B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1]);
+}
+
+
+// ceil
+template<typename NumericT>
+__global__ void matrix_col_element_ceil_kernel(
+ NumericT * A,
+ unsigned int A_start1, unsigned int A_start2,
+ unsigned int A_inc1, unsigned int A_inc2,
+ unsigned int A_size1, unsigned int A_size2,
+ unsigned int A_internal_size1, unsigned int A_internal_size2,
+
+ const NumericT * B,
+ unsigned int B_start1, unsigned int B_start2,
+ unsigned int B_inc1, unsigned int B_inc2,
+ unsigned int B_internal_size1, unsigned int B_internal_size2)
+{
+ unsigned int row_gid = (blockIdx.x * blockDim.x + threadIdx.x) / blockDim.x;
+ unsigned int col_gid = (blockIdx.x * blockDim.x + threadIdx.x) % blockDim.x;
+
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1] = ceil(B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1]);
+}
+
+
+// cos
+template<typename NumericT>
+__global__ void matrix_col_element_cos_kernel(
+ NumericT * A,
+ unsigned int A_start1, unsigned int A_start2,
+ unsigned int A_inc1, unsigned int A_inc2,
+ unsigned int A_size1, unsigned int A_size2,
+ unsigned int A_internal_size1, unsigned int A_internal_size2,
+
+ const NumericT * B,
+ unsigned int B_start1, unsigned int B_start2,
+ unsigned int B_inc1, unsigned int B_inc2,
+ unsigned int B_internal_size1, unsigned int B_internal_size2)
+{
+ unsigned int row_gid = (blockIdx.x * blockDim.x + threadIdx.x) / blockDim.x;
+ unsigned int col_gid = (blockIdx.x * blockDim.x + threadIdx.x) % blockDim.x;
+
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1] = cos(B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1]);
+}
+
+
+// cosh
+template<typename NumericT>
+__global__ void matrix_col_element_cosh_kernel(
+ NumericT * A,
+ unsigned int A_start1, unsigned int A_start2,
+ unsigned int A_inc1, unsigned int A_inc2,
+ unsigned int A_size1, unsigned int A_size2,
+ unsigned int A_internal_size1, unsigned int A_internal_size2,
+
+ const NumericT * B,
+ unsigned int B_start1, unsigned int B_start2,
+ unsigned int B_inc1, unsigned int B_inc2,
+ unsigned int B_internal_size1, unsigned int B_internal_size2)
+{
+ unsigned int row_gid = (blockIdx.x * blockDim.x + threadIdx.x) / blockDim.x;
+ unsigned int col_gid = (blockIdx.x * blockDim.x + threadIdx.x) % blockDim.x;
+
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1] = cosh(B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1]);
+}
+
+
+// exp
+template<typename NumericT>
+__global__ void matrix_col_element_exp_kernel(
+ NumericT * A,
+ unsigned int A_start1, unsigned int A_start2,
+ unsigned int A_inc1, unsigned int A_inc2,
+ unsigned int A_size1, unsigned int A_size2,
+ unsigned int A_internal_size1, unsigned int A_internal_size2,
+
+ const NumericT * B,
+ unsigned int B_start1, unsigned int B_start2,
+ unsigned int B_inc1, unsigned int B_inc2,
+ unsigned int B_internal_size1, unsigned int B_internal_size2)
+{
+ unsigned int row_gid = (blockIdx.x * blockDim.x + threadIdx.x) / blockDim.x;
+ unsigned int col_gid = (blockIdx.x * blockDim.x + threadIdx.x) % blockDim.x;
+
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1] = exp(B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1]);
+}
+
+
+// fabs
+template<typename NumericT>
+__global__ void matrix_col_element_fabs_kernel(
+ NumericT * A,
+ unsigned int A_start1, unsigned int A_start2,
+ unsigned int A_inc1, unsigned int A_inc2,
+ unsigned int A_size1, unsigned int A_size2,
+ unsigned int A_internal_size1, unsigned int A_internal_size2,
+
+ const NumericT * B,
+ unsigned int B_start1, unsigned int B_start2,
+ unsigned int B_inc1, unsigned int B_inc2,
+ unsigned int B_internal_size1, unsigned int B_internal_size2)
+{
+ unsigned int row_gid = (blockIdx.x * blockDim.x + threadIdx.x) / blockDim.x;
+ unsigned int col_gid = (blockIdx.x * blockDim.x + threadIdx.x) % blockDim.x;
+
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1] = fabs(B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1]);
+}
+
+
+// floor
+template<typename NumericT>
+__global__ void matrix_col_element_floor_kernel(
+ NumericT * A,
+ unsigned int A_start1, unsigned int A_start2,
+ unsigned int A_inc1, unsigned int A_inc2,
+ unsigned int A_size1, unsigned int A_size2,
+ unsigned int A_internal_size1, unsigned int A_internal_size2,
+
+ const NumericT * B,
+ unsigned int B_start1, unsigned int B_start2,
+ unsigned int B_inc1, unsigned int B_inc2,
+ unsigned int B_internal_size1, unsigned int B_internal_size2)
+{
+ unsigned int row_gid = (blockIdx.x * blockDim.x + threadIdx.x) / blockDim.x;
+ unsigned int col_gid = (blockIdx.x * blockDim.x + threadIdx.x) % blockDim.x;
+
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1] = floor(B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1]);
+}
+
+
+// log
+template<typename NumericT>
+__global__ void matrix_col_element_log_kernel(
+ NumericT * A,
+ unsigned int A_start1, unsigned int A_start2,
+ unsigned int A_inc1, unsigned int A_inc2,
+ unsigned int A_size1, unsigned int A_size2,
+ unsigned int A_internal_size1, unsigned int A_internal_size2,
+
+ const NumericT * B,
+ unsigned int B_start1, unsigned int B_start2,
+ unsigned int B_inc1, unsigned int B_inc2,
+ unsigned int B_internal_size1, unsigned int B_internal_size2)
+{
+ unsigned int row_gid = (blockIdx.x * blockDim.x + threadIdx.x) / blockDim.x;
+ unsigned int col_gid = (blockIdx.x * blockDim.x + threadIdx.x) % blockDim.x;
+
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1] = log(B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1]);
+}
+
+
+// log10
+template<typename NumericT>
+__global__ void matrix_col_element_log10_kernel(
+ NumericT * A,
+ unsigned int A_start1, unsigned int A_start2,
+ unsigned int A_inc1, unsigned int A_inc2,
+ unsigned int A_size1, unsigned int A_size2,
+ unsigned int A_internal_size1, unsigned int A_internal_size2,
+
+ const NumericT * B,
+ unsigned int B_start1, unsigned int B_start2,
+ unsigned int B_inc1, unsigned int B_inc2,
+ unsigned int B_internal_size1, unsigned int B_internal_size2)
+{
+ unsigned int row_gid = (blockIdx.x * blockDim.x + threadIdx.x) / blockDim.x;
+ unsigned int col_gid = (blockIdx.x * blockDim.x + threadIdx.x) % blockDim.x;
+
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1] = log10(B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1]);
+}
+
+
+// sin
+template<typename NumericT>
+__global__ void matrix_col_element_sin_kernel(
+ NumericT * A,
+ unsigned int A_start1, unsigned int A_start2,
+ unsigned int A_inc1, unsigned int A_inc2,
+ unsigned int A_size1, unsigned int A_size2,
+ unsigned int A_internal_size1, unsigned int A_internal_size2,
+
+ const NumericT * B,
+ unsigned int B_start1, unsigned int B_start2,
+ unsigned int B_inc1, unsigned int B_inc2,
+ unsigned int B_internal_size1, unsigned int B_internal_size2)
+{
+ unsigned int row_gid = (blockIdx.x * blockDim.x + threadIdx.x) / blockDim.x;
+ unsigned int col_gid = (blockIdx.x * blockDim.x + threadIdx.x) % blockDim.x;
+
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1] = sin(B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1]);
+}
+
+
+// sinh
+template<typename NumericT>
+__global__ void matrix_col_element_sinh_kernel(
+ NumericT * A,
+ unsigned int A_start1, unsigned int A_start2,
+ unsigned int A_inc1, unsigned int A_inc2,
+ unsigned int A_size1, unsigned int A_size2,
+ unsigned int A_internal_size1, unsigned int A_internal_size2,
+
+ const NumericT * B,
+ unsigned int B_start1, unsigned int B_start2,
+ unsigned int B_inc1, unsigned int B_inc2,
+ unsigned int B_internal_size1, unsigned int B_internal_size2)
+{
+ unsigned int row_gid = (blockIdx.x * blockDim.x + threadIdx.x) / blockDim.x;
+ unsigned int col_gid = (blockIdx.x * blockDim.x + threadIdx.x) % blockDim.x;
+
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1] = sinh(B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1]);
+}
+
+
+// sqrt
+template<typename NumericT>
+__global__ void matrix_col_element_sqrt_kernel(
+ NumericT * A,
+ unsigned int A_start1, unsigned int A_start2,
+ unsigned int A_inc1, unsigned int A_inc2,
+ unsigned int A_size1, unsigned int A_size2,
+ unsigned int A_internal_size1, unsigned int A_internal_size2,
+
+ const NumericT * B,
+ unsigned int B_start1, unsigned int B_start2,
+ unsigned int B_inc1, unsigned int B_inc2,
+ unsigned int B_internal_size1, unsigned int B_internal_size2)
+{
+ unsigned int row_gid = (blockIdx.x * blockDim.x + threadIdx.x) / blockDim.x;
+ unsigned int col_gid = (blockIdx.x * blockDim.x + threadIdx.x) % blockDim.x;
+
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1] = sqrt(B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1]);
+}
+
+
+// tan
+template<typename NumericT>
+__global__ void matrix_col_element_tan_kernel(
+ NumericT * A,
+ unsigned int A_start1, unsigned int A_start2,
+ unsigned int A_inc1, unsigned int A_inc2,
+ unsigned int A_size1, unsigned int A_size2,
+ unsigned int A_internal_size1, unsigned int A_internal_size2,
+
+ const NumericT * B,
+ unsigned int B_start1, unsigned int B_start2,
+ unsigned int B_inc1, unsigned int B_inc2,
+ unsigned int B_internal_size1, unsigned int B_internal_size2)
+{
+ unsigned int row_gid = (blockIdx.x * blockDim.x + threadIdx.x) / blockDim.x;
+ unsigned int col_gid = (blockIdx.x * blockDim.x + threadIdx.x) % blockDim.x;
+
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1] = tan(B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1]);
+}
+
+
+// tanh
+template<typename NumericT>
+__global__ void matrix_col_element_tanh_kernel(
+ NumericT * A,
+ unsigned int A_start1, unsigned int A_start2,
+ unsigned int A_inc1, unsigned int A_inc2,
+ unsigned int A_size1, unsigned int A_size2,
+ unsigned int A_internal_size1, unsigned int A_internal_size2,
+
+ const NumericT * B,
+ unsigned int B_start1, unsigned int B_start2,
+ unsigned int B_inc1, unsigned int B_inc2,
+ unsigned int B_internal_size1, unsigned int B_internal_size2)
+{
+ unsigned int row_gid = (blockIdx.x * blockDim.x + threadIdx.x) / blockDim.x;
+ unsigned int col_gid = (blockIdx.x * blockDim.x + threadIdx.x) % blockDim.x;
+
+ for (unsigned int col = col_gid; col < A_size2; col += gridDim.x)
+ for (unsigned int row = row_gid; row < A_size1; row += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1] = tanh(B[(row * B_inc1 + B_start1) + (col * B_inc2 + B_start2) * B_internal_size1]);
+}
+
+
+
+//
+// matrix-vector product
+//
+
+template<typename NumericT>
+__global__ void vec_mul_col_kernel(
+ const NumericT * A,
+ unsigned int A_row_start,
+ unsigned int A_col_start,
+ unsigned int A_row_inc,
+ unsigned int A_col_inc,
+ unsigned int A_row_size,
+ unsigned int A_col_size,
+ unsigned int A_internal_rows,
+ unsigned int A_internal_cols,
+ const NumericT * v,
+ unsigned int v_start,
+ unsigned int v_inc,
+ unsigned int v_size,
+ NumericT * result,
+ unsigned int result_start,
+ unsigned int result_inc,
+ unsigned int result_size)
+{
+
+ for (unsigned int row = blockIdx.x * blockDim.x + threadIdx.x; row < A_row_size; row += gridDim.x * blockDim.x)
+ {
+ NumericT dot_prod = 0;
+ for (unsigned int col = 0; col < A_col_size; ++col)
+ dot_prod += A[(row * A_row_inc + A_row_start) + (col * A_col_inc + A_col_start) * A_internal_rows] * v[v_start + v_inc * col];
+ result[row * result_inc + result_start] = dot_prod;
+ }
+}
+
+
+template<typename NumericT>
+__global__ void trans_vec_mul_col_kernel(
+ const NumericT * A,
+ unsigned int A_row_start,
+ unsigned int A_col_start,
+ unsigned int A_row_inc,
+ unsigned int A_col_inc,
+ unsigned int A_row_size,
+ unsigned int A_col_size,
+ unsigned int A_internal_rows,
+ unsigned int A_internal_cols,
+ const NumericT * v,
+ unsigned int v_start,
+ unsigned int v_inc,
+ unsigned int v_size,
+ NumericT * result,
+ unsigned int result_start,
+ unsigned int result_inc,
+ unsigned int result_size)
+{
+ __shared__ NumericT work[128];
+
+ unsigned int row_gid = (blockIdx.x * blockDim.x + threadIdx.x) / blockDim.x;
+ unsigned int col_gid = (blockIdx.x * blockDim.x + threadIdx.x) % blockDim.x;
+ unsigned int lid = threadIdx.x;
+
+ for (unsigned int row = row_gid; row < A_col_size; row += gridDim.x)
+ {
+ NumericT dot_prod = 0;
+ for (unsigned int col = col_gid; col < A_row_size; col += blockDim.x)
+ dot_prod += A[(row * A_col_inc + A_col_start) * A_internal_rows + col * A_row_inc + A_row_start] * v[v_start + v_inc * col];
+ work[lid] = dot_prod;
+
+ for (unsigned int stride = blockDim.x/2; stride>0; stride>>=1){
+ __syncthreads();
+ if (lid < stride)
+ work[lid] += work[lid+stride];
+ }
+
+ if (lid == 0)
+ result[row * result_inc + result_start] = work[0];
+ }
+}
+
+
+//
+// matrix-matrix products
+//
+
+
+
+
+//
+// scaled rank-1-update
+//
+
+// alpha on CPU
+template<typename NumericT>
+__global__ void scaled_rank1_update_col_kernel(
+ NumericT * A,
+ unsigned int A_start1, unsigned int A_start2,
+ unsigned int A_inc1, unsigned int A_inc2,
+ unsigned int A_size1, unsigned int A_size2,
+ unsigned int A_internal_size1, unsigned int A_internal_size2,
+
+ NumericT val,
+ unsigned int options2,
+
+ const NumericT * vec1,
+ unsigned int start1,
+ unsigned int inc1,
+ unsigned int size1,
+
+ const NumericT * vec2,
+ unsigned int start2,
+ unsigned int inc2,
+ unsigned int size2)
+{
+ NumericT alpha = val;
+ if (options2 & (1 << 0))
+ alpha = -alpha;
+ if (options2 & (1 << 1))
+ alpha = NumericT(1) / alpha;
+
+ unsigned int row_gid = (blockIdx.x * blockDim.x + threadIdx.x) / blockDim.x;
+ unsigned int col_gid = (blockIdx.x * blockDim.x + threadIdx.x) % blockDim.x;
+
+ for (unsigned int row = row_gid; row < A_size1; row += gridDim.x)
+ {
+ NumericT tmp = alpha * vec1[row * inc1 + start1];
+ for (unsigned int col = col_gid; col < A_size2; col += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1] += tmp * vec2[col * inc2 + start2];
+ }
+}
+
+
+// alpha on GPU
+template<typename NumericT>
+__global__ void scaled_rank1_update_col_kernel(
+ NumericT * A,
+ unsigned int A_start1, unsigned int A_start2,
+ unsigned int A_inc1, unsigned int A_inc2,
+ unsigned int A_size1, unsigned int A_size2,
+ unsigned int A_internal_size1, unsigned int A_internal_size2,
+
+ const NumericT * val,
+ unsigned int options2,
+
+ const NumericT * vec1,
+ unsigned int start1,
+ unsigned int inc1,
+ unsigned int size1,
+
+ const NumericT * vec2,
+ unsigned int start2,
+ unsigned int inc2,
+ unsigned int size2)
+{
+ NumericT alpha = *val;
+ if (options2 & (1 << 0))
+ alpha = -alpha;
+ if (options2 & (1 << 1))
+ alpha = NumericT(1) / alpha;
+
+ unsigned int row_gid = (blockIdx.x * blockDim.x + threadIdx.x) / blockDim.x;
+ unsigned int col_gid = (blockIdx.x * blockDim.x + threadIdx.x) % blockDim.x;
+
+ for (unsigned int row = row_gid; row < A_size1; row += gridDim.x)
+ {
+ NumericT tmp = alpha * vec1[row * inc1 + start1];
+ for (unsigned int col = col_gid; col < A_size2; col += blockDim.x)
+ A[(row * A_inc1 + A_start1) + (col * A_inc2 + A_start2) * A_internal_size1] += tmp * vec2[col * inc2 + start2];
+ }
+}
+
+
+template <typename T>
+__global__ void bidiag_pack_row_major_kernel(
+ T * A,
+ T * D,
+ T * S,
+ unsigned int size1,
+ unsigned int size2,
+ unsigned int stride)
+{
+ unsigned int size = min(size1, size2);
+ if(blockIdx.x * blockDim.x + threadIdx.x == 0)
+ S[0] = 0;
+
+ for(unsigned int i = blockIdx.x * blockDim.x + threadIdx.x;
+ i < size;
+ i += gridDim.x * blockDim.x)
+ {
+ D[i] = A[i*stride + i];
+ S[i+1] = (i + 1 < size2) ? A[i*stride + (i + 1)] : 0;
+ }
+}
+
+template <typename T>
+__global__ void bidiag_pack_column_major_kernel(
+ T * A,
+ T * D,
+ T * S,
+ unsigned int size1,
+ unsigned int size2,
+ unsigned int stride)
+{
+ unsigned int size = min(size1, size2);
+ if(blockIdx.x * blockDim.x + threadIdx.x == 0)
+ S[0] = 0;
+
+ for(unsigned int i = blockIdx.x * blockDim.x + threadIdx.x;
+ i < size;
+ i += gridDim.x * blockDim.x)
+ {
+ D[i] = A[i*stride + i];
+ S[i+1] = (i + 1 < size2) ? A[i + (i + 1) * stride] : 0;
+ }
+}
+
+
+
+template<typename T>
+__global__ void copy_col_row_major_kernel(
+ T * A,
+ T * V,
+ unsigned int row_start,
+ unsigned int col_start,
+ unsigned int size,
+ unsigned int stride)
+{
+ unsigned int x = blockIdx.x * blockDim.x + threadIdx.x;
+ unsigned int sz = gridDim.x * blockDim.x;
+
+ for(unsigned int i = row_start + x; i < size; i += sz)
+ {
+ V[i - row_start] = A[i * stride + col_start];
+ }
+}
+
+template<typename T>
+__global__ void copy_col_column_major_kernel(
+ T * A,
+ T * V,
+ unsigned int row_start,
+ unsigned int col_start,
+ unsigned int size,
+ unsigned int stride)
+{
+ unsigned int x = blockIdx.x * blockDim.x + threadIdx.x;
+ unsigned int sz = gridDim.x * blockDim.x;
+
+ for(unsigned int i = row_start + x; i < size; i += sz)
+ {
+ V[i - row_start] = A[i + col_start * stride];
+ }
+}
+
+template<typename T>
+__global__ void copy_row_row_major_kernel(
+ T * A,
+ T * V,
+ unsigned int row_start,
+ unsigned int col_start,
+ unsigned int size,
+ unsigned int stride)
+{
+ unsigned int x = blockIdx.x * blockDim.x + threadIdx.x;
+ unsigned int sz = gridDim.x * blockDim.x;
+
+ for(unsigned int i = col_start + x; i < size; i += sz)
+ {
+ V[i - col_start] = A[row_start * stride + i];
+ }
+
+}
+
+template<typename T>
+__global__ void copy_row_column_major_kernel(
+ T * A,
+ T * V,
+ unsigned int row_start,
+ unsigned int col_start,
+ unsigned int size,
+ unsigned int stride)
+{
+ unsigned int x = blockIdx.x * blockDim.x + threadIdx.x;
+ unsigned int sz = gridDim.x * blockDim.x;
+
+ for(unsigned int i = col_start + x; i < size; i += sz)
+ {
+ V[i - col_start] = A[row_start + i * stride];
+ }
+
+}
+
+
+
+template<typename T>
+__global__ void house_update_A_left_row_major_kernel(
+ T * A,
+ T * V, //householder vector
+ unsigned int row_start,
+ unsigned int col_start,
+ unsigned int size1,
+ unsigned int size2,
+ unsigned int stride)
+{
+ T ss = 0;
+
+ for(unsigned int i = blockIdx.x * blockDim.x + threadIdx.x + col_start;
+ i < size2;
+ i += gridDim.x * blockDim.x)
+ {
+ ss = 0;
+ for(unsigned int j = row_start; j < size1; j++)
+ ss = ss +(V[j] * A[j * stride + i]);
+
+ for(unsigned int j = row_start; j < size1; j++)
+ A[j * stride + i] = A[j * stride + i] - (2 * V[j] * ss);
+ }
+}
+
+template<typename T>
+__global__ void house_update_A_left_column_major_kernel(
+ T * A,
+ T * V, //householder vector
+ unsigned int row_start,
+ unsigned int col_start,
+ unsigned int size1,
+ unsigned int size2,
+ unsigned int stride)
+{
+ T ss = 0;
+
+ for(unsigned int i = blockIdx.x * blockDim.x + threadIdx.x + col_start;
+ i < size2;
+ i += gridDim.x * blockDim.x)
+ {
+ ss = 0;
+ for(unsigned int j = row_start; j < size1; j++)
+ ss = ss +(V[j] * A[j + i * stride]);
+
+ for(unsigned int j = row_start; j < size1; j++)
+ A[j + i * stride] = A[j + i * stride] - (2 * V[j] * ss);
+ }
+}
+
+
+
+template<typename T>
+__global__ void house_update_A_right_row_major_kernel(
+ T * A,
+ T * V, //householder vector
+ unsigned int row_start,
+ unsigned int col_start,
+ unsigned int size1,
+ unsigned int size2,
+ unsigned int stride)
+{
+ __shared__ T sums[128];
+ T ss = 0;
+
+ for(unsigned int i = blockIdx.x + row_start; i < size1; i+= gridDim.x)
+ {
+ ss = 0;
+ for(unsigned int j = threadIdx.x; j < size2; j+= blockDim.x)
+ ss = ss + (V[j] * A[i * stride + j]);
+ sums[threadIdx.x] = ss;
+
+ __syncthreads();
+ col_reduce_lcl_array(sums, threadIdx.x, blockDim.x);
+ __syncthreads();
+
+ T sum_Av = sums[0];
+
+ for(unsigned int j = threadIdx.x; j < size2; j+= blockDim.x)
+ A[i * stride + j] = A[i * stride + j] - (2 * V[j] * sum_Av);
+ }
+}
+
+template<typename T>
+__global__ void house_update_A_right_column_major_kernel(
+ T * A,
+ T * V, //householder vector
+ unsigned int row_start,
+ unsigned int col_start,
+ unsigned int size1,
+ unsigned int size2,
+ unsigned int stride)
+{
+ __shared__ T sums[128];
+ T ss = 0;
+
+ for(unsigned int i = blockIdx.x + row_start; i < size1; i+= gridDim.x)
+ {
+ ss = 0;
+ for(unsigned int j = threadIdx.x; j < size2; j+= blockDim.x)
+ ss = ss + (V[j] * A[i + j * stride]);
+ sums[threadIdx.x] = ss;
+
+ __syncthreads();
+ col_reduce_lcl_array(sums, threadIdx.x, blockDim.x);
+ __syncthreads();
+
+ T sum_Av = sums[0];
+
+ for(unsigned int j = threadIdx.x; j < size2; j+= blockDim.x)
+ A[i + j * stride] = A[i + j * stride] - (2 * V[j] * sum_Av);
+ }
+}
+
+
+
+template<typename T>
+__device__ void col_reduce_lcl_array(
+ T * sums,
+ unsigned int th_Idx,
+ unsigned int bl_Dim)
+{
+ unsigned int step = bl_Dim >> 1;
+
+ while(step > 0)
+ {
+ if(th_Idx < step)
+ sums[th_Idx] += sums[th_Idx + step];
+ step >>= 1;
+ __syncthreads();
+ }
+}
+
+
+template <typename T>
+__global__ void house_update_QL_row_major_kernel(
+ T * QL,
+ T * V,
+ unsigned int size1,
+ unsigned int strideQ)
+{
+ __shared__ T sums[128];
+ T ss = 0;
+ for(unsigned int i = blockIdx.x; i < size1; i += gridDim.x)
+ {
+ ss = 0;
+ for(unsigned int j = threadIdx.x; j < size1; j += blockDim.x)
+ ss = ss + (V[j] * QL[i * strideQ + j]);
+ sums[threadIdx.x] = ss;
+
+ __syncthreads();
+ col_reduce_lcl_array(sums, threadIdx.x, blockDim.x);
+ __syncthreads();
+
+ T sum_Qv = sums[0];
+
+ for(unsigned int j = threadIdx.x; j < size1; j += blockDim.x)
+ QL[i * strideQ + j] = QL[i * strideQ + j] - (2 * V[j] * sum_Qv);
+ }
+}
+
+template <typename T>
+__global__ void house_update_QL_column_major_kernel(
+ T * QL,
+ T * V,
+ unsigned int size1,
+ unsigned int strideQ)
+{
+ __shared__ T sums[128];
+ T ss = 0;
+ for(unsigned int i = blockIdx.x; i < size1; i += gridDim.x)
+ {
+ ss = 0;
+ for(unsigned int j = threadIdx.x; j < size1; j += blockDim.x)
+ ss = ss + (V[j] * QL[i + j * strideQ]);
+ sums[threadIdx.x] = ss;
+
+ __syncthreads();
+ col_reduce_lcl_array(sums, threadIdx.x, blockDim.x);
+ __syncthreads();
+
+ T sum_Qv = sums[0];
+
+ for(unsigned int j = threadIdx.x; j < size1; j += blockDim.x)
+ QL[i + j * strideQ] = QL[i + j * strideQ] - (2 * V[j] * sum_Qv);
+ }
+}
+
+
+template <typename T>
+__global__ void givens_next_row_major_kernel(
+ T * matr,
+ T * cs,
+ T * ss,
+ unsigned int size,
+ unsigned int stride,
+ unsigned int start_i,
+ unsigned int end_i)
+{
+ unsigned int j = blockIdx.x * blockDim.x + threadIdx.x;
+ __shared__ T cs_lcl[256];
+ __shared__ T ss_lcl[256];
+
+ T x = (j < size) ? matr[(end_i + 1) + j * stride] : 0;
+
+ unsigned int elems_num = end_i - start_i + 1;
+ unsigned int block_num = (elems_num + blockDim.x - 1) / blockDim.x;
+
+ for(unsigned int block_id = 0; block_id < block_num; block_id++)
+ {
+ unsigned int to = min(elems_num - block_id * blockDim.x, blockDim.x);
+
+ if(threadIdx.x < to)
+ {
+ cs_lcl[threadIdx.x] = cs[end_i - (threadIdx.x + block_id * blockDim.x)];
+ ss_lcl[threadIdx.x] = ss[end_i - (threadIdx.x + block_id * blockDim.x)];
+ }
+ __syncthreads();
+ if(j < size)
+ {
+ for(unsigned int ind = 0; ind < to; ind++)
+ {
+ unsigned int i = end_i - (ind + block_id * blockDim.x);
+ T z = matr[i + j * stride];
+ T cs_val = cs_lcl[ind];
+ T ss_val = ss_lcl[ind];
+ matr[(i + 1) + j * stride] = x * cs_val + z * ss_val;
+ x = -x * ss_val + z * cs_val;
+ }
+ }
+ __syncthreads();
+ }
+ if(j < size)
+ matr[(start_i) + j * stride] = x;
+}
+
+template <typename T>
+__global__ void givens_next_column_major_kernel(
+ T * matr,
+ T * cs,
+ T * ss,
+ unsigned int size,
+ unsigned int stride,
+ unsigned int start_i,
+ unsigned int end_i)
+{
+ unsigned int j = blockIdx.x * blockDim.x + threadIdx.x;
+ __shared__ T cs_lcl[256];
+ __shared__ T ss_lcl[256];
+
+ T x = (j < size) ? matr[(end_i + 1) *stride + j] : 0;
+
+ unsigned int elems_num = end_i - start_i + 1;
+ unsigned int block_num = (elems_num + blockDim.x - 1) / blockDim.x;
+
+ for(unsigned int block_id = 0; block_id < block_num; block_id++)
+ {
+ unsigned int to = min(elems_num - block_id * blockDim.x, blockDim.x);
+
+ if(threadIdx.x < to)
+ {
+ cs_lcl[threadIdx.x] = cs[end_i - (threadIdx.x + block_id * blockDim.x)];
+ ss_lcl[threadIdx.x] = ss[end_i - (threadIdx.x + block_id * blockDim.x)];
+ }
+ __syncthreads();
+ if(j < size)
+ {
+ for(unsigned int ind = 0; ind < to; ind++)
+ {
+ unsigned int i = end_i - (ind + block_id * blockDim.x);
+ T z = matr[i *stride + j];
+ T cs_val = cs_lcl[ind];
+ T ss_val = ss_lcl[ind];
+ matr[(i + 1) * stride + j] = x * cs_val + z * ss_val;
+ x = -x * ss_val + z * cs_val;
+ }
+ }
+ __syncthreads();
+ }
+ if(j < size)
+ matr[(start_i) * stride + j] = x;
+}
+
+
+
+
+
+} // namespace cuda
+} //namespace linalg
+} //namespace viennacl
+
+
+#endif