/*#include<iostream>
#include<cuda.h>
#include<cuda_runtime.h>
#include "device_launch_parameters.h"
#include <device_functions.h>

#define imin(a,b)((a<b)?a:b)

const int N =33 * 1024;
const int threadsPerBlock = 256;
const int blocksPerGrid = imin(32, (N+ threadsPerBlock-1) / threadsPerBlock);


__global__ void kernel(float*a,float*b,float*c)
{
	__shared__ float cache[threadsPerBlock];//shared between threads of a block. we have 32 blocks in this example thus, we will have 32 varialbes created for cache each of size 256*float bytes. 
	float temp = 0;
	int tid = threadIdx.x + blockIdx.x*blockDim.x;
	int cacheIndex = threadIdx.x;
	while (tid < N)
	{
		temp += a[tid]*b[tid];
		tid += gridDim.x*blockDim.x;
	}

	cache[cacheIndex] = temp;
	__syncthreads();

	int i = blockDim.x / 2;
	while (i!=0)
	{
		if(cacheIndex<i)
			cache[cacheIndex] += cache[cacheIndex + i];
		__syncthreads();
		i /= 2;
	}

	if (cacheIndex == 0)
	{
		c[blockIdx.x] = cache[0];
		//each cache element in all the 32 blocks returning their values at index 0
		printf("blockIdx.x= %d ,threadIdx.x=%d , cache=%f \n", blockIdx.x, threadIdx.x, cache[0]);
	}
}

int main()
{
	
	float* h_a = new float[N];
	float* h_b = new float[N];
	float* h_c = new float[blocksPerGrid];

	float* d_a;
	float* d_b;
	float* d_c;

	cudaMalloc((void**)&d_a, sizeof(float)*N);
	cudaMalloc((void**)&d_b, sizeof(float)*N);
	cudaMalloc((void**)&d_c, sizeof(float)*blocksPerGrid);

	for (int i = 0; i < N; i++)
	{
		h_a[i] = i;
		h_b[i] = 2 * i;
	}

	cudaMemcpy(d_a, h_a, sizeof(float)*N, cudaMemcpyHostToDevice);
	cudaMemcpy(d_b, h_b, sizeof(float)*N, cudaMemcpyHostToDevice);

	kernel << <blocksPerGrid, threadsPerBlock >> >(d_a,d_b,d_c);

	cudaMemcpy(h_c, d_c, sizeof(float)*blocksPerGrid, cudaMemcpyDeviceToHost);
	//Output h_c
	
	//This partial sum calculated on CPU, better than wasting GPU resources
	float c = 0;
	for (int i = 0; i < blocksPerGrid; i++)
	{
		c += h_c[i];
	}

	//CPU sum for verification and compare
	#define sumSquares(x) (x*(x+1)*(2*x+1)/6)
	printf("Does GPU value %.6g == %.6g\n", c,2*sumSquares((float) (N-1)));


	//Delete all memroAllocs
	cudaFree(d_a);
	cudaFree(d_b);
	cudaFree(d_c);
	delete [] h_a;
	delete [] h_b;
	delete[] h_c;
	return 911;
}*/
	.file	"tmpxft_002e421e_00000000-6_dotProduct.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<iostream>
#include<cuda.h>
#include<cuda_runtime.h>
#include "device_launch_parameters.h"
#include <device_functions.h>

#define imin(a,b)((a<b)?a:b)

const int N =33 * 1024;
const int threadsPerBlock = 256;
const int blocksPerGrid = imin(32, (N+ threadsPerBlock-1) / threadsPerBlock);


__global__ void kernel(float*a,float*b,float*c)
{
	__shared__ float cache[threadsPerBlock];//shared between threads of a block. we have 32 blocks in this example thus, we will have 32 varialbes created for cache each of size 256*float bytes. 
	float temp = 0;
	int tid = threadIdx.x + blockIdx.x*blockDim.x;
	int cacheIndex = threadIdx.x;
	while (tid < N)
	{
		temp += a[tid]*b[tid];
		tid += gridDim.x*blockDim.x;
	}

	cache[cacheIndex] = temp;
	__syncthreads();

	int i = blockDim.x / 2;
	while (i!=0)
	{
		if(cacheIndex<i)
			cache[cacheIndex] += cache[cacheIndex + i];
		__syncthreads();
		i /= 2;
	}

	if (cacheIndex == 0)
	{
		c[blockIdx.x] = cache[0];
		//each cache element in all the 32 blocks returning their values at index 0
		printf("blockIdx.x= %d ,threadIdx.x=%d , cache=%f \n", blockIdx.x, threadIdx.x, cache[0]);
	}
}

int main()
{
	
	float* h_a = new float[N];
	float* h_b = new float[N];
	float* h_c = new float[blocksPerGrid];

	float* d_a;
	float* d_b;
	float* d_c;

	cudaMalloc((void**)&d_a, sizeof(float)*N);
	cudaMalloc((void**)&d_b, sizeof(float)*N);
	cudaMalloc((void**)&d_c, sizeof(float)*blocksPerGrid);

	for (int i = 0; i < N; i++)
	{
		h_a[i] = i;
		h_b[i] = 2 * i;
	}

	cudaMemcpy(d_a, h_a, sizeof(float)*N, cudaMemcpyHostToDevice);
	cudaMemcpy(d_b, h_b, sizeof(float)*N, cudaMemcpyHostToDevice);

	kernel << <blocksPerGrid, threadsPerBlock >> >(d_a,d_b,d_c);

	cudaMemcpy(h_c, d_c, sizeof(float)*blocksPerGrid, cudaMemcpyDeviceToHost);
	//Output h_c
	
	//This partial sum calculated on CPU, better than wasting GPU resources
	float c = 0;
	for (int i = 0; i < blocksPerGrid; i++)
	{
		c += h_c[i];
	}

	//CPU sum for verification and compare
	#define sumSquares(x) (x*(x+1)*(2*x+1)/6)
	printf("Does GPU value %.6g == %.6g\n", c,2*sumSquares((float) (N-1)));


	//Delete all memroAllocs
	cudaFree(d_a);
	cudaFree(d_b);
	cudaFree(d_c);
	delete [] h_a;
	delete [] h_b;
	delete[] h_c;
	return 911;
}*/
	.text
	.file	"dotProduct.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
