I have a question about Linux boot auto-start

Graphics / Linux Linux
,
1

When my program is compiled and run, the output result is normal, when I put the program execution into rc.local, the output to the txt file is all 0, and I find the code with zero output in the middle like this:

void copy_zero(cublasHandle_t handle,float* input,int nfft,int M_Array,int fs,float* output)
{
 	for(int i=0;i<M_Array;i++)
        {
        	cudaDeviceSynchronize(); 
        	#pragma acc host_data use_device(input,output)
        	cublasScopy(handle, fs, input+i*fs,1,output+i*nfft,1);
        	cudaError_t cudaStatus = cudaGetLastError();
        	if (cudaStatus != cudaSuccess) 
        	{
        		//fprintf(stderr, "CUDA Error: %s\n", cudaGetErrorString(cudaStatus));
        		string sss=cudaGetErrorString(cudaStatus);
        		logWrite("sss"+sss);
        	}
        	cudaDeviceSynchronize(); 
        }
}

Call in the main function:

copy_zero(handle1,process_recv_data,NFFT,M_Array,Fs,process_recv_data32k);
 	#pragma acc update host(process_recv_data32k[:10])
        for(int f=0;f<10;f++)
        logWrite("process_recv_data32k"+to_string(f)+"="+to_string(process_recv_data32k[f]));

In the txt file process_recv_data32k it’s all zeros, of course, the back ones are also zeros, and when I don’t boot up, it process_recv_data32k’s not zero. (Because of the limited equipment, I use the ssh remote vim to open the txt, I think this does not affect), I suspect that the boot self-starting linear algebra library is not configured well, but I see in the copy_zero(handle1,process_recv_data,NFFT,M_Array,Fs,process_recv_data32k); In the previous code, there was cuda’s fft function and the dot multiplication function cublasSscal, which ran without any problems:

#pragma acc host_data use_device(input_ifft)
    {
        cublasSscal(handle1, M_Array * FsJ2, &alpha, input_ifft, 1);       
    }

It doesn’t feel like a library issue, I tried changing cublasScopy to cudaMemcpy code

void copy_zero(cublasHandle_t handle,float* input,int nfft,int M_Array,int fs,float* output)
{
      for(int i=0;i<M_Array;i++)
        {
        cudaDeviceSynchronize(); 
        #pragma acc host_data use_device(input,output)
        cudaMemcpy(output+i*nfft,input+i*fs,fs*sizeof(float),cudaMemcpyDeviceToDevice);
        cudaDeviceSynchronize(); 
        }
}

In this case, the function can be run with results, but many places in the code after copy_zero use CUDA’s functions and code, obviously my solution is not good.So I don’t have a clue right now.
I use echo $LD_LIBRARY_PATH to check the environment and display /usr/local/cuda-11.4/lib64: I want to start the environment has been configured, I also tried to write the environment configuration to rc-local.service, add it before running the program, but the operation still has no result.
Here’s my boot auto-start script:

#  SPDX-License-Identifier: LGPL-2.1+
#
#  This file is part of systemd.
#
#  systemd is free software; you can redistribute it and/or modify it
#  under the terms of the GNU Lesser General Public License as published by
#  the Free Software Foundation; either version 2.1 of the License, or
#  (at your option) any later version.

# This unit gets pulled automatically into multi-user.target by
# systemd-rc-local-generator if /etc/rc.local is executable.
[Unit]
Description=/etc/rc.local Compatibility
Documentation=man:systemd-rc-local-generator(8)
ConditionFileIsExecutable=/etc/rc.local
After=network.target

[Service]
Type=simple
ExecStart=/etc/rc.local start
TimeoutSec=0
RemainAfterExit=yes
Environment="LD_LIBRARY_PATH=/usr/local/cuda-11.4/lib64:$LD_LIBRARY_PATH"
Environment="CUDA_HOME=/usr/local/cuda-11.4"
GuessMainPID=no

[Install]
WantedBy=multi-user.target
Alias=rc-local.service

rc.local

#!/bin/bash

sleep 5
export LD_LIBRARY_PATH=/usr/local/cuda-11.4/lib64:$LD_LIBRARY_PATH
export CUDA_HOME=/usr/local/cuda-11.4

su - orin<<!
cd /home/orin/Desktop
(sleep 40 && ./daemonnode) &
exit
!
exit 0

Here’s how the program is compiled:

process:process.o  cudaCode.o 
	nvc++ -acc -gpu=cc87 -fast -cuda -cudalib -std=c++17 -Minfo=accel -lnppig -lnppc -lnppisu -lnppidei    -o process   process.o cudaCode.o
process.o:process.c
	nvc++ -fast -acc -gpu=cc87 -std=c++17 -cudalib -c process.c 
cudaCode.o:cudaCode.cu
	nvcc -rdc=true -c cudaCode.cu

datapack.o:datapack.c
	nvc++ -c datapack.c -o datapack.o

CircularBuffer.o:CircularBuffer.c
	nvc++ -c CircularBuffer.c -o CircularBuffer.o

CircularBufferSpliceData.o:CircularBufferSpliceData.c
	nvc++ -c CircularBufferSpliceData.c -o CircularBufferSpliceData.o

Resample.o:Resample.cu
	nvc++ -c Resample.cu -o Resample.o

FFTuse.o:FFTuse.c
	nvc++ -c -cuda -cudalib FFTuse.c -o FFTuse.o

macros.o:macros.c
	nvc++ -c macros.h -o macros.o
	
base_function.o:base_function.c
	nvc++ -c base_function.c -o base_function.o
clean:
	rm -f process.o cudaCode.o