
/*
 * File smc_impl_nested.cuh contains the implementation of the nested SMC.
 * This file is included by smc_impl.cuh and relies on the includes in smc_impl.cuh.
 */

// Nested inference is never used, and this code has become obsolete.
/*
 #include "macros/macros.cuh"
 #include "smc.cuh"
 #include "dists/dists.cuh"
 #include "particles_memory_handler.cuh"
 #include "resample/systematic/systematic_cpu.cuh"
 
 #ifdef __NVCC__
 #include <curand_kernel.h>
 #include "utils/cuda_error_utils.cuh"
 #include "resample/systematic/systematic_gpu.cuh"
 #include "smc_kernels.cuh"
 #endif


DEV double runSMCNested(
    #ifdef __NVCC__
    curandState* randState,
    #endif
    pplFunc_t* bblocks, int numBblocks, int numParticles, size_t progStateSize, bool parallelExec, bool parallelResampling, int parentIdx, 
    callbackFunc_t callback, void* ret, void* arg) {

    if(parallelExec || parallelResampling) {
        #ifndef GPU
        printf("Cannot run in parallel when not compiled for GPU");
        return 0.0;
        #endif
    }

    bool requireRandStates = parallelExec;

    floating_t logNormConstant = 0;
    
    particles_t particles = allocateParticlesNested(numParticles, progStateSize);
    
    #ifdef __NVCC__
    const int NUM_BLOCKS = (numParticles + NUM_THREADS_PER_BLOCK_NESTED - 1) / NUM_THREADS_PER_BLOCK_NESTED;

    curandState* randStates;
    if(requireRandStates) {
        randStates = new curandState[numParticles];
        initCurandStates<<<NUM_BLOCKS, NUM_THREADS_PER_BLOCK_NESTED>>>(randStates, numParticles, parentIdx);
        cudaDeviceSynchronize();
        cudaCheckErrorDev();
    }
    #endif

    resampler_t resampler = initResamplerNested(numParticles, progStateSize);

    // Run program/inference
    while(true) {

        if(parallelExec) {
            #ifdef __NVCC__
            // Use nested randStates
            execFuncs<<<NUM_BLOCKS, NUM_THREADS_PER_BLOCK_NESTED>>>(randStates, particles, bblocks, numParticles, numParticles, numBblocks, arg);
            cudaDeviceSynchronize();
            cudaCheckErrorDev();
            #endif
        
        } else {
            
            for(int i = 0; i < numParticles; i++) {
                int pc = particles.pcs[i];
                if(pc < numBblocks) {
                    bblocks[pc](
                        #ifdef __NVCC__
                        randState,
                        #endif
                        particles, i, arg); 
                }
            }
        }
        
        floating_t logWeightSum;
        if(parallelResampling) {
            #ifdef __NVCC__
            logWeightSum = calcLogWeightSumGpu(particles.weights, resampler, numParticles, NUM_BLOCKS, NUM_THREADS_PER_BLOCK_NESTED);
            #endif
        } else {
            logWeightSum = calcLogWeightSumCpu(particles.weights, resampler, numParticles);
        }

        logNormConstant += logWeightSum - log(static_cast<floating_t>(numParticles));
        
        if(particles.pcs[0] >= numBblocks) // Assumption: All terminate at the same time
            break;

        if(parallelResampling) {
            #ifdef __NVCC__
            resampleSystematicGpuNested(randState, particles, resampler, numParticles, NUM_BLOCKS);
            #endif
        } else {
            resampleSystematicCpu(
                #ifdef __NVCC__
                randState,
                #endif 
                particles, resampler, numParticles);
        }
        
        
    }

    callback(particles, numParticles, ret);
        
    // Clean up
    destResamplerNested(resampler);
    freeParticlesNested(particles);
    #ifdef __NVCC__
    if(requireRandStates)
        delete[] randStates;
    #endif

    return logNormConstant;
}
*/

	.file	"tmpxft_0023020c_00000000-6_smc_nested.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>

/*
 * File smc_impl_nested.cuh contains the implementation of the nested SMC.
 * This file is included by smc_impl.cuh and relies on the includes in smc_impl.cuh.
 */

// Nested inference is never used, and this code has become obsolete.
/*
 #include "macros/macros.cuh"
 #include "smc.cuh"
 #include "dists/dists.cuh"
 #include "particles_memory_handler.cuh"
 #include "resample/systematic/systematic_cpu.cuh"
 
 #ifdef __NVCC__
 #include <curand_kernel.h>
 #include "utils/cuda_error_utils.cuh"
 #include "resample/systematic/systematic_gpu.cuh"
 #include "smc_kernels.cuh"
 #endif


DEV double runSMCNested(
    #ifdef __NVCC__
    curandState* randState,
    #endif
    pplFunc_t* bblocks, int numBblocks, int numParticles, size_t progStateSize, bool parallelExec, bool parallelResampling, int parentIdx, 
    callbackFunc_t callback, void* ret, void* arg) {

    if(parallelExec || parallelResampling) {
        #ifndef GPU
        printf("Cannot run in parallel when not compiled for GPU");
        return 0.0;
        #endif
    }

    bool requireRandStates = parallelExec;

    floating_t logNormConstant = 0;
    
    particles_t particles = allocateParticlesNested(numParticles, progStateSize);
    
    #ifdef __NVCC__
    const int NUM_BLOCKS = (numParticles + NUM_THREADS_PER_BLOCK_NESTED - 1) / NUM_THREADS_PER_BLOCK_NESTED;

    curandState* randStates;
    if(requireRandStates) {
        randStates = new curandState[numParticles];
        initCurandStates<<<NUM_BLOCKS, NUM_THREADS_PER_BLOCK_NESTED>>>(randStates, numParticles, parentIdx);
        cudaDeviceSynchronize();
        cudaCheckErrorDev();
    }
    #endif

    resampler_t resampler = initResamplerNested(numParticles, progStateSize);

    // Run program/inference
    while(true) {

        if(parallelExec) {
            #ifdef __NVCC__
            // Use nested randStates
            execFuncs<<<NUM_BLOCKS, NUM_THREADS_PER_BLOCK_NESTED>>>(randStates, particles, bblocks, numParticles, numParticles, numBblocks, arg);
            cudaDeviceSynchronize();
            cudaCheckErrorDev();
            #endif
        
        } else {
            
            for(int i = 0; i < numParticles; i++) {
                int pc = particles.pcs[i];
                if(pc < numBblocks) {
                    bblocks[pc](
                        #ifdef __NVCC__
                        randState,
                        #endif
                        particles, i, arg); 
                }
            }
        }
        
        floating_t logWeightSum;
        if(parallelResampling) {
            #ifdef __NVCC__
            logWeightSum = calcLogWeightSumGpu(particles.weights, resampler, numParticles, NUM_BLOCKS, NUM_THREADS_PER_BLOCK_NESTED);
            #endif
        } else {
            logWeightSum = calcLogWeightSumCpu(particles.weights, resampler, numParticles);
        }

        logNormConstant += logWeightSum - log(static_cast<floating_t>(numParticles));
        
        if(particles.pcs[0] >= numBblocks) // Assumption: All terminate at the same time
            break;

        if(parallelResampling) {
            #ifdef __NVCC__
            resampleSystematicGpuNested(randState, particles, resampler, numParticles, NUM_BLOCKS);
            #endif
        } else {
            resampleSystematicCpu(
                #ifdef __NVCC__
                randState,
                #endif 
                particles, resampler, numParticles);
        }
        
        
    }

    callback(particles, numParticles, ret);
        
    // Clean up
    destResamplerNested(resampler);
    freeParticlesNested(particles);
    #ifdef __NVCC__
    if(requireRandStates)
        delete[] randStates;
    #endif

    return logNormConstant;
}
*/

	.text
	.file	"smc_nested.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
