#define TILE_DIM 32

template<typename T, typename R>
__device__ void common_std(const T* matrix, R* result,
                           const int numRows, const int numColumns) {

  __shared__ R sumTile[TILE_DIM][TILE_DIM];
  __shared__ R squareSumTile[TILE_DIM][TILE_DIM];

  int tx = threadIdx.x;
  int ty = threadIdx.y;
  sumTile[ty][tx] = 0;
  squareSumTile[ty][tx] = 0;

  #pragma unroll
  for (int tr = 0; tr < (numRows - 1) / TILE_DIM + 1; tr++) {
    for (int tc = 0; tc < (numColumns - 1) / TILE_DIM + 1; tc++) {
      int r = tr * TILE_DIM + ty;
      int c = tc * TILE_DIM + tx;
      if (r < numRows && c < numColumns) {
        T value = matrix[r * numColumns + c];
        sumTile[ty][tx] += value;
        squareSumTile[ty][tx] += value * value;
      }
      __syncthreads();
    }
  }

  if (tx == 0 && ty == 0) {
    R sum = 0;
    R squareSum = 0;
    #pragma unroll
    for (int i = 0; i < TILE_DIM; i++) {
      #pragma unroll
      for (int j = 0; j < TILE_DIM; j++) {
        sum += sumTile[i][j];
        squareSum += squareSumTile[i][j];
      }
    }
    int length = numRows * numColumns;
    result[0] = sqrt((squareSum - (sum * sum) / length) / length);
  }
}

template<typename T>
__device__ void math_std(const T* matrix, float* result,
                         const int numRows, const int numColumns) {
  common_std<T, float>(matrix, result, numRows, numColumns);
}

template<typename T>
__device__ void math_stdd(const T* matrix, double* result,
                     const int numRows, const int numColumns) {
  common_std<T, double>(matrix, result, numRows, numColumns);
}

template<typename T, typename R>
__device__ void common_rowsStd(const T* matrix, R* result,
                               const int numRows, const int numColumns) {

  __shared__ T tile[TILE_DIM][TILE_DIM];

  int by = blockIdx.y;
  int ty = threadIdx.y;
  int row = by * blockDim.y + ty;
  R sum = 0;
  R squareSum = 0;

  #pragma unroll
  for (int t = 0; t < (numColumns - 1) / TILE_DIM + 1; t++) {
    #pragma unroll
    for (int i = 0; i < TILE_DIM; i++) {
      int r = by * TILE_DIM + i;
      int c = t * TILE_DIM + ty;
      if (r < numRows && c < numColumns) {
        tile[i][ty] = matrix[r * numColumns + c];
      } else {
        tile[i][ty] = 0;
      }
    }
    __syncthreads();

    #pragma unroll
    for (int j = 0; j < TILE_DIM; j++) {
      T value = tile[ty][j];
      sum += value;
      squareSum += value * value;
    }
    __syncthreads();
  }

  if (row < numRows) {
    result[row] = sqrt((squareSum - (sum * sum) / numColumns) / numColumns);
  }
}

template<typename T>
__device__ void rowsStd(const T* matrix, float* result,
                        const int numRows, const int numColumns) {
  common_rowsStd<T, float>(matrix, result, numRows, numColumns);
}

template<typename T>
__device__ void rowsStdd(const T* matrix, double* result,
                         const int numRows, const int numColumns) {
  common_rowsStd<T, double>(matrix, result, numRows, numColumns);
}

template<typename T, typename R>
__device__ void common_columnsStd(const T* matrix, R* result,
                                  const int numRows, const int numColumns) {

  int bx = blockIdx.x;
  int tx = threadIdx.x;
  int col = bx * blockDim.x + tx;
  if (col < numColumns) {
    R sum = 0;
    R squareSum = 0;
    #pragma unroll
    for (int i = 0; i < numRows; i++) {
      int index = i * numColumns + col;
      T value = matrix[index];
      sum += value;
      squareSum += value * value;
    }
    result[col] = sqrt((squareSum - (sum * sum) / numRows) / numRows);
  }
}

template<typename T>
__device__ void columnsStd(const T* matrix, float* result,
                           const int numRows, const int numColumns) {
  common_columnsStd<T, float>(matrix, result, numRows, numColumns);
}

template<typename T>
__device__ void columnsStdd(const T* matrix, double* result,
                            const int numRows, const int numColumns) {
  common_columnsStd<T, double>(matrix, result, numRows, numColumns);
}

	.file	"tmpxft_002db397_00000000-6_Std.cudafe1.cpp"
	.text
#APP

#NO_APP
	.type	_ZL26__cudaUnregisterBinaryUtilv, @function
_ZL26__cudaUnregisterBinaryUtilv:
.LFB2019:
	.cfi_startproc
	endbr64
	movq	_ZL20__cudaFatCubinHandle(%rip), %rdi
	jmp	__cudaUnregisterFatBinary@PLT
	.cfi_endproc
.LFE2019:
	.size	_ZL26__cudaUnregisterBinaryUtilv, .-_ZL26__cudaUnregisterBinaryUtilv
	.section	.text.startup,"ax",@progbits
	.type	_ZL24__sti____cudaRegisterAllv, @function
_ZL24__sti____cudaRegisterAllv:
.LFB2042:
	.cfi_startproc
	endbr64
	pushq	%rax
	.cfi_def_cfa_offset 16
	leaq	_ZL15__fatDeviceText(%rip), %rdi
	call	__cudaRegisterFatBinary@PLT
	movq	%rax, %rdi
	movq	%rax, _ZL20__cudaFatCubinHandle(%rip)
	call	__cudaRegisterFatBinaryEnd@PLT
	leaq	_ZL26__cudaUnregisterBinaryUtilv(%rip), %rdi
	popq	%rdx
	.cfi_def_cfa_offset 8
	jmp	atexit@PLT
	.cfi_endproc
.LFE2042:
	.size	_ZL24__sti____cudaRegisterAllv, .-_ZL24__sti____cudaRegisterAllv
	.section	.init_array,"aw"
	.align 8
	.quad	_ZL24__sti____cudaRegisterAllv
	.section	.nvFatBinSegment,"aw"
	.align 8
	.type	_ZL15__fatDeviceText, @object
	.size	_ZL15__fatDeviceText, 24
_ZL15__fatDeviceText:
	.long	1180844977
	.long	1
	.quad	fatbinData
	.quad	0
	.local	_ZL20__cudaFatCubinHandle
	.comm	_ZL20__cudaFatCubinHandle,8,8
	.ident	"GCC: (Ubuntu 13.3.0-6ubuntu2~24.04) 13.3.0"
	.section	.note.GNU-stack,"",@progbits
	.section	.note.gnu.property,"a"
	.align 8
	.long	1f - 0f
	.long	4f - 1f
	.long	5
0:
	.string	"GNU"
1:
	.align 8
	.long	0xc0000002
	.long	3f - 2f
2:
	.long	0x3
3:
	.align 8
4:


#include <hip/hip_runtime.h>
#define TILE_DIM 32

template<typename T, typename R>
__device__ void common_std(const T* matrix, R* result,
                           const int numRows, const int numColumns) {

  __shared__ R sumTile[TILE_DIM][TILE_DIM];
  __shared__ R squareSumTile[TILE_DIM][TILE_DIM];

  int tx = threadIdx.x;
  int ty = threadIdx.y;
  sumTile[ty][tx] = 0;
  squareSumTile[ty][tx] = 0;

  #pragma unroll
  for (int tr = 0; tr < (numRows - 1) / TILE_DIM + 1; tr++) {
    for (int tc = 0; tc < (numColumns - 1) / TILE_DIM + 1; tc++) {
      int r = tr * TILE_DIM + ty;
      int c = tc * TILE_DIM + tx;
      if (r < numRows && c < numColumns) {
        T value = matrix[r * numColumns + c];
        sumTile[ty][tx] += value;
        squareSumTile[ty][tx] += value * value;
      }
      __syncthreads();
    }
  }

  if (tx == 0 && ty == 0) {
    R sum = 0;
    R squareSum = 0;
    #pragma unroll
    for (int i = 0; i < TILE_DIM; i++) {
      #pragma unroll
      for (int j = 0; j < TILE_DIM; j++) {
        sum += sumTile[i][j];
        squareSum += squareSumTile[i][j];
      }
    }
    int length = numRows * numColumns;
    result[0] = sqrt((squareSum - (sum * sum) / length) / length);
  }
}

template<typename T>
__device__ void math_std(const T* matrix, float* result,
                         const int numRows, const int numColumns) {
  common_std<T, float>(matrix, result, numRows, numColumns);
}

template<typename T>
__device__ void math_stdd(const T* matrix, double* result,
                     const int numRows, const int numColumns) {
  common_std<T, double>(matrix, result, numRows, numColumns);
}

template<typename T, typename R>
__device__ void common_rowsStd(const T* matrix, R* result,
                               const int numRows, const int numColumns) {

  __shared__ T tile[TILE_DIM][TILE_DIM];

  int by = blockIdx.y;
  int ty = threadIdx.y;
  int row = by * blockDim.y + ty;
  R sum = 0;
  R squareSum = 0;

  #pragma unroll
  for (int t = 0; t < (numColumns - 1) / TILE_DIM + 1; t++) {
    #pragma unroll
    for (int i = 0; i < TILE_DIM; i++) {
      int r = by * TILE_DIM + i;
      int c = t * TILE_DIM + ty;
      if (r < numRows && c < numColumns) {
        tile[i][ty] = matrix[r * numColumns + c];
      } else {
        tile[i][ty] = 0;
      }
    }
    __syncthreads();

    #pragma unroll
    for (int j = 0; j < TILE_DIM; j++) {
      T value = tile[ty][j];
      sum += value;
      squareSum += value * value;
    }
    __syncthreads();
  }

  if (row < numRows) {
    result[row] = sqrt((squareSum - (sum * sum) / numColumns) / numColumns);
  }
}

template<typename T>
__device__ void rowsStd(const T* matrix, float* result,
                        const int numRows, const int numColumns) {
  common_rowsStd<T, float>(matrix, result, numRows, numColumns);
}

template<typename T>
__device__ void rowsStdd(const T* matrix, double* result,
                         const int numRows, const int numColumns) {
  common_rowsStd<T, double>(matrix, result, numRows, numColumns);
}

template<typename T, typename R>
__device__ void common_columnsStd(const T* matrix, R* result,
                                  const int numRows, const int numColumns) {

  int bx = blockIdx.x;
  int tx = threadIdx.x;
  int col = bx * blockDim.x + tx;
  if (col < numColumns) {
    R sum = 0;
    R squareSum = 0;
    #pragma unroll
    for (int i = 0; i < numRows; i++) {
      int index = i * numColumns + col;
      T value = matrix[index];
      sum += value;
      squareSum += value * value;
    }
    result[col] = sqrt((squareSum - (sum * sum) / numRows) / numRows);
  }
}

template<typename T>
__device__ void columnsStd(const T* matrix, float* result,
                           const int numRows, const int numColumns) {
  common_columnsStd<T, float>(matrix, result, numRows, numColumns);
}

template<typename T>
__device__ void columnsStdd(const T* matrix, double* result,
                            const int numRows, const int numColumns) {
  common_columnsStd<T, double>(matrix, result, numRows, numColumns);
}

	.text
	.file	"Std.hip"
	.type	__hip_cuid_,@object             # @__hip_cuid_
	.bss
	.globl	__hip_cuid_
__hip_cuid_:
	.byte	0                               # 0x0
	.size	__hip_cuid_, 1

	.ident	"AMD clang version 18.0.0git (https://github.com/RadeonOpenCompute/llvm-project roc-6.3.2 25012 e5bf7e55c91490b07c49d8960fa7983d864936c4)"
	.section	".note.GNU-stack","",@progbits
	.addrsig
	.addrsig_sym __hip_cuid_

	.text
	.p2alignl 7, 3214868480
	.fill 96, 4, 3214868480
	.type	__hip_cuid_,@object             ; @__hip_cuid_
	.section	.bss,"aw",@nobits
	.globl	__hip_cuid_
__hip_cuid_:
	.byte	0                               ; 0x0
	.size	__hip_cuid_, 1

	.ident	"AMD clang version 18.0.0git (https://github.com/RadeonOpenCompute/llvm-project roc-6.3.2 25012 e5bf7e55c91490b07c49d8960fa7983d864936c4)"
	.section	".note.GNU-stack","",@progbits
	.addrsig
	.addrsig_sym __hip_cuid_
	.amdgcn_target "amdgcn-amd-amdhsa--gfx1100"
	.amdgpu_metadata
---
amdhsa.kernels:  []
amdhsa.target:   amdgcn-amd-amdhsa--gfx1100
amdhsa.version:
  - 1
  - 2
...

	.end_amdgpu_metadata
