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I have an application which generates CUDA C++ source code, compiles it into PTX at runtime using NVRTC, and then creates CUDA modules from it using the CUDA driver API.
If I debug this application using cuda-gdb, it displays the kernel (where an error occured) in the backtrace, but does not show the line number.
I export the generated source code into a file, and give the directory to cuda-gdb using the --directory option. I also tried passing its file name to nvrtcCreateProgram() (name argument). I use the compile options --device-debug and --generate-line-info with NVRTC.
Is there a way to let cuda-gdb know the location of the generated source code file, and display the line number information in its backtrace?
For those who may not be familiar with nvrtc, it is a CUDA facility that allows runtime-compilation of CUDA C++ device code. As a result, device code generated at runtime (including modifications) can be used on a CUDA GPU. There is documentation for nvrtc and there are various CUDA sample codes for nvrtc, most or all of which have _nvrtc in the file name.
I was able to do kernel source-level debugging on a nvrtc-generated kernel with cuda-gdb as follows:
start with vectorAdd_nvrtc sample code
modify the compileFileToPTX routine (provided by nvrtc_helper.h) to add the --device-debug switch during the compile-cu-to-ptx step.
modify the loadPTX routine (provided by nvrtc_helper.h) to add the CU_JIT_GENERATE_DEBUG_INFO option (set to 1) for the cuModuleLoadDataEx load/JIT PTX-to-binary step.
compile the main function (vectorAdd.cpp) with -g option.
Here is a complete test case/session. I'm only showing the vectorAdd.cpp file from the project because that is the only file I modified. Other project file(s) are identical to what is in the sample project:
$ cat vectorAdd.cpp
/**
* Copyright 1993-2015 NVIDIA Corporation. All rights reserved.
*
* Please refer to the NVIDIA end user license agreement (EULA) associated
* with this source code for terms and conditions that govern your use of
* this software. Any use, reproduction, disclosure, or distribution of
* this software and related documentation outside the terms of the EULA
* is strictly prohibited.
*
*/
/**
* Vector addition: C = A + B.
*
* This sample is a very basic sample that implements element by element
* vector addition. It is the same as the sample illustrating Chapter 2
* of the programming guide with some additions like error checking.
*/
#include <stdio.h>
#include <cmath>
// For the CUDA runtime routines (prefixed with "cuda_")
#include <cuda.h>
#include <cuda_runtime.h>
// helper functions and utilities to work with CUDA
#include <helper_functions.h>
#include <nvrtc_helper.h>
#include <iostream>
#include <fstream>
/**
* Host main routine
*/
void my_compileFileToPTX(char *filename, int argc, char **argv, char **ptxResult,
size_t *ptxResultSize, int requiresCGheaders) {
std::ifstream inputFile(filename,
std::ios::in | std::ios::binary | std::ios::ate);
if (!inputFile.is_open()) {
std::cerr << "\nerror: unable to open " << filename << " for reading!\n";
exit(1);
}
std::streampos pos = inputFile.tellg();
size_t inputSize = (size_t)pos;
char *memBlock = new char[inputSize + 1];
inputFile.seekg(0, std::ios::beg);
inputFile.read(memBlock, inputSize);
inputFile.close();
memBlock[inputSize] = '\x0';
int numCompileOptions = 0;
char *compileParams[2];
std::string compileOptions;
if (requiresCGheaders) {
char HeaderNames[256];
#if defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64)
sprintf_s(HeaderNames, sizeof(HeaderNames), "%s", "cooperative_groups.h");
#else
snprintf(HeaderNames, sizeof(HeaderNames), "%s", "cooperative_groups.h");
#endif
compileOptions = "--include-path=";
std::string path = sdkFindFilePath(HeaderNames, argv[0]);
if (!path.empty()) {
std::size_t found = path.find(HeaderNames);
path.erase(found);
} else {
printf(
"\nCooperativeGroups headers not found, please install it in %s "
"sample directory..\n Exiting..\n",
argv[0]);
}
compileOptions += path.c_str();
compileParams[0] = reinterpret_cast<char *>(
malloc(sizeof(char) * (compileOptions.length() + 1)));
#if defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64)
sprintf_s(compileParams[0], sizeof(char) * (compileOptions.length() + 1),
"%s", compileOptions.c_str());
#else
snprintf(compileParams[0], compileOptions.size(), "%s",
compileOptions.c_str());
#endif
numCompileOptions++;
}
compileOptions = "--device-debug ";
compileParams[numCompileOptions] = reinterpret_cast<char *>(malloc(sizeof(char) * (compileOptions.length() + 1)));
snprintf(compileParams[numCompileOptions], compileOptions.size(), "%s", compileOptions.c_str());
numCompileOptions++;
// compile
nvrtcProgram prog;
NVRTC_SAFE_CALL("nvrtcCreateProgram",
nvrtcCreateProgram(&prog, memBlock, filename, 0, NULL, NULL));
nvrtcResult res = nvrtcCompileProgram(prog, numCompileOptions, compileParams);
// dump log
size_t logSize;
NVRTC_SAFE_CALL("nvrtcGetProgramLogSize",
nvrtcGetProgramLogSize(prog, &logSize));
char *log = reinterpret_cast<char *>(malloc(sizeof(char) * logSize + 1));
NVRTC_SAFE_CALL("nvrtcGetProgramLog", nvrtcGetProgramLog(prog, log));
log[logSize] = '\x0';
if (strlen(log) >= 2) {
std::cerr << "\n compilation log ---\n";
std::cerr << log;
std::cerr << "\n end log ---\n";
}
free(log);
NVRTC_SAFE_CALL("nvrtcCompileProgram", res);
// fetch PTX
size_t ptxSize;
NVRTC_SAFE_CALL("nvrtcGetPTXSize", nvrtcGetPTXSize(prog, &ptxSize));
char *ptx = reinterpret_cast<char *>(malloc(sizeof(char) * ptxSize));
NVRTC_SAFE_CALL("nvrtcGetPTX", nvrtcGetPTX(prog, ptx));
NVRTC_SAFE_CALL("nvrtcDestroyProgram", nvrtcDestroyProgram(&prog));
*ptxResult = ptx;
*ptxResultSize = ptxSize;
#ifdef DUMP_PTX
std::ofstream my_f;
my_f.open("vectorAdd.ptx");
for (int i = 0; i < ptxSize; i++)
my_f << ptx[i];
my_f.close();
#endif
if (requiresCGheaders) free(compileParams[0]);
}
CUmodule my_loadPTX(char *ptx, int argc, char **argv) {
CUmodule module;
CUcontext context;
int major = 0, minor = 0;
char deviceName[256];
// Picks the best CUDA device available
CUdevice cuDevice = findCudaDeviceDRV(argc, (const char **)argv);
// get compute capabilities and the devicename
checkCudaErrors(cuDeviceGetAttribute(
&major, CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR, cuDevice));
checkCudaErrors(cuDeviceGetAttribute(
&minor, CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR, cuDevice));
checkCudaErrors(cuDeviceGetName(deviceName, 256, cuDevice));
printf("> GPU Device has SM %d.%d compute capability\n", major, minor);
checkCudaErrors(cuInit(0));
checkCudaErrors(cuDeviceGet(&cuDevice, 0));
checkCudaErrors(cuCtxCreate(&context, 0, cuDevice));
CUjit_option opt[1];
opt[0] = CU_JIT_GENERATE_DEBUG_INFO;
void **vals = new void *[1];
vals[0] = (void *)(size_t)1;
checkCudaErrors(cuModuleLoadDataEx(&module, ptx, 1, opt, vals));
free(ptx);
return module;
}
int main(int argc, char **argv) {
char *ptx, *kernel_file;
size_t ptxSize;
kernel_file = sdkFindFilePath("vectorAdd_kernel.cu", argv[0]);
my_compileFileToPTX(kernel_file, argc, argv, &ptx, &ptxSize, 0);
CUmodule module = my_loadPTX(ptx, argc, argv);
CUfunction kernel_addr;
checkCudaErrors(cuModuleGetFunction(&kernel_addr, module, "vectorAdd"));
// Print the vector length to be used, and compute its size
int numElements = 50000;
size_t size = numElements * sizeof(float);
printf("[Vector addition of %d elements]\n", numElements);
// Allocate the host input vector A
float *h_A = reinterpret_cast<float *>(malloc(size));
// Allocate the host input vector B
float *h_B = reinterpret_cast<float *>(malloc(size));
// Allocate the host output vector C
float *h_C = reinterpret_cast<float *>(malloc(size));
// Verify that allocations succeeded
if (h_A == NULL || h_B == NULL || h_C == NULL) {
fprintf(stderr, "Failed to allocate host vectors!\n");
exit(EXIT_FAILURE);
}
// Initialize the host input vectors
for (int i = 0; i < numElements; ++i) {
h_A[i] = rand() / static_cast<float>(RAND_MAX);
h_B[i] = rand() / static_cast<float>(RAND_MAX);
}
// Allocate the device input vector A
CUdeviceptr d_A;
checkCudaErrors(cuMemAlloc(&d_A, size));
// Allocate the device input vector B
CUdeviceptr d_B;
checkCudaErrors(cuMemAlloc(&d_B, size));
// Allocate the device output vector C
CUdeviceptr d_C;
checkCudaErrors(cuMemAlloc(&d_C, size));
// Copy the host input vectors A and B in host memory to the device input
// vectors in device memory
printf("Copy input data from the host memory to the CUDA device\n");
checkCudaErrors(cuMemcpyHtoD(d_A, h_A, size));
checkCudaErrors(cuMemcpyHtoD(d_B, h_B, size));
// Launch the Vector Add CUDA Kernel
int threadsPerBlock = 256;
int blocksPerGrid = (numElements + threadsPerBlock - 1) / threadsPerBlock;
printf("CUDA kernel launch with %d blocks of %d threads\n", blocksPerGrid,
threadsPerBlock);
dim3 cudaBlockSize(threadsPerBlock, 1, 1);
dim3 cudaGridSize(blocksPerGrid, 1, 1);
void *arr[] = {reinterpret_cast<void *>(&d_A), reinterpret_cast<void *>(&d_B),
reinterpret_cast<void *>(&d_C),
reinterpret_cast<void *>(&numElements)};
checkCudaErrors(cuLaunchKernel(kernel_addr, cudaGridSize.x, cudaGridSize.y,
cudaGridSize.z, /* grid dim */
cudaBlockSize.x, cudaBlockSize.y,
cudaBlockSize.z, /* block dim */
0, 0, /* shared mem, stream */
&arr[0], /* arguments */
0));
checkCudaErrors(cuCtxSynchronize());
// Copy the device result vector in device memory to the host result vector
// in host memory.
printf("Copy output data from the CUDA device to the host memory\n");
checkCudaErrors(cuMemcpyDtoH(h_C, d_C, size));
// Verify that the result vector is correct
for (int i = 0; i < numElements; ++i) {
if (fabs(h_A[i] + h_B[i] - h_C[i]) > 1e-5) {
fprintf(stderr, "Result verification failed at element %d!\n", i);
exit(EXIT_FAILURE);
}
}
printf("Test PASSED\n");
// Free device global memory
checkCudaErrors(cuMemFree(d_A));
checkCudaErrors(cuMemFree(d_B));
checkCudaErrors(cuMemFree(d_C));
// Free host memory
free(h_A);
free(h_B);
free(h_C);
printf("Done\n");
return 0;
}
$ nvcc -g -I/usr/local/cuda/samples/common/inc -o test vectorAdd.cpp -lnvrtc -lcuda
$ cuda-gdb ./test
NVIDIA (R) CUDA Debugger
10.0 release
Portions Copyright (C) 2007-2018 NVIDIA Corporation
GNU gdb (GDB) 7.12
Copyright (C) 2016 Free Software Foundation, Inc.
License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>
This is free software: you are free to change and redistribute it.
There is NO WARRANTY, to the extent permitted by law. Type "show copying"
and "show warranty" for details.
This GDB was configured as "x86_64-pc-linux-gnu".
Type "show configuration" for configuration details.
For bug reporting instructions, please see:
<http://www.gnu.org/software/gdb/bugs/>.
Find the GDB manual and other documentation resources online at:
<http://www.gnu.org/software/gdb/documentation/>.
For help, type "help".
Type "apropos word" to search for commands related to "word"...
Reading symbols from ./test...done.
(cuda-gdb) break vectorAdd
Function "vectorAdd" not defined.
Make breakpoint pending on future shared library load? (y or [n]) y
Breakpoint 1 (vectorAdd) pending.
(cuda-gdb) r
Starting program: /home/user2/misc/junk/vectorAdd_nvrtc/test
[Thread debugging using libthread_db enabled]
Using host libthread_db library "/lib64/libthread_db.so.1".
[New Thread 0x7fffedc00700 (LWP 16789)]
> Using CUDA Device [1]: Tesla K40m
> GPU Device has SM 3.5 compute capability
[New Thread 0x7fffed3ff700 (LWP 16790)]
[Vector addition of 50000 elements]
Copy input data from the host memory to the CUDA device
CUDA kernel launch with 196 blocks of 256 threads
[Switching focus to CUDA kernel 0, grid 1, block (0,0,0), thread (0,0,0), device 0, sm 0, warp 0, lane 0]
Thread 1 "test" hit Breakpoint 1, vectorAdd<<<(196,1,1),(256,1,1)>>> (A=0x7fffce800000, B=0x7fffce830e00, C=0x7fffce861c00, numElements=50000) at ./vectorAdd_kernel.cu:21
21 int i = blockDim.x * blockIdx.x + threadIdx.x;
(cuda-gdb) step
23 if (i < numElements) {
(cuda-gdb) step
24 C[i] = A[i] + B[i];
(cuda-gdb) step
26 }
(cuda-gdb) quit
A debugging session is active.
Inferior 1 [process 16777] will be killed.
Quit anyway? (y or n) y
$
I have an application which generates CUDA C++ source code, compiles it into PTX at runtime using NVRTC, and then creates CUDA modules from it using the CUDA driver API.
If I debug this application using cuda-gdb, it displays the kernel (where an error occured) in the backtrace, but does not show the line number.
I export the generated source code into a file, and give the directory to cuda-gdb using the --directory option. I also tried passing its file name to nvrtcCreateProgram() (name argument). I use the compile options --device-debug and --generate-line-info with NVRTC.
Is there a way to let cuda-gdb know the location of the generated source code file, and display the line number information in its backtrace?
For those who may not be familiar with nvrtc, it is a CUDA facility that allows runtime-compilation of CUDA C++ device code. As a result, device code generated at runtime (including modifications) can be used on a CUDA GPU. There is documentation for nvrtc and there are various CUDA sample codes for nvrtc, most or all of which have _nvrtc in the file name.
I was able to do kernel source-level debugging on a nvrtc-generated kernel with cuda-gdb as follows:
start with vectorAdd_nvrtc sample code
modify the compileFileToPTX routine (provided by nvrtc_helper.h) to add the --device-debug switch during the compile-cu-to-ptx step.
modify the loadPTX routine (provided by nvrtc_helper.h) to add the CU_JIT_GENERATE_DEBUG_INFO option (set to 1) for the cuModuleLoadDataEx load/JIT PTX-to-binary step.
compile the main function (vectorAdd.cpp) with -g option.
Here is a complete test case/session. I'm only showing the vectorAdd.cpp file from the project because that is the only file I modified. Other project file(s) are identical to what is in the sample project:
$ cat vectorAdd.cpp
/**
* Copyright 1993-2015 NVIDIA Corporation. All rights reserved.
*
* Please refer to the NVIDIA end user license agreement (EULA) associated
* with this source code for terms and conditions that govern your use of
* this software. Any use, reproduction, disclosure, or distribution of
* this software and related documentation outside the terms of the EULA
* is strictly prohibited.
*
*/
/**
* Vector addition: C = A + B.
*
* This sample is a very basic sample that implements element by element
* vector addition. It is the same as the sample illustrating Chapter 2
* of the programming guide with some additions like error checking.
*/
#include <stdio.h>
#include <cmath>
// For the CUDA runtime routines (prefixed with "cuda_")
#include <cuda.h>
#include <cuda_runtime.h>
// helper functions and utilities to work with CUDA
#include <helper_functions.h>
#include <nvrtc_helper.h>
#include <iostream>
#include <fstream>
/**
* Host main routine
*/
void my_compileFileToPTX(char *filename, int argc, char **argv, char **ptxResult,
size_t *ptxResultSize, int requiresCGheaders) {
std::ifstream inputFile(filename,
std::ios::in | std::ios::binary | std::ios::ate);
if (!inputFile.is_open()) {
std::cerr << "\nerror: unable to open " << filename << " for reading!\n";
exit(1);
}
std::streampos pos = inputFile.tellg();
size_t inputSize = (size_t)pos;
char *memBlock = new char[inputSize + 1];
inputFile.seekg(0, std::ios::beg);
inputFile.read(memBlock, inputSize);
inputFile.close();
memBlock[inputSize] = '\x0';
int numCompileOptions = 0;
char *compileParams[2];
std::string compileOptions;
if (requiresCGheaders) {
char HeaderNames[256];
#if defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64)
sprintf_s(HeaderNames, sizeof(HeaderNames), "%s", "cooperative_groups.h");
#else
snprintf(HeaderNames, sizeof(HeaderNames), "%s", "cooperative_groups.h");
#endif
compileOptions = "--include-path=";
std::string path = sdkFindFilePath(HeaderNames, argv[0]);
if (!path.empty()) {
std::size_t found = path.find(HeaderNames);
path.erase(found);
} else {
printf(
"\nCooperativeGroups headers not found, please install it in %s "
"sample directory..\n Exiting..\n",
argv[0]);
}
compileOptions += path.c_str();
compileParams[0] = reinterpret_cast<char *>(
malloc(sizeof(char) * (compileOptions.length() + 1)));
#if defined(WIN32) || defined(_WIN32) || defined(WIN64) || defined(_WIN64)
sprintf_s(compileParams[0], sizeof(char) * (compileOptions.length() + 1),
"%s", compileOptions.c_str());
#else
snprintf(compileParams[0], compileOptions.size(), "%s",
compileOptions.c_str());
#endif
numCompileOptions++;
}
compileOptions = "--device-debug ";
compileParams[numCompileOptions] = reinterpret_cast<char *>(malloc(sizeof(char) * (compileOptions.length() + 1)));
snprintf(compileParams[numCompileOptions], compileOptions.size(), "%s", compileOptions.c_str());
numCompileOptions++;
// compile
nvrtcProgram prog;
NVRTC_SAFE_CALL("nvrtcCreateProgram",
nvrtcCreateProgram(&prog, memBlock, filename, 0, NULL, NULL));
nvrtcResult res = nvrtcCompileProgram(prog, numCompileOptions, compileParams);
// dump log
size_t logSize;
NVRTC_SAFE_CALL("nvrtcGetProgramLogSize",
nvrtcGetProgramLogSize(prog, &logSize));
char *log = reinterpret_cast<char *>(malloc(sizeof(char) * logSize + 1));
NVRTC_SAFE_CALL("nvrtcGetProgramLog", nvrtcGetProgramLog(prog, log));
log[logSize] = '\x0';
if (strlen(log) >= 2) {
std::cerr << "\n compilation log ---\n";
std::cerr << log;
std::cerr << "\n end log ---\n";
}
free(log);
NVRTC_SAFE_CALL("nvrtcCompileProgram", res);
// fetch PTX
size_t ptxSize;
NVRTC_SAFE_CALL("nvrtcGetPTXSize", nvrtcGetPTXSize(prog, &ptxSize));
char *ptx = reinterpret_cast<char *>(malloc(sizeof(char) * ptxSize));
NVRTC_SAFE_CALL("nvrtcGetPTX", nvrtcGetPTX(prog, ptx));
NVRTC_SAFE_CALL("nvrtcDestroyProgram", nvrtcDestroyProgram(&prog));
*ptxResult = ptx;
*ptxResultSize = ptxSize;
#ifdef DUMP_PTX
std::ofstream my_f;
my_f.open("vectorAdd.ptx");
for (int i = 0; i < ptxSize; i++)
my_f << ptx[i];
my_f.close();
#endif
if (requiresCGheaders) free(compileParams[0]);
}
CUmodule my_loadPTX(char *ptx, int argc, char **argv) {
CUmodule module;
CUcontext context;
int major = 0, minor = 0;
char deviceName[256];
// Picks the best CUDA device available
CUdevice cuDevice = findCudaDeviceDRV(argc, (const char **)argv);
// get compute capabilities and the devicename
checkCudaErrors(cuDeviceGetAttribute(
&major, CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MAJOR, cuDevice));
checkCudaErrors(cuDeviceGetAttribute(
&minor, CU_DEVICE_ATTRIBUTE_COMPUTE_CAPABILITY_MINOR, cuDevice));
checkCudaErrors(cuDeviceGetName(deviceName, 256, cuDevice));
printf("> GPU Device has SM %d.%d compute capability\n", major, minor);
checkCudaErrors(cuInit(0));
checkCudaErrors(cuDeviceGet(&cuDevice, 0));
checkCudaErrors(cuCtxCreate(&context, 0, cuDevice));
CUjit_option opt[1];
opt[0] = CU_JIT_GENERATE_DEBUG_INFO;
void **vals = new void *[1];
vals[0] = (void *)(size_t)1;
checkCudaErrors(cuModuleLoadDataEx(&module, ptx, 1, opt, vals));
free(ptx);
return module;
}
int main(int argc, char **argv) {
char *ptx, *kernel_file;
size_t ptxSize;
kernel_file = sdkFindFilePath("vectorAdd_kernel.cu", argv[0]);
my_compileFileToPTX(kernel_file, argc, argv, &ptx, &ptxSize, 0);
CUmodule module = my_loadPTX(ptx, argc, argv);
CUfunction kernel_addr;
checkCudaErrors(cuModuleGetFunction(&kernel_addr, module, "vectorAdd"));
// Print the vector length to be used, and compute its size
int numElements = 50000;
size_t size = numElements * sizeof(float);
printf("[Vector addition of %d elements]\n", numElements);
// Allocate the host input vector A
float *h_A = reinterpret_cast<float *>(malloc(size));
// Allocate the host input vector B
float *h_B = reinterpret_cast<float *>(malloc(size));
// Allocate the host output vector C
float *h_C = reinterpret_cast<float *>(malloc(size));
// Verify that allocations succeeded
if (h_A == NULL || h_B == NULL || h_C == NULL) {
fprintf(stderr, "Failed to allocate host vectors!\n");
exit(EXIT_FAILURE);
}
// Initialize the host input vectors
for (int i = 0; i < numElements; ++i) {
h_A[i] = rand() / static_cast<float>(RAND_MAX);
h_B[i] = rand() / static_cast<float>(RAND_MAX);
}
// Allocate the device input vector A
CUdeviceptr d_A;
checkCudaErrors(cuMemAlloc(&d_A, size));
// Allocate the device input vector B
CUdeviceptr d_B;
checkCudaErrors(cuMemAlloc(&d_B, size));
// Allocate the device output vector C
CUdeviceptr d_C;
checkCudaErrors(cuMemAlloc(&d_C, size));
// Copy the host input vectors A and B in host memory to the device input
// vectors in device memory
printf("Copy input data from the host memory to the CUDA device\n");
checkCudaErrors(cuMemcpyHtoD(d_A, h_A, size));
checkCudaErrors(cuMemcpyHtoD(d_B, h_B, size));
// Launch the Vector Add CUDA Kernel
int threadsPerBlock = 256;
int blocksPerGrid = (numElements + threadsPerBlock - 1) / threadsPerBlock;
printf("CUDA kernel launch with %d blocks of %d threads\n", blocksPerGrid,
threadsPerBlock);
dim3 cudaBlockSize(threadsPerBlock, 1, 1);
dim3 cudaGridSize(blocksPerGrid, 1, 1);
void *arr[] = {reinterpret_cast<void *>(&d_A), reinterpret_cast<void *>(&d_B),
reinterpret_cast<void *>(&d_C),
reinterpret_cast<void *>(&numElements)};
checkCudaErrors(cuLaunchKernel(kernel_addr, cudaGridSize.x, cudaGridSize.y,
cudaGridSize.z, /* grid dim */
cudaBlockSize.x, cudaBlockSize.y,
cudaBlockSize.z, /* block dim */
0, 0, /* shared mem, stream */
&arr[0], /* arguments */
0));
checkCudaErrors(cuCtxSynchronize());
// Copy the device result vector in device memory to the host result vector
// in host memory.
printf("Copy output data from the CUDA device to the host memory\n");
checkCudaErrors(cuMemcpyDtoH(h_C, d_C, size));
// Verify that the result vector is correct
for (int i = 0; i < numElements; ++i) {
if (fabs(h_A[i] + h_B[i] - h_C[i]) > 1e-5) {
fprintf(stderr, "Result verification failed at element %d!\n", i);
exit(EXIT_FAILURE);
}
}
printf("Test PASSED\n");
// Free device global memory
checkCudaErrors(cuMemFree(d_A));
checkCudaErrors(cuMemFree(d_B));
checkCudaErrors(cuMemFree(d_C));
// Free host memory
free(h_A);
free(h_B);
free(h_C);
printf("Done\n");
return 0;
}
$ nvcc -g -I/usr/local/cuda/samples/common/inc -o test vectorAdd.cpp -lnvrtc -lcuda
$ cuda-gdb ./test
NVIDIA (R) CUDA Debugger
10.0 release
Portions Copyright (C) 2007-2018 NVIDIA Corporation
GNU gdb (GDB) 7.12
Copyright (C) 2016 Free Software Foundation, Inc.
License GPLv3+: GNU GPL version 3 or later <http://gnu.org/licenses/gpl.html>
This is free software: you are free to change and redistribute it.
There is NO WARRANTY, to the extent permitted by law. Type "show copying"
and "show warranty" for details.
This GDB was configured as "x86_64-pc-linux-gnu".
Type "show configuration" for configuration details.
For bug reporting instructions, please see:
<http://www.gnu.org/software/gdb/bugs/>.
Find the GDB manual and other documentation resources online at:
<http://www.gnu.org/software/gdb/documentation/>.
For help, type "help".
Type "apropos word" to search for commands related to "word"...
Reading symbols from ./test...done.
(cuda-gdb) break vectorAdd
Function "vectorAdd" not defined.
Make breakpoint pending on future shared library load? (y or [n]) y
Breakpoint 1 (vectorAdd) pending.
(cuda-gdb) r
Starting program: /home/user2/misc/junk/vectorAdd_nvrtc/test
[Thread debugging using libthread_db enabled]
Using host libthread_db library "/lib64/libthread_db.so.1".
[New Thread 0x7fffedc00700 (LWP 16789)]
> Using CUDA Device [1]: Tesla K40m
> GPU Device has SM 3.5 compute capability
[New Thread 0x7fffed3ff700 (LWP 16790)]
[Vector addition of 50000 elements]
Copy input data from the host memory to the CUDA device
CUDA kernel launch with 196 blocks of 256 threads
[Switching focus to CUDA kernel 0, grid 1, block (0,0,0), thread (0,0,0), device 0, sm 0, warp 0, lane 0]
Thread 1 "test" hit Breakpoint 1, vectorAdd<<<(196,1,1),(256,1,1)>>> (A=0x7fffce800000, B=0x7fffce830e00, C=0x7fffce861c00, numElements=50000) at ./vectorAdd_kernel.cu:21
21 int i = blockDim.x * blockIdx.x + threadIdx.x;
(cuda-gdb) step
23 if (i < numElements) {
(cuda-gdb) step
24 C[i] = A[i] + B[i];
(cuda-gdb) step
26 }
(cuda-gdb) quit
A debugging session is active.
Inferior 1 [process 16777] will be killed.
Quit anyway? (y or n) y
$
I'm new to OpenACC. I like it very much so far as I'm familiar with OpenMP.
I have 2 1080Ti cards each with 9GB and I've 128GB of RAM. I'm trying a very basic test to allocate an array, initialize it, then sum it up in parallel. This works for 8 GB but when I increase to 10 GB I get out-of-memory error. My understanding was that with unified memory of Pascal (which these card are) and CUDA 8, I could allocate an array larger than the GPU's memory and the hardware will page in and page out on demand.
Here's my full C code test :
$ cat firstAcc.c
#include <stdio.h>
#include <openacc.h>
#include <stdlib.h>
#define GB 10
int main()
{
float *a;
size_t n = GB*1024*1024*1024/sizeof(float);
size_t s = n * sizeof(float);
a = (float *)malloc(s);
if (!a) { printf("Failed to malloc.\n"); return 1; }
printf("Initializing ... ");
for (int i = 0; i < n; ++i) {
a[i] = 0.1f;
}
printf("done\n");
float sum=0.0;
#pragma acc loop reduction (+:sum)
for (int i = 0; i < n; ++i) {
sum+=a[i];
}
printf("Sum is %f\n", sum);
free(a);
return 0;
}
As per the "Enable Unified Memory" section of this article I compile it with :
$ pgcc -acc -fast -ta=tesla:managed:cuda8 -Minfo firstAcc.c
main:
20, Loop not fused: function call before adjacent loop
Generated vector simd code for the loop
28, Loop not fused: function call before adjacent loop
Generated vector simd code for the loop containing reductions
Generated a prefetch instruction for the loop
I need to understand those messages but for now I don't think they are relevant. Then I run it :
$ ./a.out
malloc: call to cuMemAllocManaged returned error 2: Out of memory
Aborted (core dumped)
This works fine if I change GB to 8. I expected 10GB to work (despite the GPU card having 9GB) thanks to Pascal 1080Ti and CUDA 8.
Have I misunderstand, or what am I doing wrong? Thanks in advance.
$ pgcc -V
pgcc 17.4-0 64-bit target on x86-64 Linux -tp haswell
PGI Compilers and Tools
Copyright (c) 2017, NVIDIA CORPORATION. All rights reserved.
$ cat /usr/local/cuda-8.0/version.txt
CUDA Version 8.0.61
Besides what Bob mentioned, I made a few more fixes.
First, you're not actually generating an OpenACC compute region since you only have a "#pragma acc loop" directive. This should be "#pragma acc parallel loop". You can see this in the compiler feedback messages where it's only showing host code optimizations.
Second, the "i" index should be declared as a "long". Otherwise, you'll overflow the index.
Finally, you need to add "cc60" to your target accelerator options to tell the compiler to target a Pascal based GPU.
% cat mi.c
#include <stdio.h>
#include <openacc.h>
#include <stdlib.h>
#define GB 20ULL
int main()
{
float *a;
size_t n = GB*1024ULL*1024ULL*1024ULL/sizeof(float);
size_t s = n * sizeof(float);
printf("n = %lu, s = %lu\n", n, s);
a = (float *)malloc(s);
if (!a) { printf("Failed to malloc.\n"); return 1; }
printf("Initializing ... ");
for (int i = 0; i < n; ++i) {
a[i] = 0.1f;
}
printf("done\n");
double sum=0.0;
#pragma acc parallel loop reduction (+:sum)
for (long i = 0; i < n; ++i) {
sum+=a[i];
}
printf("Sum is %f\n", sum);
free(a);
return 0;
}
% pgcc -fast -acc -ta=tesla:managed,cuda8.0,cc60 -Minfo=accel mi.c
main:
21, Accelerator kernel generated
Generating Tesla code
21, Generating reduction(+:sum)
22, #pragma acc loop gang, vector(128) /* blockIdx.x threadIdx.x */
21, Generating implicit copyin(a[:5368709120])
% ./a.out
n = 5368709120, s = 21474836480
Initializing ... done
Sum is 536870920.000000
I believe a problem is here:
size_t n = GB*1024*1024*1024/sizeof(float);
when I compile that line of code with g++, I get a warning about integer overflow. For some reason the PGI compiler is not warning, but the same badness is occurring under the hood. After the declarations of s, and n, if I add a printout like this:
size_t n = GB*1024*1024*1024/sizeof(float);
size_t s = n * sizeof(float);
printf("n = %lu, s = %lu\n", n, s); // add this line
and compile with PGI 17.04, and run (on a P100, with 16GB) I get output like this:
$ pgcc -acc -fast -ta=tesla:managed:cuda8 -Minfo m1.c
main:
16, Loop not fused: function call before adjacent loop
Generated vector simd code for the loop
22, Loop not fused: function call before adjacent loop
Generated vector simd code for the loop containing reductions
Generated a prefetch instruction for the loop
$ ./a.out
n = 4611686017890516992, s = 18446744071562067968
malloc: call to cuMemAllocManaged returned error 2: Out of memory
Aborted
$
so it's evident that n and s are not what you intended.
We can fix this by marking all of those constants with ULL, and then things seem to work correctly for me:
$ cat m1.c
#include <stdio.h>
#include <openacc.h>
#include <stdlib.h>
#define GB 20ULL
int main()
{
float *a;
size_t n = GB*1024ULL*1024ULL*1024ULL/sizeof(float);
size_t s = n * sizeof(float);
printf("n = %lu, s = %lu\n", n, s);
a = (float *)malloc(s);
if (!a) { printf("Failed to malloc.\n"); return 1; }
printf("Initializing ... ");
for (int i = 0; i < n; ++i) {
a[i] = 0.1f;
}
printf("done\n");
double sum=0.0;
#pragma acc loop reduction (+:sum)
for (int i = 0; i < n; ++i) {
sum+=a[i];
}
printf("Sum is %f\n", sum);
free(a);
return 0;
}
$ pgcc -acc -fast -ta=tesla:managed:cuda8 -Minfo m1.c
main:
16, Loop not fused: function call before adjacent loop
Generated vector simd code for the loop
22, Loop not fused: function call before adjacent loop
Generated vector simd code for the loop containing reductions
Generated a prefetch instruction for the loop
$ ./a.out
n = 5368709120, s = 21474836480
Initializing ... done
Sum is 536870920.000000
$
Note that I've made another change above as well. I changed the sum accumulation variable from float to double. This is necessary to preserve somewhat "sensible" results when doing a very large reduction across very small quantities.
And, as #MatColgrove pointed out in his answer, I missed a few other things as well.
In one of my projects, I'm seeing some incorrect results when using CUB's
DeviceReduce::ReduceByKey. However, using the same inputs/outputs with thrust::reduce_by_key produces the expected results.
#include "cub/cub.cuh"
#include <vector>
#include <iostream>
#include <cuda.h>
struct AddFunctor {
__host__ __device__ __forceinline__
float operator()(const float & a, const float & b) const {
return a + b;
}
} reduction_op;
int main() {
int n = 7680;
std::vector < uint64_t > keys_h(n);
for (int i = 0; i < 4000; i++) keys_h[i] = 1;
for (int i = 4000; i < 5000; i++) keys_h[i] = 2;
for (int i = 5000; i < 7680; i++) keys_h[i] = 3;
uint64_t * keys;
cudaMalloc(&keys, sizeof(uint64_t) * n);
cudaMemcpy(keys, &keys_h[0], sizeof(uint64_t) * n, cudaMemcpyDefault);
uint64_t * unique_keys;
cudaMalloc(&unique_keys, sizeof(uint64_t) * n);
std::vector < float > values_h(n);
for (int i = 0; i < n; i++) values_h[i] = 1.0;
float * values;
cudaMalloc(&values, sizeof(float) * n);
cudaMemcpy(values, &values_h[0], sizeof(float) * n, cudaMemcpyDefault);
float * aggregates;
cudaMalloc(&aggregates, sizeof(float) * n);
int * remaining;
cudaMalloc(&remaining, sizeof(int));
size_t size = 0;
void * buffer = NULL;
cub::DeviceReduce::ReduceByKey(
buffer,
size,
keys,
unique_keys,
values,
aggregates,
remaining,
reduction_op,
n);
cudaMalloc(&buffer, sizeof(char) * size);
cub::DeviceReduce::ReduceByKey(
buffer,
size,
keys,
unique_keys,
values,
aggregates,
remaining,
reduction_op,
n);
int remaining_h;
cudaMemcpy(&remaining_h, remaining, sizeof(int), cudaMemcpyDefault);
std::vector < float > aggregates_h(remaining_h);
cudaMemcpy(&aggregates_h[0], aggregates, sizeof(float) * remaining_h, cudaMemcpyDefault);
for (int i = 0; i < remaining_h; i++) {
std::cout << i << ", " << aggregates_h[i] << std::endl;
}
cudaFree(buffer);
cudaFree(keys);
cudaFree(unique_keys);
cudaFree(values);
cudaFree(aggregates);
cudaFree(remaining);
}
When I include "-gencode arch=compute_35,code=sm_35" (for a Kepler GTX Titan), it produces the wrong results, but when I leave these flags out entirely, it works.
$ nvcc cub_test.cu
$ ./a.out
0, 4000
1, 1000
2, 2680
$ nvcc cub_test.cu -gencode arch=compute_35,code=sm_35
$ ./a.out
0, 4000
1, 1000
2, 768
I use a handful of other CUB calls without issue, just this one is misbehaving. I've also tried running this code on a GTX 1080 Ti (with
compute_61, sm_61) and see the same behavior.
Is the right solution to omit these compiler flags?
tried on one machine with:
cuda 8.0
ubuntu 16.04
gcc 5.4.0
cub 1.6.4
Kepler GTX Titan (compute capability 3.5)
and another with:
cuda 8.0
ubuntu 16.04
gcc 5.4.0
cub 1.6.4
Pascal GTX 1080 Ti (compute capability 6.1)
Sounds like you should file a bug report at the CUB repository issues page.
Edit: I can reproduce this issue:
[joeuser#myhost:/tmp]$ nvcc -I/opt/cub -o a a.cu
nvcc warning : The 'compute_20', 'sm_20', and 'sm_21' architectures are deprecated, and may be removed in a future release (Use -Wno-deprecated-gpu-targets to suppress warning).
[joeuser#myhost:/tmp]$ ./a
0, 4000
1, 1000
2, 2680
[joeuser#myhost:/tmp]$ nvcc -I/opt/cub -o a a.cu -gencode arch=compute_30,code=sm_30
[joeuser#myhost:/tmp]$ ./a
0, 4000
1, 1000
2, 512
Relevant info:
CUDA: 8.0.61
nVIDIA driver: 375.39
Distribution: GNU/Linux Mint 18.1
Linux kernel: 4.4.0
GCC: 5.4.0-6ubuntu1~16.04.4
cub: 1.6.4
GPU: GTX 650 Ti (Compute Capability 3.0)
I have a Cuda C++ code that uses Thrust currently working properly on a single GPU. I'd now like to modify it for multi-gpu. I have a host function that includes a number of Thrust calls that sort, copy, calculate differences etc on device arrays. I want to use each GPU to run this sequence of Thrust calls on it's own (independent) set of arrays at the same time. I've read that Thrust functions that return values are synchronous but can I use OpenMP to have each host thread call up a function (with Thrust calls) that runs on a separate GPU?
For example (coded in browser):
#pragma omp parallel for
for (int dev=0; dev<Ndev; dev++){
cudaSetDevice(dev);
runthrustfunctions(dev);
}
void runthrustfunctions(int dev){
/*lots of Thrust functions running on device arrays stored on corresponding GPU*/
//for example this is just a few of the lines"
thrust::device_ptr<double> pos_ptr = thrust::device_pointer_cast(particle[dev].pos);
thrust::device_ptr<int> list_ptr = thrust::device_pointer_cast(particle[dev].list);
thrust::sequence(list_ptr,list_ptr+length);
thrust::sort_by_key(pos_ptr, pos_ptr+length,list_ptr);
thrust::device_vector<double> temp(length);
thrust::gather(list_ptr,list_ptr+length,pos_ptr,temp.begin());
thrust::copy(temp.begin(), temp.end(), pos_ptr);
}`
I think I also need the structure "particle[0]" to be stored on GPU 0, particle[1] on GPU 1 etc and I my guess is this not possible. An option might be to use "switch" with separate code for each GPU case.
I'd like to know if this is a correct approach or if there is a better way?
Thanks
Yes, you can combine thrust and OpenMP.
Here's a complete worked example with results:
$ cat t340.cu
#include <omp.h>
#include <stdio.h>
#include <stdlib.h>
#include <thrust/host_vector.h>
#include <thrust/device_vector.h>
#include <thrust/sort.h>
#include <thrust/copy.h>
#include <time.h>
#include <sys/time.h>
#define DSIZE 200000000
using namespace std;
int main(int argc, char *argv[])
{
timeval t1, t2;
int num_gpus = 0; // number of CUDA GPUs
printf("%s Starting...\n\n", argv[0]);
// determine the number of CUDA capable GPUs
cudaGetDeviceCount(&num_gpus);
if (num_gpus < 1)
{
printf("no CUDA capable devices were detected\n");
return 1;
}
// display CPU and GPU configuration
printf("number of host CPUs:\t%d\n", omp_get_num_procs());
printf("number of CUDA devices:\t%d\n", num_gpus);
for (int i = 0; i < num_gpus; i++)
{
cudaDeviceProp dprop;
cudaGetDeviceProperties(&dprop, i);
printf(" %d: %s\n", i, dprop.name);
}
printf("initialize data\n");
// initialize data
typedef thrust::device_vector<int> dvec;
typedef dvec *p_dvec;
std::vector<p_dvec> dvecs;
for(unsigned int i = 0; i < num_gpus; i++) {
cudaSetDevice(i);
p_dvec temp = new dvec(DSIZE);
dvecs.push_back(temp);
}
thrust::host_vector<int> data(DSIZE);
thrust::generate(data.begin(), data.end(), rand);
// copy data
for (unsigned int i = 0; i < num_gpus; i++) {
cudaSetDevice(i);
thrust::copy(data.begin(), data.end(), (*(dvecs[i])).begin());
}
printf("start sort\n");
gettimeofday(&t1,NULL);
// run as many CPU threads as there are CUDA devices
omp_set_num_threads(num_gpus); // create as many CPU threads as there are CUDA devices
#pragma omp parallel
{
unsigned int cpu_thread_id = omp_get_thread_num();
cudaSetDevice(cpu_thread_id);
thrust::sort((*(dvecs[cpu_thread_id])).begin(), (*(dvecs[cpu_thread_id])).end());
cudaDeviceSynchronize();
}
gettimeofday(&t2,NULL);
printf("finished\n");
unsigned long et = ((t2.tv_sec * 1000000)+t2.tv_usec) - ((t1.tv_sec * 1000000) + t1.tv_usec);
if (cudaSuccess != cudaGetLastError())
printf("%s\n", cudaGetErrorString(cudaGetLastError()));
printf("sort time = %fs\n", (float)et/(float)(1000000));
// check results
thrust::host_vector<int> result(DSIZE);
thrust::sort(data.begin(), data.end());
for (int i = 0; i < num_gpus; i++)
{
cudaSetDevice(i);
thrust::copy((*(dvecs[i])).begin(), (*(dvecs[i])).end(), result.begin());
for (int j = 0; j < DSIZE; j++)
if (data[j] != result[j]) { printf("mismatch on device %d at index %d, host: %d, device: %d\n", i, j, data[j], result[j]); return 1;}
}
printf("Success\n");
return 0;
}
$ nvcc -Xcompiler -fopenmp -O3 -arch=sm_20 -o t340 t340.cu -lgomp
$ CUDA_VISIBLE_DEVICES="0" ./t340
./t340 Starting...
number of host CPUs: 12
number of CUDA devices: 1
0: Tesla M2050
initialize data
start sort
finished
sort time = 0.398922s
Success
$ ./t340
./t340 Starting...
number of host CPUs: 12
number of CUDA devices: 4
0: Tesla M2050
1: Tesla M2070
2: Tesla M2050
3: Tesla M2070
initialize data
start sort
finished
sort time = 0.460058s
Success
$
We can see that when I restrict the program to using a single device, the sort operation takes about 0.4 seconds. Then when I allow it to use all 4 devices (repeating the same sort on all 4 devices) the overall operation only take 0.46 seconds, even though we're doing 4 times as much work.
For this particular case I happened to be using CUDA 5.0 with thrust v1.7, and gcc 4.4.6 (RHEL 6.2)