/*#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
#include <stdlib.h>

#define TILE_SIZE 4
#define INPUT_SIZE 12
#define MASK_WIDTH 5
__constant__ float M[MASK_WIDTH];

__global__ void convolution_shared_memory(float* N, float* P) {

	int i = blockIdx.x * blockDim.x + threadIdx.x;

	__shared__ float N_s[TILE_SIZE];

	N_s[threadIdx.x] = N[i];

	__syncthreads();

	int this_title_start_point = blockIdx.x * blockDim.x;
	int next_tile_start_point = (blockIdx.x + 1) * blockDim.x;
	int n_start_point = i - (MASK_WIDTH / 2);
	float Pvalue = 0;

	for (int j = 0; j < MASK_WIDTH; j++) {

		int N_index = n_start_point + j;

		if (N_index >= 0 && N_index < INPUT_SIZE) {
			if ((N_index >= this_title_start_point) && (N_index < next_tile_start_point)) {
				Pvalue += N_s[threadIdx.x + j - (MASK_WIDTH / 2)] * M[j];
			}
			else {
				Pvalue += N[N_index] * M[j];
			}
		}
	}

	P[i] = Pvalue;
}


__global__ void convolution_constant_memory(float* N, float* P, int Width) {

	int i = blockIdx.x * blockDim.x + threadIdx.x;

	float Pvalue = 0;

	int n_start_point = i - (MASK_WIDTH / 2);

	for (int j = 0; j < MASK_WIDTH; j++) {
		if (n_start_point + j >= 0 && n_start_point + j < Width) {
			Pvalue += N[n_start_point + j] * M[j];
		}
	}

	P[i] = Pvalue;
}

__global__ void convolution_global_memory(float* N, float* M, float* P, int Width) {


	int i = blockIdx.x * blockDim.x + threadIdx.x;

	float Pvalue = 0;

	int n_start_point = i - (MASK_WIDTH / 2);

	for (int j = 0; j < MASK_WIDTH; j++) {
		if (n_start_point + j >= 0 && n_start_point + j < Width) {
			Pvalue += N[n_start_point + j] * M[j];
		}
	}

	P[i] = Pvalue;
}

int mafghjkin() {

	//device input and output
	float* d_N = 0;
	float* d_P = 0;

	cudaMalloc(&d_N, INPUT_SIZE * sizeof(float));
	cudaMalloc(&d_P, INPUT_SIZE * sizeof(float));

	//host input and output
	float* h_N = (float*)malloc(INPUT_SIZE * sizeof(float));
	float* h_P = (float*)malloc(INPUT_SIZE * sizeof(float));
	float* h_M = (float*)malloc(MASK_WIDTH * sizeof(float));

	//initialize input on host
	for (int i = 0; i < INPUT_SIZE; ++i) {
		h_N[i] = (float)i;
	}

	//transfer input to device
	cudaMemcpy(d_N, h_N, INPUT_SIZE * sizeof(float), cudaMemcpyHostToDevice);
	cudaMemcpy(d_P, h_P, INPUT_SIZE * sizeof(float), cudaMemcpyHostToDevice);

	//initialize mask on host
	for (int j = 0; j < MASK_WIDTH; ++j) {
		h_M[j] = (float)j;
	}

	//transfer mask to constant memory
	cudaMemcpyToSymbol(M, h_M, MASK_WIDTH * sizeof(float));


	//call convolution kernel
	convolution_shared_memory << <(INPUT_SIZE + TILE_SIZE - 1) / TILE_SIZE, TILE_SIZE >> > (d_N, d_P);

	//retrieve result from device
	cudaMemcpy(h_P, d_P, INPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost);

	for (int i = 0; i < INPUT_SIZE; ++i) {
		printf("%f\n", h_P[i]);
	}

	cudaFree(d_N);
	cudaFree(d_P);
	cudaFree(M);

	free(h_N);
	free(h_P);
	free(h_M);
}*/
	.file	"tmpxft_002679e7_00000000-6_kernel.cudafe1.cpp"
	.text
#APP

#NO_APP
	.type	_ZL26__cudaUnregisterBinaryUtilv, @function
_ZL26__cudaUnregisterBinaryUtilv:
.LFB2010:
	.cfi_startproc
	endbr64
	movq	_ZL20__cudaFatCubinHandle(%rip), %rdi
	jmp	__cudaUnregisterFatBinary@PLT
	.cfi_endproc
.LFE2010:
	.size	_ZL26__cudaUnregisterBinaryUtilv, .-_ZL26__cudaUnregisterBinaryUtilv
	.section	.text.startup,"ax",@progbits
	.type	_ZL24__sti____cudaRegisterAllv, @function
_ZL24__sti____cudaRegisterAllv:
.LFB2033:
	.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
.LFE2033:
	.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>
/*#include "cuda_runtime.h"
#include "device_launch_parameters.h"
#include <stdio.h>
#include <stdlib.h>

#define TILE_SIZE 4
#define INPUT_SIZE 12
#define MASK_WIDTH 5
__constant__ float M[MASK_WIDTH];

__global__ void convolution_shared_memory(float* N, float* P) {

	int i = blockIdx.x * blockDim.x + threadIdx.x;

	__shared__ float N_s[TILE_SIZE];

	N_s[threadIdx.x] = N[i];

	__syncthreads();

	int this_title_start_point = blockIdx.x * blockDim.x;
	int next_tile_start_point = (blockIdx.x + 1) * blockDim.x;
	int n_start_point = i - (MASK_WIDTH / 2);
	float Pvalue = 0;

	for (int j = 0; j < MASK_WIDTH; j++) {

		int N_index = n_start_point + j;

		if (N_index >= 0 && N_index < INPUT_SIZE) {
			if ((N_index >= this_title_start_point) && (N_index < next_tile_start_point)) {
				Pvalue += N_s[threadIdx.x + j - (MASK_WIDTH / 2)] * M[j];
			}
			else {
				Pvalue += N[N_index] * M[j];
			}
		}
	}

	P[i] = Pvalue;
}


__global__ void convolution_constant_memory(float* N, float* P, int Width) {

	int i = blockIdx.x * blockDim.x + threadIdx.x;

	float Pvalue = 0;

	int n_start_point = i - (MASK_WIDTH / 2);

	for (int j = 0; j < MASK_WIDTH; j++) {
		if (n_start_point + j >= 0 && n_start_point + j < Width) {
			Pvalue += N[n_start_point + j] * M[j];
		}
	}

	P[i] = Pvalue;
}

__global__ void convolution_global_memory(float* N, float* M, float* P, int Width) {


	int i = blockIdx.x * blockDim.x + threadIdx.x;

	float Pvalue = 0;

	int n_start_point = i - (MASK_WIDTH / 2);

	for (int j = 0; j < MASK_WIDTH; j++) {
		if (n_start_point + j >= 0 && n_start_point + j < Width) {
			Pvalue += N[n_start_point + j] * M[j];
		}
	}

	P[i] = Pvalue;
}

int mafghjkin() {

	//device input and output
	float* d_N = 0;
	float* d_P = 0;

	cudaMalloc(&d_N, INPUT_SIZE * sizeof(float));
	cudaMalloc(&d_P, INPUT_SIZE * sizeof(float));

	//host input and output
	float* h_N = (float*)malloc(INPUT_SIZE * sizeof(float));
	float* h_P = (float*)malloc(INPUT_SIZE * sizeof(float));
	float* h_M = (float*)malloc(MASK_WIDTH * sizeof(float));

	//initialize input on host
	for (int i = 0; i < INPUT_SIZE; ++i) {
		h_N[i] = (float)i;
	}

	//transfer input to device
	cudaMemcpy(d_N, h_N, INPUT_SIZE * sizeof(float), cudaMemcpyHostToDevice);
	cudaMemcpy(d_P, h_P, INPUT_SIZE * sizeof(float), cudaMemcpyHostToDevice);

	//initialize mask on host
	for (int j = 0; j < MASK_WIDTH; ++j) {
		h_M[j] = (float)j;
	}

	//transfer mask to constant memory
	cudaMemcpyToSymbol(M, h_M, MASK_WIDTH * sizeof(float));


	//call convolution kernel
	convolution_shared_memory << <(INPUT_SIZE + TILE_SIZE - 1) / TILE_SIZE, TILE_SIZE >> > (d_N, d_P);

	//retrieve result from device
	cudaMemcpy(h_P, d_P, INPUT_SIZE * sizeof(float), cudaMemcpyDeviceToHost);

	for (int i = 0; i < INPUT_SIZE; ++i) {
		printf("%f\n", h_P[i]);
	}

	cudaFree(d_N);
	cudaFree(d_P);
	cudaFree(M);

	free(h_N);
	free(h_P);
	free(h_M);
}*/
	.text
	.file	"kernel.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
