1. Introduction — CUDA-GDB 12.9 documentation (original) (raw)

CUDA-GDB

The user manual for CUDA-GDB, the NVIDIA tool for debugging CUDA applications on Linux and QNX systems.

This document introduces CUDA-GDB, the NVIDIA® CUDA® debugger for Linux and QNX targets.

1.1. What is CUDA-GDB?

CUDA-GDB is the NVIDIA tool for debugging CUDA applications running on Linux and QNX. CUDA-GDB is an extension to GDB, the GNU Project debugger. The tool provides developers with a mechanism for debugging CUDA applications running on actual hardware. This enables developers to debug applications without the potential variations introduced by simulation and emulation environments.

1.2. Supported Features

CUDA-GDB is designed to present the user with a seamless debugging environment that allows simultaneous debugging of both GPU and CPU code within the same application. Just as programming in CUDA C is an extension to C programming, debugging with CUDA-GDB is a natural extension to debugging with GDB. The existing GDB debugging features are inherently present for debugging the host code, and additional features have been provided to support debugging CUDA device code.

CUDA-GDB supports debugging C/C++ and Fortran CUDA applications. Fortran debugging support is limited to 64-bit Linux operating system.

CUDA-GDB allows the user to set breakpoints, to single-step CUDA applications, and also to inspect and modify the memory and variables of any given thread running on the hardware.

CUDA-GDB supports debugging all CUDA applications, whether they use the CUDA driver API, the CUDA runtime API, or both.

CUDA-GDB supports debugging kernels that have been compiled for specific CUDA architectures, such as sm_75 or sm_80, but also supports debugging kernels compiled at runtime, referred to as just-in-time compilation, or JIT compilation for short.

1.3. About This Document

This document is the main documentation for CUDA-GDB and is organized more as a user manual than a reference manual. The rest of the document will describe how to install and use CUDA-GDB to debug CUDA kernels and how to use the new CUDA commands that have been added to GDB. Some walk-through examples are also provided. It is assumed that the user already knows the basic GDB commands used to debug host applications.

2. Release Notes

12.9 Release

Updated GDB version

Features

Fixed Issues

12.8 Release

Maxwell, Pascal, and Volta deprecation notice

Coredump env var deprecation notice

Features

Fixed Issues

12.7 Release

Features

Fixed Issues

12.6 Release

Features

Fixed Issues

12.5 Release

Updated GDB version

Support removal notice

Features

Fixed Issues

12.4 Release

Updated GDB version

Android deprecation notice

Python 3.6 and 3.7 deprecation notice

Features

Fixed Issues

12.3 Release

macOS host client deprecation notice

Features

Fixed Issues

12.2 Release

Features

Fixed Issues

12.1 Release

CUDA Driver API added for controlling core dump behavior

Features

Fixed Issues

12.0 Release

Updated GDB version

Texture and surface reference support removed

CUDA Memory Checker integration removed

Debugging of applications using CUDA Dynamic Parallelism

Features

Fixed Issues

11.8 Release

Features

Notes

11.7 Release

Features

Fixed Issues

11.6 Release

Updated GDB version

Features

Fixed Issues

11.5 Release

Python 3 support on Jetson and Drive Tegra devices

Fixed Issues

11.4 Update 1 Release

Known Issues with Fedora 34

Fixed Issues

11.4 Release

Updated GDB version

Python 3 support

GDB TUI mode disabled

Kepler deprecation notice

Coredump support

Fixed Issues

11.3 Release

Python 2 deprecation notice

Fixed Issues

11.2 Update 1 Release

GDB TUI deprecation notice

Fixed Issues

11.1 Release

Updated GDB version

Support for SM 8.6

Updated DWARF parser

Fixed Issues

11.0 Release

Updated GDB version

Support for SM8.0

Support for Bfloat16

MIG support

Mac support

10.1 Release

Enhanced debugging with only linenumber information

10.0 Release

Turing Uniform Register Support

9.2 Release

User induced core dump support

9.1 Release

Volta-MPS core dump support

Lightweight GPU core dump support

7.0 Release

GPU core dump support

6.5 Release

CUDA Fortran Support

GDB 7.6.2 Code Base

6.0 Release

Unified Memory Support

GDB 7.6 Code Base

Android Support

Single-Stepping Optimizations

Faster Remote Debugging

Kernel Entry Breakpoints

Precise Error Attribution

Live Range Optimizations

Event Notifications

5.5 Release

Kernel Launch Trace

Single-GPU Debugging (BETA)

Remote GPU Debugging

5.0 Release

Dynamic Parallelism Support

Attach/Detach

Attach on exception

API Error Reporting

Inlined Subroutine Support

4.2 Release

Kepler Support

4.1 Release

Source Base Upgraded to GDB 7.2

Simultaneous Sessions Support

New Autostep Command

Multiple Context Support

Device Assertions Support

Temporary Directory

3. Getting Started

The CUDA toolkit can be installed by following instructions in the Quick Start Guide.

Further steps should be taken to set up the debugger environment, build the application, and run the debugger.

3.1. Setting Up the Debugger Environment

3.1.1. Temporary Directory

By default, CUDA-GDB uses /tmp as the directory to store temporary files. To select a different directory, set the $TMPDIR environment variable.

Note

The user must have write and execute permission to the temporary directory used by CUDA-GDB. Otherwise, the debugger will fail with an internal error.

Note

The value of $TMPDIR must be the same in the environment of the application and CUDA-GDB. If they do not match, CUDA-GDB will fail to attach onto the application process.

Note

Since /tmp folder does not exist on Android device, the $TMPDIR environment variable must be set and point to a user-writeable folder before launching cuda-gdb.

3.1.2. Using the CUDA-GDB debugger on Jetson and Drive Tegra devices

By default, on Jetson and Drive Tegra devices, GPU debugging is supported only if cuda-gdb and cuda-gdbserver are launched by a user who is a member of the debug group.

To add the current user to the debug group run this command:

sudo usermod -a -G debug $USER

3.2. Compiling the Application

3.2.1. Debug Compilation

NVCC, the NVIDIA CUDA compiler driver, provides a mechanism for generating the debugging information necessary for CUDA-GDB to work properly. The -g -G option pair must be passed to NVCC when an application is compiled for ease of debugging with CUDA-GDB; for example,

Using this line to compile the CUDA application foo.cu

Note

Enabling the -G option increases the binary size by including debug information and reduces performance due to the absence of compiler optimizations.

To compile your CUDA Fortran code with debgging information necessary for CUDA-GDB to work properly, pgfortran, the PGI CUDA Fortran compiler, must be invoked with -g option. Also, for the ease of debugging and forward compatibility with the future GPU architectures, it is recommended to compile the code with -Mcuda=nordc option; for example,

pgfortran -g -Mcuda=nordc foo.cuf -o foo

For more information about the available compilation flags, please consult the PGI compiler documentation.

3.2.2. Compilation With Linenumber Information

Several enhancements were made to cuda-gdb’s support for debugging programs compiled with -lineinfo but not with -G. This is intended primarily for debugging programs built with OptiX/RTCore.

Note that -lineinfo can be used when trying to debug optimized code. In this case, debugger stepping and breakpoint behavior may appear somewhat erratic.

When debugging OptiX/RTCore code, the following should be kept in mind:

3.2.3. Compilation With PTX Debug Option

Using -Xptxas nvcc option, --make-errors-visible-at-exit can be used for error detection.

--make-errors-visible-at-exit (-make-errors-visible-at-exit) Generate required instructions at exit point to make memory faults and errors visible at exit.

Note

Extra instructions generated by this flag can reduce application performance.

3.2.4. Compiling For Specific GPU architectures

By default, the compiler will only generate code for the compute_52 PTX and sm_52 cubins. For later GPUs, the kernels are recompiled at runtime from the PTX for the architecture of the target GPU(s). Compiling for a specific virtual architecture guarantees that the application will work for any GPU architecture after that, for a trade-off in performance. This is done for forward-compatibility.

It is highly recommended to compile the application once and for all for the GPU architectures targeted by the application, and to generate the PTX code for the latest virtual architecture for forward compatibility.

A GPU architecture is defined by its compute capability. The list of GPUs and their respective compute capability, see https://developer.nvidia.com/cuda-gpus. The same application can be compiled for multiple GPU architectures. Use the -gencode compilation option to dictate which GPU architecture to compile for. The option can be specified multiple times.

For instance, to compile an application for a GPU with compute capability 7.0, add the following flag to the compilation command:

-gencode arch=compute_70,code=sm_70

To compile PTX code for any future architecture past the compute capability 7.0, add the following flag to the compilation command:

-gencode arch=compute_70,code=compute_70

For additional information, please consult the compiler documentation at https://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/index.html#extended-notation

3.2.5. Compiling with tensor memory checks

Starting with CUDA 12.8, the compiler can provide additional checks to catch invalid access errors by using the PTXAS flag -g-tmem-access-check.

--g-tensor-memory-access-check (-g-tmem-access-check) Enable tensor memory access checks for tcgen05 operations.

For additional information, please consult the compiler documentation at https://docs.nvidia.com/cuda/cuda-compiler-driver-nvcc/index.html#g-tensor-memory-access-check-g-tmem-access-check

3.3. Using the Debugger

CUDA-GDB can be used in the following system configurations:

3.3.1. Single-GPU Debugging with the Desktop Manager Running

For devices with compute capability 6.0 and higher CUDA-GDB can be used to debug CUDA applications on the same GPU that is running the desktop GUI.

Additionally for devices with compute capability less than 6.0 software preemption can be used to debug CUDA applications on the same GPU that is running the desktop GUI. There are two ways to enable this functionality:

Note

This is a BETA feature available on Linux and is only supported on Maxwell. The options listed below are ignored for GPUs with SM6.0 compute capability and higher.

Either of the options above will activate software preemption. These options must be set prior to running the application. When the GPU hits a breakpoint or any other event that would normally cause the GPU to freeze, CUDA-GDB releases the GPU for use by the desktop or other applications. This enables CUDA-GDB to debug a CUDA application on the same GPU that is running the desktop GUI, and also enables debugging of multiple CUDA applications context-switching on the same GPU.

3.3.2. Multi-GPU Debugging

Multi-GPU debugging designates the scenario where the application is running on more than one CUDA-capable device. Multi-GPU debugging is not much different than single-GPU debugging except for a few additional CUDA-GDB commands that let you switch between the GPUs.

Any GPU hitting a breakpoint will pause all the GPUs running CUDA on that system. Once paused, you can use info cuda kernels to view all the active kernels and the GPUs they are running on. When any GPU is resumed, all the GPUs are resumed.

Note

If the CUDA_VISIBLE_DEVICES environment is used, only the specified devices are suspended and resumed.

All CUDA-capable GPUs may run one or more kernels. To switch to an active kernel, use cuda kernel <n>, where n is the ID of the kernel retrieved from info cuda kernels.

Note

The same kernel can be loaded and used by different contexts and devices at the same time. When a breakpoint is set in such a kernel, by either name or file name and line number, it will be resolved arbitrarily to only one instance of that kernel. With the runtime API, the exact instance to which the breakpoint will be resolved cannot be controlled. With the driver API, the user can control the instance to which the breakpoint will be resolved to by setting the breakpoint right after its module is loaded.

3.3.3. Remote Debugging

There are multiple methods to remote debug an application with CUDA-GDB. In addition to using SSH or VNC from the host system to connect to the target system, it is also possible to use the target remote GDB feature. Using this option, the local cuda-gdb (client) connects to the cuda-gdbserver process (the server) running on the target system. This option is supported with a Linux client and a Linux or QNX server.

Setting remote debugging that way is a 2-step process:

Launch the cuda-gdbserver on the remote host

cuda-gdbserver can be launched on the remote host in different operation modes.

Note

Attaching to an already running process is not supported on QNX platforms.

Launch cuda-gdb on the client

Configure cuda-gdb to connect to the remote target using either:

or

(cuda-gdb) target extended-remote

Note

QNX platforms require setting the QNX_TARGET environment variable to the location of the target rootfs. If this is not available on the client running cuda-gdb, set it to an empty string.

It is recommended to use set sysroot command if libraries installed on the debug target might differ from the ones installed on the debug host. For example, cuda-gdb could be configured to connect to remote target as follows:

(cuda-gdb) set sysroot remote:// (cuda-gdb) target remote 192.168.0.2:1234

Where 192.168.0.2 is the IP address or domain name of the remote target, and 1234 is the TCP port previously previously opened by cuda-gdbserver.

3.3.4. Multiple Debuggers

For devices with compute capability 6.0 and higher several debugging sessions may take place simultaneously.

For devices with compute capability less than 6.0, several debugging sessions may take place simultaneously as long as the CUDA devices are used exclusively. For instance, one instance of CUDA-GDB can debug a first application that uses the first GPU while another instance of CUDA-GDB debugs a second application that uses the second GPU. The exclusive use of a GPU is achieved by specifying which GPU is visible to the application by using the CUDA_VISIBLE_DEVICES environment variable.

$ CUDA_VISIBLE_DEVICES=1 cuda-gdb my_app

Additionally for devices with compute capability less than 6.0, with software preemption enabled (set cuda software_preemption on), multiple CUDA-GDB instances can be used to debug CUDA applications context-switching on the same GPU.

3.3.5. Attaching/Detaching

CUDA-GDB can attach to and detach from a CUDA application running on GPUs with compute capability 2.0 and beyond, using GDB’s built-in commands for attaching to or detaching from a process.

Additionally, if the environment variable CUDA_DEVICE_WAITS_ON_EXCEPTION is set to 1 prior to running the CUDA application, the application will run normally until a device exception occurs. The application will then wait for CUDA-GDB to attach itself to it for further debugging. This feature is not supported on WSL.

Note

By default on some Linux distributions, the debugger cannot attach to an already running processes due to security settings. In order to enable the attach feature of the CUDA debugger, either cuda-gdb should be launched as root, or /proc/sys/kernel/yama/ptrace_scope should be set to zero, using the following command:

$ sudo sh -c "echo 0 >/proc/sys/kernel/yama/ptrace_scope"

To make the change permanent, edit /etc/sysctl.d/10-ptrace.conf.

4. CUDA-GDB Extensions

4.1. Command Naming Convention

The existing GDB commands are unchanged. Every new CUDA command or option is prefixed with the CUDA keyword. As much as possible, CUDA-GDB command names will be similar to the equivalent GDB commands used for debugging host code. For instance, the GDB command to display the host threads and switch to host thread 1 are, respectively:

(cuda-gdb) info threads (cuda-gdb) thread 1

To display the CUDA threads and switch to cuda thread 1, the user only has to type:

(cuda-gdb) info cuda threads (cuda-gdb) cuda thread 1

4.2. Getting Help

As with GDB commands, the built-in help for the CUDA commands is accessible from the cuda-gdb command line by using the help command:

(cuda-gdb) help cuda name_of_the_cuda_command (cuda-gdb) help set cuda name_of_the_cuda_option (cuda-gdb) help info cuda name_of_the_info_cuda_command

Moreover, all the CUDA commands can be auto-completed by pressing the TAB key, as with any other GDB command.

CUDA commands can also be queried using the apropos command.

4.3. Initialization File

The initialization file for CUDA-GDB is named .cuda-gdbinit and follows the same rules as the standard .gdbinit file used by GDB. The initialization file may contain any CUDA- GDB command. Those commands will be processed in order when CUDA-GDB is launched.

4.4. GUI Integration

Emacs

CUDA-GDB works with GUD in Emacs and XEmacs. No extra step is required other than pointing to the right binary.

To use CUDA-GDB, the gud-gdb-command-name variable must be set to cuda-gdb annotate=3. Use M-x customize-variable to set the variable.

Ensure that cuda-gdb is present in the Emacs/XEmacs $PATH.

DDD

CUDA-GDB works with DDD. To use DDD with CUDA-GDB, launch DDD with the following command:

cuda-gdb must be in your $PATH.

4.5. GPU core dump support

There are two ways to configure the core dump options for CUDA applications. Environment variables set in the application environment or programmatically from the application with the CUDA Driver API.

Note

GPU core dump generation is unsupported when other CUDA developer tools, including CUDA-GDB, are interacting with the application, unless explicitly documented as a supported use case (e.g., generate-core-file command).

Compilation for GPU core dump generation

GPU core dumps will be generated regardless of compilation flags used to generate the GPU application. For the best debugging experience, it is recommended to compile the application with the -g -G or the -lineinfo option with NVCC. See Compiling the Application for more information on passing compilation flags for debugging.

Enabling GPU core dump generation on exception with environment variables

Set the CUDA_ENABLE_COREDUMP_ON_EXCEPTION environment variable to 1 in order to enable generating a GPU core dump when a GPU exception is encountered. This option is disabled by default.

Set the CUDA_ENABLE_CPU_COREDUMP_ON_EXCEPTION environment variable to 0 in order to disable generating a CPU core dump when a GPU exception is encountered. This option is enabled by default when GPU core dump generation is enabled, but is deprecated in favor of the CUDA_COREDUMP_GENERATION_FLAGS="skip_abort" option.

Note

Enabling core dump generation can impact application performance even if no exception is encountered.

Controlling behavior of GPU core dump generation

The CUDA_COREDUMP_GENERATION_FLAGS environment variable can be used when generating GPU core dumps to deviate from default generation behavior. Multiple flags can be provided to this environment variable and are delimited by ,. These flags can be used to accomplish tasks such as reducing the size of the generated GPU core dump or other desired behaviors that deviate from the defaults. The table below lists each flag and the behavior when present.

GPU core dump CUDA_COREDUMP_GENERATION_FLAGS

Environment Variable flag Description
skip_nonrelocated_elf_images Disables including copies of nonrelocated elf images in the GPU core dump. Only the relocated images will be present.
skip_global_memory Disables dumping of GPU global and constbank memory segments.
skip_shared_memory Disables dumping of GPU shared memory segments.
skip_local_memory Disables dumping of GPU local memory segments.
skip_abort Disables calling abort() at the end of the GPU core dump generation process.

Note

Setting the CUDA_ENABLE_CPU_COREDUMP_ON_EXCEPTION environment variable to 0 is equivalent to CUDA_COREDUMP_GENERATION_FLAGS="skip_abort".

Limitations and notes for core dump generation

The following limitations apply to core dump support:

Note

The user should not send the application process a signal and ensure that the application process does not automatically terminate while the coredump generation is in process. Doing so may cause GPU coredump generation to abort.

Note

CPU core dumps will be located in a distribution specific location. Examining the /proc/sys/kernel/core_pattern file will typically hint at the name/location of the CPU core dump.

Note

NVIDIA vGPU platforms must explicitly enable debugging support to perform GPU core dump generation. Please reference the Virtual GPU Software User Guide for information on how to enable debugging on vGPU.

Note

NVIDIA Jetson and Drive Tegra devices must explicitly enable debugging support to perform GPU core dump generation. Refer to the Using the CUDA-GDB debugger on Jetson and Drive Tegra devices section.

Note

When generating core dumps on NVIDIA Drive Tegra devices running QNX, core dump generation may hang when generating CPU core dumps. If a hang is encountered, set CUDA_ENABLE_CPU_COREDUMP_EXCEPTION to 0.

Note

If core dumps are not generated when running programs built with OptiX/RTCore, try setting the environment variable OPTIX_FORCE_DEPRECATED_LAUNCHER to 1. Refer to the Debugging OptiX/RTCore applications section.

Note

Core dumps generation on Windows and Windows Subsystem for Linux (WSL) require the debug interface to be enabled by setting the registry key >HKEY_LOCAL_MACHINE\SOFTWARE\NVIDIA Corporation\GPUDebugger\EnableInterface to (DWORD) 1.

Note

GPU core dump is supported on GPUs running with Confidential Compute mode only with devtools mode. See Confidential Computing Deployment Guide https://docs.nvidia.com/confidential-computing-deployment-guide.pdf for more details on how to enable the mode.

Naming of GPU core dump files

By default, a GPU core dump is created in the current working directory. It is named core_TIME_HOSTNAME_PID.nvcudmp where TIME is the number of seconds since the Epoch, HOSTNAME is the host name of the machine running the CUDA application and PID is the process identifier of the CUDA application.

The CUDA_COREDUMP_FILE environment variable can be used to define a template that is used to change the name of a GPU core dump file. The template can either be an absolute path or a relative path to the current working directory. The template can contain % specifiers which are substituted by the following patterns when a GPU core dump is created:

Specifier Description
%h Host name of the machine running the CUDA application
%p Process identifier of the CUDA application
%t Time as the number of seconds since the Epoch, 1970-01-01 00:00:00 +0000 (UTC)

As an example, setting CUDA_COREDUMP_FILE to:

export CUDA_COREDUMP_FILE=newName.%h.%p

Would result in GPU core dumps being written to newName.myhost.1234 relative to the current working directory. Here myhost and 1234 are replaced with the real host name and pid respectively.

Setting CUDA_COREDUMP_FILE to:

export CUDA_COREDUMP_FILE="/home/$USER/newName.%h.%p"

Would result in GPU core dumps being written to the user’s home directory with the same name logic as in the above example.

If CUDA_COREDUMP_FILE points to an existing file of FIFO type (e.g named pipe), the core dump will be streamed to it.

Coredumps may be piped to shell commands via CUDA_COREDUMP_FILE with the following format:

export CUDA_COREDUMP_FILE='| cmd > file'

For example, to pipe a coredump to gzip use:

export CUDA_COREDUMP_FILE='| gzip -9 > cuda-coredump.gz'

Note

When piping a coredump, the % specifiers will not be recognized.

Enabling user induced GPU core dump generation

For the devices that support compute preemption, the user can interrupt a running CUDA process to generate the GPU core dump.

Set the CUDA_ENABLE_USER_TRIGGERED_COREDUMP environment variable to 1 in order to enable generating a user induced GPU core dump. This option is disabled by default. Setting this environment variable will open a communication pipe for each subsequently running CUDA process. To induce the GPU core dump, the user simply writes to the pipe.

To change the default pipe file name, set the CUDA_COREDUMP_PIPE environment variable to a specific pipe name. The default pipe name is in the following format: corepipe.cuda.HOSTNAME.PID where HOSTNAME is the host name of machine running the CUDA application and PID is the process identifier of the CUDA application. This environment variable can take % specifiers as decribed in the above section.

GPU core dumps can be manually generated using cuda-gdb generate-core-file or gcore command. An optional filename can be passed, see help generate-core-file for details. Several flags are available to control what to save and whether or not to generate a CPU core file along with it.

Displaying core dump generation progress

By default, when an application crashes and generates a GPU core dump, the application may appear to be unresponsive or frozen until fully generated.

Set the CUDA_COREDUMP_SHOW_PROGRESS environment variable to 1 in order to print core dump generation progress messages to stderr. This can be used to determine how far along the coredump generation is:

coredump: SM 1/14 has finished state collection coredump: SM 2/14 has finished state collection coredump: SM 3/14 has finished state collection coredump: SM 4/14 has finished state collection coredump: SM 5/14 has finished state collection coredump: SM 6/14 has finished state collection coredump: SM 7/14 has finished state collection coredump: SM 8/14 has finished state collection coredump: SM 9/14 has finished state collection coredump: SM 10/14 has finished state collection coredump: SM 11/14 has finished state collection coredump: SM 12/14 has finished state collection coredump: SM 13/14 has finished state collection coredump: SM 14/14 has finished state collection coredump: Device 1/1 has finished state collection coredump: Calculating ELF file layout coredump: ELF file layout calculated coredump: Writing ELF file to core_TIME_HOSTNAME_PID.nvcudmp coredump: Writing out global memory (1073741824 bytes) coredump: 5%... coredump: 10%... coredump: 15%... coredump: 20%... coredump: 25%... coredump: 30%... coredump: 35%... coredump: 40%... coredump: 45%... coredump: 50%... coredump: 55%... coredump: 60%... coredump: 65%... coredump: 70%... coredump: 75%... coredump: 80%... coredump: 85%... coredump: 90%... coredump: 95%... coredump: 100%... coredump: Writing out device table coredump: Finalizing coredump: All done

Enabling GPU core dump generation with the CUDA Driver API

The Driver API has equivalent settings for all of the environment variables, with the added feature of being able to set different core dump settings per-context instead of globally. This API can be called directly inside your application. Use cuCoredumpGetAttributeGlobal and cuCoredumpSetAttributeGlobal to fetch or set the global attribute. Use cuCoredumpGetAttribute and cuCoredumpSetAttribute to fetch or set the per context attribute. See the Coredump Attributes Control API manual for more information.

The table below lists the environment variables and the equivalent CUcoredumpSettings flags that are available to manage core dump settings with the Coredump Attributes Control API.

Note

The CU_COREDUMP_ENABLE_USER_TRIGGER setting can only be set globally in the driver API and CU_COREDUMP_PIPE must be set (if desired) before user-triggered core dumps are enabled.

GPU core dump configuration parameters

Environment Variable Description
Environment Variable: CUDA_ENABLE_COREDUMP_ON_EXCEPTION CUcoredumpSettings Flag: CU_COREDUMP_ENABLE_ON_EXCEPTION Enables GPU core dump generation for exceptions. Disabled by default.
Environment Variable: CUDA_ENABLE_CPU_COREDUMP_ON_EXCEPTION CUcoredumpSettings Flag: CU_COREDUMP_TRIGGER_HOST Triggers host (CPU) core dump after GPU core dump is complete. Enabled by default. Deprecated as of CUDA 12.5 in favor of theCU_COREDUMP_SKIP_ABORT value given with the CU_COREDUMP_GENERATION_FLAGS option.
Environment Variable: CUDA_ENABLE_USER_TRIGGERED_COREDUMP CUcoredumpSettings Flag: CU_COREDUMP_ENABLE_USER_TRIGGER Enables user triggerable core dumps by writing to a pipe defined in the COREDUMP_PIPE setting. Disabled by default.
Environment Variable: CUDA_COREDUMP_FILE CUcoredumpSettings Flag: CU_COREDUMP_FILE Filename template for the GPU core dump.
Environment Variable: CUDA_COREDUMP_PIPE CUcoredumpSettings Flag: CU_COREDUMP_PIPE Filename template for the user pipe trigger.
Environment Variable: CUDA_COREDUMP_GENERATION_FLAGS CUcoredumpSettings Flag: CU_COREDUMP_GENERATION_FLAGS Flags used to control GPU coredump generation settings. Use a bitwise OR to pass multiple settings in one call. See CUCoredumpGenerationFlags in cuda.h for the list of valid values in the current release.

Inspecting GPU and GPU+CPU core dumps in cuda-gdb

Use the following command to load the GPU core dump into the debugger

Use the following command to load CPU and GPU core dumps into the debugger

Note

Coredump inspection does not require that a GPU be installed on the system

5. Kernel Focus

A CUDA application may be running several host threads and many device threads. To simplify the visualization of information about the state of application, commands are applied to the entity in focus.

When the focus is set to a host thread, the commands will apply only to that host thread (unless the application is fully resumed, for instance). On the device side, the focus is always set to the lowest granularity level–the device thread.

5.1. Software Coordinates vs. Hardware Coordinates

A device thread belongs to a block, which in turn belongs to a kernel. Thread, block, and kernel are the software coordinates of the focus. A device thread runs on a lane. A lane belongs to a warp, which belongs to an SM, which in turn belongs to a device. Lane, warp, SM, and device are the hardware coordinates of the focus. Software and hardware coordinates can be used interchangeably and simultaneously as long as they remain coherent.

Another software coordinate is sometimes used: the grid. The difference between a grid and a kernel is the scope. The grid ID is unique per GPU whereas the kernel ID is unique across all GPUs. Therefore there is a 1:1 mapping between a kernel and a (grid,device) tuple.

Note

If software preemption is enabled (set cuda software_preemption on), hardware coordinates corresponding to a device thread are likely to change upon resuming execution on the device. However, software coordinates will remain intact and will not change for the lifetime of the device thread.

5.2. Current Focus

To inspect the current focus, use the cuda command followed by the coordinates of interest:

(cuda-gdb) cuda device sm warp lane block thread block (0,0,0), thread (0,0,0), device 0, sm 0, warp 0, lane 0 (cuda-gdb) cuda kernel block thread kernel 1, block (0,0,0), thread (0,0,0) (cuda-gdb) cuda kernel kernel 1

5.3. Switching Focus

To switch the current focus, use the cuda command followed by the coordinates to be changed:

(cuda-gdb) cuda device 0 sm 1 warp 2 lane 3 [Switching focus to CUDA kernel 1, grid 2, block (8,0,0), thread (67,0,0), device 0, sm 1, warp 2, lane 3] 374 int totalThreads = gridDim.x * blockDim.x;

If the specified focus is not fully defined by the command, the debugger will assume that the omitted coordinates are set to the coordinates in the current focus, including the subcoordinates of the block and thread.

(cuda-gdb) cuda thread (15) [Switching focus to CUDA kernel 1, grid 2, block (8,0,0), thread (15,0,0), device 0, sm 1, warp 0, lane 15] 374 int totalThreads = gridDim.x * blockDim.x;

The parentheses for the block and thread arguments are optional.

(cuda-gdb) cuda block 1 thread 3 [Switching focus to CUDA kernel 1, grid 2, block (1,0,0), thread (3,0,0), device 0, sm 3, warp 0, lane 3] 374 int totalThreads = gridDim.x * blockDim.

6. Program Execution

Applications are launched the same way in CUDA-GDB as they are with GDB by using the run command. This chapter describes how to interrupt and single-step CUDA applications

6.1. Interrupting the Application

If the CUDA application appears to be hanging or stuck in an infinite loop, it is possible to manually interrupt the application by pressing CTRL+C. When the signal is received, the GPUs are suspended and the cuda-gdb prompt will appear.

At that point, the program can be inspected, modified, single-stepped, resumed, or terminated at the user’s discretion.

This feature is limited to applications running within the debugger. It is not possible to break into and debug applications that have been launched outside the debugger.

6.2. Single Stepping

Single-stepping device code is supported. However, unlike host code single-stepping, device code single-stepping works at the warp level. This means that single-stepping a device kernel advances all the active threads in the warp currently in focus. The divergent threads in the warp are not single-stepped. When the CUDA thread in focus becomes divergent, behavior depends on the value of set cuda step_divergent_lanes. When on (default), the warp in focus will be continuously single-stepped until the CUDA thread in focus becomes active. When off, the warp in focus will be stepped and the focused CUDA thread will be changed to the nearest active lane in the warp.

In order to advance the execution of more than one warp, a breakpoint must be set at the desired location and then the application must be fully resumed.

A special case is single-stepping over thread barrier calls like: __syncthreads() or cluster-wide barriers. In this case, an implicit temporary breakpoint is set immediately after the barrier and all threads are resumed until the temporary breakpoint is hit.

You can step in, over, or out of the device functions as long as they are not inlined. To force a function to not be inlined by the compiler, the __noinline__ keyword must be added to the function declaration.

Asynchronous SASS instructions executed on the device, such as the warpgroup instructions, at prior PCs are not guaranteed to be complete.

With Dynamic Parallelism, several CUDA APIs can be called directly from device code. The following list defines single-step behavior when encountering these APIs:

Note

It is not possible to step into a device launch call (nor the routine launched by the call).

7. Breakpoints and Watchpoints

There are multiple ways to set a breakpoint on a CUDA application. These methods are described below. The commands used to set a breakpoint on device code are the same as the commands used to set a breakpoint on host code.

If a breakpoint is set on device code, the breakpoint will be marked pending until the ELF image of the kernel is loaded. At that point, the breakpoint will be resolved and its address will be updated.

When a breakpoint is set, it forces all resident GPU threads to stop at this location when it reaches the corresponding PC.

When a breakpoint is hit by one thread, there is no guarantee that the other threads will hit the breakpoint at the same time. Therefore the same breakpoint may be hit several times, and the user must be careful with checking which thread(s) actually hit(s) the breakpoint. The disable command can be used to prevent hitting the breakpoint by additional threads.

7.1. Symbolic Breakpoints

To set a breakpoint at the entry of a function, use the break command followed by the name of the function or method:

(cuda-gdb) break my_function (cuda-gdb) break my_class::my_method

For templatized functions and methods, the full signature must be given:

(cuda-gdb) break int my_templatized_function(int)

The mangled name of the function can also be used. To find the mangled name of a function, you can use the following command:

(cuda-gdb) set demangle-style none (cuda-gdb) info function my_function_name (cuda-gdb) set demangle-style auto

7.2. Line Breakpoints

To set a breakpoint on a specific line number, use the following syntax:

(cuda-gdb) break my_file.cu:185

If the specified line corresponds to an instruction within templatized code, multiple breakpoints will be created, one for each instance of the templatized code.

7.3. Address Breakpoints

To set a breakpoint at a specific address, use the break command with the address as argument:

(cuda-gdb) break *0x1afe34d0

The address can be any address on the device or the host.

7.4. Kernel Entry Breakpoints

To break on the first instruction of every launched kernel, set the break_on_launch option to application:

(cuda-gdb) set cuda break_on_launch application

See set cuda break_on_launch for more information.

7.5. Conditional Breakpoints

To make the breakpoint conditional, use the optional if keyword or the cond command.

(cuda-gdb) break foo.cu:23 if threadIdx.x == 1 && i < 5 (cuda-gdb) cond 3 threadIdx.x == 1 && i < 5

Conditional expressions may refer any variable, including built-in variables such as threadIdx and blockIdx. Function calls are not allowed in conditional expressions.

Note that conditional breakpoints are always hit and evaluated, but the debugger reports the breakpoint as being hit only if the conditional statement is evaluated to TRUE. The process of hitting the breakpoint and evaluating the corresponding conditional statement is time-consuming. Therefore, running applications while using conditional breakpoints may slow down the debugging session. Moreover, if the conditional statement is always evaluated to FALSE, the debugger may appear to be hanging or stuck, although it is not the case. You can interrupt the application with CTRL-C to verify that progress is being made.

Conditional breakpoints can be set on code from CUDA modules that are not already loaded. The verification of the condition will then only take place when the ELF image of that module is loaded. Therefore any error in the conditional expression will be deferred until the CUDA module is loaded. To double check the desired conditional expression, first set an unconditional breakpoint at the desired location and continue. When the breakpoint is hit, evaluate the desired conditional statement by using the cond command.

7.6. Watchpoints

Watchpoints on CUDA code are not supported.

Watchpoints on host code are supported. The user is invited to read the GDB documentation for a tutorial on how to set watchpoints on host code.

8. Inspecting Program State

8.1. Memory and Variables

The GDB print command has been extended to decipher the location of any program variable and can be used to display the contents of any CUDA program variable including:

8.2. Variable Storage and Accessibility

Depending on the variable type and usage, variables can be stored either in registers or in local, shared, const or global memory. You can print the address of any variable to find out where it is stored and directly access the associated memory.

The example below shows how the variable array, which is of type shared int *, can be directly accessed in order to see what the stored values are in the array.

(cuda-gdb) print &array $1 = (@shared int (*)[0]) 0x20 (cuda-gdb) print array[0]@4 $2 = {0, 128, 64, 192}

You can also access the shared memory indexed into the starting offset to see what the stored values are:

(cuda-gdb) print (@shared int)0x20 $3 = 0 (cuda-gdb) print (@shared int)0x24 $4 = 128 (cuda-gdb) print (@shared int)0x28 $5 = 64

The example below shows how to access the starting address of the input parameter to the kernel.

(cuda-gdb) print &data $6 = (const @global void * const @parameter *) 0x10 (cuda-gdb) print *(@global void * const @parameter *) 0x10 $7 = (@global void * const @parameter) 0x110000</>

8.3. Info CUDA Commands

These are commands that display information about the GPU and the application’s CUDA state. The available options are:

devices

information about all the devices

sms

information about all the active SMs in the current device

warps

information about all the active warps in the current SM

lanes

information about all the active lanes in the current warp

kernels

information about all the active kernels

blocks

information about all the active blocks in the current kernel

threads

information about all the active threads in the current kernel

launch trace

information about the parent kernels of the kernel in focus

launch children

information about the kernels launched by the kernels in focus

contexts

information about all the contexts

A filter can be applied to every info cuda command. The filter restricts the scope of the command. A filter is composed of one or more restrictions. A restriction can be any of the following:

where n, x, y, z are integers, or one of the following special keywords: current, any, and all. current indicates that the corresponding value in the current focus should be used. any and all indicate that any value is acceptable.

Note

The breakpoint all and breakpoint n filter are only effective for the info cuda threads command.

8.3.1. info cuda devices

This command enumerates all the GPUs in the system sorted by device index. A * indicates the device currently in focus. This command supports filters. The default is device all. This command prints No CUDA Devices if no active GPUs are found. A device is not considered active until the first kernel launch has been encountered.

(cuda-gdb) info cuda devices Dev PCI Bus/Dev ID Name Description SM Type SMs Warps/SM Lanes/Warp Max Regs/Lane Active SMs Mask 0 06:00.0 GeForce GTX TITAN Z GK110B sm_35 15 64 32 256 0x00000000 1 07:00.0 GeForce GTX TITAN Z GK110B sm_35 15 64 32 256 0x00000000

8.3.2. info cuda sms

This command shows all the SMs for the device and the associated active warps on the SMs. This command supports filters and the default is device current sm all. A * indicates the SM is focus. The results are grouped per device.

(cuda-gdb) info cuda sms SM Active Warps Mask Device 0

8.3.3. info cuda warps

This command takes you one level deeper and prints all the warps information for the SM in focus. This command supports filters and the default is device current sm current warp all. The command can be used to display which warp executes what block.

(cuda-gdb) info cuda warps Wp /Active Lanes Mask/ Divergent Lanes Mask/Active Physical PC/Kernel/BlockIdx Device 0 SM 0

8.3.4. info cuda lanes

This command displays all the lanes (threads) for the warp in focus. This command supports filters and the default is device current sm current warp current lane all. In the example below you can see that all the lanes are at the same physical PC. The command can be used to display which lane executes what thread.

(cuda-gdb) info cuda lanes Ln State Physical PC ThreadIdx Device 0 SM 0 Warp 0

8.3.5. info cuda kernels

This command displays on all the active kernels on the GPU in focus. It prints the SM mask, kernel ID, and the grid ID for each kernel with the associated dimensions and arguments. The kernel ID is unique across all GPUs whereas the grid ID is unique per GPU. The Parent column shows the kernel ID of the parent grid. This command supports filters and the default is kernel all.

(cuda-gdb) info cuda kernels Kernel Parent Dev Grid Status SMs Mask GridDim BlockDim Name Args

This command will also show grids that have been launched on the GPU with Dynamic Parallelism. Kernels with a negative grid ID have been launched from the GPU, while kernels with a positive grid ID have been launched from the CPU.

8.3.6. info cuda barriers

This command displays the states of all the lanes (threads) for the warp in focus. The results are grouped per warp. This command supports filters and the default is device current sm current warp. Each line of output represents one or more lanes (CUDA threads). Each CUDA thread has a state (Ready, BlockedPlus, Exited, etc.). It can be active, exited, participating in a collective region, or synchronized on one or more warp convergence barriers. The outputs are coalesced by default, so that threads having identical state are grouped together.

(cuda-gdb) info cuda barriers Device 0 SM 0 Wp 0 Ln(s) Thread State Active Exited Collective Warp Convergence Barrier(s) (0-2) Ready yes no no 0 3 Ready yes no no none (4-31) Ready yes no no 0

When coalescing is turned off the state of every lane (CUDA thread) is displayed per line:

(cuda-gdb) set cuda coalescing off Coalescing of the CUDA commands output is off. (cuda-gdb) info cuda barriers Device 0 SM 0 Wp 0 Ln(s) Thread State Active Exited Collective Warp Convergence Barrier(s) 0 Ready yes no no 0 1 Ready yes no no 0 2 Ready yes no no 0 3 Ready yes no no none 4 Ready yes no no 0 5 Ready yes no no 0 ...

8.3.7. info cuda blocks

This command displays all the active or running blocks for the kernel in focus. The results are grouped per kernel. This command supports filters and the default is kernel current block all. The outputs are coalesced by default.

(cuda-gdb) info cuda blocks BlockIdx To BlockIdx Count State Kernel 1

Coalescing can be turned off as follows in which case more information on the Device and the SM get displayed:

(cuda-gdb) set cuda coalescing off

The following is the output of the same command when coalescing is turned off.

(cuda-gdb) info cuda blocks BlockIdx State Dev SM Kernel 1

8.3.8. info cuda threads

This command displays the application’s active CUDA blocks and threads with the total count of threads in those blocks. Also displayed are the virtual PC and the associated source file and the line number information. The results are grouped per kernel. The command supports filters with default being kernel current block all thread all. The outputs are coalesced by default as follows:

(cuda-gdb) info cuda threads BlockIdx ThreadIdx To BlockIdx ThreadIdx Count Virtual PC Filename Line Device 0 SM 0

Coalescing can be turned off as follows in which case more information is displayed with the output.

(cuda-gdb) info cuda threads BlockIdx ThreadIdx Virtual PC Dev SM Wp Ln Filename Line Kernel 1

Note

In coalesced form, threads must be contiguous in order to be coalesced. If some threads are not currently running on the hardware, they will create holes in the thread ranges. For instance, if a kernel consist of 2 blocks of 16 threads, and only the 8 lowest threads are active, then 2 coalesced ranges will be printed: one range for block 0 thread 0 to 7, and one range for block 1 thread 0 to 7. Because threads 8-15 in block 0 are not running, the 2 ranges cannot be coalesced.

The command also supports breakpoint all and breakpoint breakpoint_number as filters. The former displays the threads that hit all CUDA breakpoints set by the user. The latter displays the threads that hit the CUDA breakpoint breakpoint_number.

(cuda-gdb) info cuda threads breakpoint all BlockIdx ThreadIdx Virtual PC Dev SM Wp Ln Filename Line Kernel 0 (1,0,0) (0,0,0) 0x0000000000948e58 0 11 0 0 infoCommands.cu 12 (1,0,0) (1,0,0) 0x0000000000948e58 0 11 0 1 infoCommands.cu 12 (1,0,0) (2,0,0) 0x0000000000948e58 0 11 0 2 infoCommands.cu 12 (1,0,0) (3,0,0) 0x0000000000948e58 0 11 0 3 infoCommands.cu 12 (1,0,0) (4,0,0) 0x0000000000948e58 0 11 0 4 infoCommands.cu 12 (1,0,0) (5,0,0) 0x0000000000948e58 0 11 0 5 infoCommands.cu 12

(cuda-gdb) info cuda threads breakpoint 2 lane 1 BlockIdx ThreadIdx Virtual PC Dev SM Wp Ln Filename Line Kernel 0 (1,0,0) (1,0,0) 0x0000000000948e58 0 11 0 1 infoCommands.cu 12

8.3.9. info cuda launch trace

This command displays the kernel launch trace for the kernel in focus. The first element in the trace is the kernel in focus. The next element is the kernel that launched this kernel. The trace continues until there is no parent kernel. In that case, the kernel is CPU-launched.

For each kernel in the trace, the command prints the level of the kernel in the trace, the kernel ID, the device ID, the grid Id, the status, the kernel dimensions, the kernel name, and the kernel arguments.

(cuda-gdb) info cuda launch trace Lvl Kernel Dev Grid Status GridDim BlockDim Invocation

A kernel that has been launched but that is not running on the GPU will have a Pending status. A kernel currently running on the GPU will be marked as Active. A kernel waiting to become active again will be displayed as Sleeping. When a kernel has terminated, it is marked as Terminated. For the few cases, when the debugger cannot determine if a kernel is pending or terminated, the status is set to Undetermined.

This command supports filters and the default is kernel all.

Note

With set cuda software_preemption on, no kernel will be reported as active.

8.3.10. info cuda launch children

This command displays the list of non-terminated kernels launched by the kernel in focus. For each kernel, the kernel ID, the device ID, the grid Id, the kernel dimensions, the kernel name, and the kernel parameters are displayed.

(cuda-gdb) info cuda launch children Kernel Dev Grid GridDim BlockDim Invocation

This command supports filters and the default is kernel all.

8.3.11. info cuda contexts

This command enumerates all the CUDA contexts running on all GPUs. A * indicates the context currently in focus. This command shows whether a context is currently active on a device or not.

(cuda-gdb) info cuda contexts Context Dev State 0x080b9518 0 inactive

8.3.12. info cuda managed

This command shows all the static managed variables on the device or on the host depending on the focus.

(cuda-gdb) info cuda managed Static managed variables on device 0 are: managed_var = 3 managed_consts = {one = 1, e = 2.71000004, pi = 3.1400000000000001}

8.4. Disassembly

The device SASS code can be disassembled using the standard GDB disassembly instructions such as x/i and display/i.

(cuda-gdb) x/4i $pc-32 0xa689a8 <acos_main(acosParams)+824>: MOV R0, c[0x0][0x34] 0xa689b8 <acos_main(acosParams)+840>: MOV R3, c[0x0][0x28] 0xa689c0 <acos_main(acosParams)+848>: IMUL R2, R0, R3 => 0xa689c8 <acos_main(acosParams)+856>: MOV R0, c[0x0][0x28]

Note

For disassembly instruction to work properly, cuobjdump must be installed and present in your $PATH.

In the disassembly view, the current pc is prefixed with =>. For Maxwell (SM 5.0) and newer architectures, if an instruction triggers an exception it will be prefixed with *>. If the pc and errorpc are the same instruction it will be prefixed with *=>.

For example, consider the following exception:

CUDA Exception: Warp Illegal Address The exception was triggered at PC 0x555555c08620 (memexceptions_kernel.cu:17)

Thread 1 "memexceptions" received signal CUDA_EXCEPTION_14, Warp Illegal Address. [Switching focus to CUDA kernel 0, grid 1, block (0,0,0), thread (0,0,0), device 0, sm 0, warp 0, lane 0] 0x0000555555c08fb0 in exception_kernel<<<(1,1,1),(1,1,1)>>> (data=0x7fffccc00000, exception=MMU_FAULT) at memexceptions_kernel.cu:50 50 } (cuda-gdb)

The disas command can be used to view both the PC and the error PC that triggered the exception.

(cuda-gdb) disas $pc,+16 Dump of assembler code from 0x555555c08fb0 to 0x555555c08fc0: => 0x0000555555c08fb0 <_Z16exception_kernelPv11exception_t+3504>: ERRBAR End of assembler dump.

(cuda-gdb) disas $errorpc,+16 Dump of assembler code from 0x555555c08620 to 0x555555c08630: *> 0x0000555555c08620 <_Z16exception_kernelPv11exception_t+1056>: ST.E.U8.STRONG.SYS [R6.64], R5 End of assembler dump.

8.5. Registers

The device registers code can be inspected/modified using the standard GDB commands such as info registers.

(cuda-gdb) info registers R0R0 R0R1 R2R2 R2R3 R0 0xf0 240 R1 0xfffc48 16776264 R2 0x7800 30720 R3 0x80 128

The registers are also accessible as $R<regnum> built-in variables, for example:

(cuda-gdb) printf "%d %d\n", R0∗R0*R0∗R3, $R2 30720 30720

Values of predicate and CC registers can be inspecting by printing system registers group or by using their respective pseudo-names: $P0..$P6 and $CC.

(cuda-gdb) info registers system P0 0x1 1 P1 0x1 1 P2 0x0 0 P3 0x0 0 P4 0x0 0 P5 0x0 0 P6 0x1 1 CC 0x0 0

8.6. Const banks

Memory allocated in the constant address space of GPU memory resides in two dimensional arrays called constant banks. Constant banks are noted c[X][Y] where X is the bank number and Y the offset. The memory address of a given bank/offset pair is obtained via the convenience function $_cuda_const_bank(bank, offset).

(cuda-gdb) disass $pc,+16 Dump of assembler code from 0x7fffd5043d40 to 0x7fffd5043d50: => 0x00007fffd5043d40 <_Z9acos_main10acosParams+1856>: MOV R0, c[0x0][0xc] End of assembler dump. (cuda-gdb) p *$_cuda_const_bank(0x0,0xc) $1 = 8

9. Event Notifications

As the application is making forward progress, CUDA-GDB notifies the users about kernel events and context events. Within CUDA-GDB, kernel refers to the device code that executes on the GPU, while context refers to the virtual address space on the GPU for the kernel. You can enable output of CUDA context and kernel events to review the flow of the active contexts and kernels. By default, only context event messages are displayed.

9.1. Context Events

Any time a CUDA context is created, pushed, popped, or destroyed by the application, CUDA-GDB can optionally display a notification message. The message includes the context id and the device id to which the context belongs.

[Context Create of context 0xad2fe60 on Device 0] [Context Destroy of context 0xad2fe60 on Device 0]

By default, context event notification is disabled. The context event notification policy is controlled with the context_events option.

9.2. Kernel Events

Any time CUDA-GDB is made aware of the launch or the termination of a CUDA kernel, a notification message can be displayed. The message includes the kernel id, the kernel name, and the device to which the kernel belongs.

[Launch of CUDA Kernel 1 (kernel3) on Device 0] [Termination of CUDA Kernel 1 (kernel3) on Device 0]

The kernel event notification policy is controlled with kernel_events and kernel_events_depth options.

Possible options are:

none

no kernel, application or system (default)

application

kernel launched by the user application

system

any kernel launched by the driver, such as memset

all

any kernel, application and system

10. Automatic Error Checking

10.1. Checking API Errors

CUDA-GDB can automatically check the return code of any driver API or runtime API call. If the return code indicates an error, the debugger will stop or warn the user.

The behavior is controlled with the set cuda api_failures option. Three modes are supported:

Note

The success return code and other non-error return codes are ignored. For the driver API, those are: CUDA_SUCCESS and CUDA_ERROR_NOT_READY. For the runtime API, they are cudaSuccess and cudaErrorNotReady.

10.2. GPU Error Reporting

With improved GPU error reporting in CUDA-GDB, application bugs are now easier to identify and easy to fix. The following table shows the new errors that are reported on GPUs with compute capability sm_20 and higher.

Note

Continuing the execution of your application after these errors are found can lead to application termination or indeterminate results.

Note

Warp errors may result in instructions to continue executing before the exception is recognized and reported. The reported $errorpc shall contain the precise address of the instruction that caused the exception. If the warp exits after the instruction causing exception has executed, but before the exception has been recognized and reported, it may result in the exception not being reported. CUDA-GDB relies on an active warp present on the device in order to report exceptions. To help avoid this scenario of unreported exceptions:

CUDA Exception Codes

Exception Code Precision of the Error Scope of the Error Description
CUDA_EXCEPTION_0 : “Device Unknown Exception” Unknown Global error on the GPU This is a global GPU error caused by the application which does not match any of the listed error codes below. This should be a rare occurrence. Potentially, this may be due to Device Hardware Stack overflows or a kernel generating an exception very close to its termination.
CUDA_EXCEPTION_1 : “Deprecated” Deprecated Deprecated This exception is deprecated and should be treated as CUDA_EXCEPTION_0.
CUDA_EXCEPTION_2 : “Lane User Stack Overflow” Precise Per lane/thread error This occurs when a thread exceeds its stack memory limit.
CUDA_EXCEPTION_3 : “Device Hardware Stack Overflow” Precise Global error on the GPU This occurs when the application triggers a global hardware stack overflow. The main cause of this error is large amounts of divergence in the presence of function calls.
CUDA_EXCEPTION_4 : “Warp Illegal Instruction” Precise Warp error This occurs when any thread within a warp has executed an illegal instruction.
CUDA_EXCEPTION_5 : “Warp Out-of-range Address” Precise Warp error This occurs when any thread within a warp accesses an address that is outside the valid range of local or shared memory regions.
CUDA_EXCEPTION_6 : “Warp Misaligned Address” Precise Warp error This occurs when any thread within a warp accesses an address in the local or shared memory segments that is not correctly aligned.
CUDA_EXCEPTION_7 : “Warp Invalid Address Space” Precise Warp error This occurs when any thread within a warp executes an instruction that accesses a memory space not permitted for that instruction.
CUDA_EXCEPTION_8 : “Warp Invalid PC” Precise Warp error This occurs when any thread within a warp advances its PC beyond the 40-bit address space.
CUDA_EXCEPTION_9 : “Warp Hardware Stack Overflow” Precise Warp error This occurs when any thread in a warp triggers a hardware stack overflow. This should be a rare occurrence.
CUDA_EXCEPTION_10 : “Device Illegal Address” Precise Global error This occurs when a thread accesses an illegal(out of bounds) global address.
CUDA_EXCEPTION_11 : “Deprecated” Deprecated Deprecated This exception is deprecated and should be treated as CUDA_EXCEPTION_0.
CUDA_EXCEPTION_12 : “Warp Assert” Precise Per warp This occurs when any thread in the warp hits a device side assertion.
CUDA_EXCEPTION_13 : “Deprecated” Deprecated Deprecated This exception is deprecated and should be treated as CUDA_EXCEPTION_0.
CUDA_EXCEPTION_14 : “Warp Illegal Address” Precise Per warp This occurs when a thread accesses an illegal(out of bounds) global/local/shared address.
CUDA_EXCEPTION_15 : “Invalid Managed Memory Access” Precise Per host thread This occurs when a host thread attempts to access managed memory currently used by the GPU.
CUDA_EXCEPTION_16 : “Deprecated” Deprecated Deprecated This exception is deprecated and should be treated as CUDA_EXCEPTION_0.
CUDA_EXCEPTION_17 : “Cluster Target Block Not Present” Not precise Per Cuda Cluster This occurs when any thread within a block accesses another block that is outside the valid range of blocks belonging to the cluster.
CUDA_EXCEPTION_18 : “Cluster Out-of-range Address” Not precise Per Cuda Cluster This occurs when any thread within a block accesses an address that is outside the valid range of shared memory regions belonging to the cluster.
CUDA_EXCEPTION_19 : “Warp Stack Canary” Precise Per warp This occurs when stack canaries are enabled for device code and the check failed.
CUDA_EXCEPTION_20 : “Tensor Memory Access” Precise Per warp This occurs when tensor memory access checks are enabled and the check failed.
CUDA_EXCEPTION_21 : “Tensor Memory Leak” Precise Per warp This occurs when tensor memory has been allocated but not deallocated at kernel exit.

10.3. Autostep

Autostep is a command to increase the precision of CUDA exceptions to the exact lane and instruction, when they would not have been otherwise.

Under normal execution, an exception may be reported several instructions after the exception occurred, or the exact thread where an exception occurred may not be known unless the exception is a lane error. However, the precise origin of the exception can be determined if the program is being single-stepped when the exception occurs. Single- stepping manually is a slow and tedious process; stepping takes much longer than normal execution and the user has to single-step each warp individually.

Autostep aides the user by allowing them to specify sections of code where they suspect an exception could occur, and these sections are automatically and transparently single- stepped the program is running. The rest of the program is executed normally to minimize the slow-down caused by single-stepping. The precise origin of an exception will be reported if the exception occurs within these sections. Thus the exact instruction and thread where an exception occurred can be found quickly and with much less effort by using autostep.

Note

The autostep command is implemented using breakpoints. If an autostep is set on the current PC, the autostep won’t be evaluated until the next time the PC is encountered.

Autostep Usage

autostep [LOCATION] autostep [LOCATION] for LENGTH [lines|instructions]

If an autostep is encountered while another autostep is being executed, then the second autostep is ignored.

If an autostep is set before the location of a memory error and no memory error is hit, then it is possible that the chosen window is too small. This may be caused by the presence of function calls between the address of the autostep location and the instruction that triggers the memory error. In that situation, either increase the size of the window to make sure that the faulty instruction is included, or move to the autostep location to an instruction that will be executed closer in time to the faulty instruction.

Related Commands

Autosteps and breakpoints share the same numbering so most commands that work with breakpoints will also work with autosteps.

info autosteps shows all breakpoints and autosteps. It is similar to info breakpoints.

(cuda-gdb) info autosteps Num Type Disp Enb Address What 1 autostep keep y 0x0000000000401234 in merge at sort.cu:30 for 49 instructions 3 autostep keep y 0x0000000000489913 in bubble at sort.cu:94 for 11 lines

disable autosteps disables an autostep. It is equivalent to disable breakpoints n.

delete autosteps n deletes an autostep. It is quivalent to delete breakpoints n.

ignore n i tells the debugger to not single-step the next i times the debugger enters the window for autostep n. This command already exists for breakpoints.

11. Walk-Through Examples

The chapter contains three CUDA-GDB walk-through examples:

11.1. Example: bitreverse

This section presents a walk-through of CUDA-GDB by debugging a sample application–called bitreverse–that performs a simple 8 bit reversal on a data set.

Source Code

1 #include <stdio.h> 2 #include <stdlib.h> 3 4 // Simple 8-bit bit reversal Compute test 5 6 #define N 256 7 8 global void bitreverse(void *data) { 9 unsigned int idata = (unsigned int)data; 10 extern shared int array[]; 11 12 array[threadIdx.x] = idata[threadIdx.x]; 13 14 array[threadIdx.x] = ((0xf0f0f0f0 & array[threadIdx.x]) >> 4) | 15 ((0x0f0f0f0f & array[threadIdx.x]) << 4); 16 array[threadIdx.x] = ((0xcccccccc & array[threadIdx.x]) >> 2) | 17 ((0x33333333 & array[threadIdx.x]) << 2); 18 array[threadIdx.x] = ((0xaaaaaaaa & array[threadIdx.x]) >> 1) | 19 ((0x55555555 & array[threadIdx.x]) << 1); 20 21 idata[threadIdx.x] = array[threadIdx.x]; 22 } 23 24 int main(void) { 25 void *d = NULL; int i; 26 unsigned int idata[N], odata[N]; 27 28 for (i = 0; i < N; i++) 29 idata[i] = (unsigned int)i; 30 31 cudaMalloc((void**)&d, sizeof(int)*N); 32 cudaMemcpy(d, idata, sizeof(int)*N, 33 cudaMemcpyHostToDevice); 34 35 bitreverse<<<1, N, N*sizeof(int)>>>(d); 36 37 cudaMemcpy(odata, d, sizeof(int)N, 38 cudaMemcpyDeviceToHost); 39 40 for (i = 0; i < N; i++) 41 printf("%u -> %u\n", idata[i], odata[i]); 42 43 cudaFree((void)d); 44 return 0; 45 }

11.1.1. Walking through the Code

  1. Begin by compiling the bitreverse.cu CUDA application for debugging by entering the following command at a shell prompt:
    $ nvcc -g -G bitreverse.cu -o bitreverse
    This command assumes that the source file name is bitreverse.cu and that no additional compiler flags are required for compilation. See also Debug Compilation
  2. Start the CUDA debugger by entering the following command at a shell prompt:
  3. Set breakpoints. Set both the host (main) and GPU (bitreverse) breakpoints here. Also, set a breakpoint at a particular line in the device function (bitreverse.cu:18).
    (cuda-gdb) break main
    Breakpoint 1 at 0x18e1: file bitreverse.cu, line 25.
    (cuda-gdb) break bitreverse
    Breakpoint 2 at 0x18a1: file bitreverse.cu, line 8.
    (cuda-gdb) break 21
    Breakpoint 3 at 0x18ac: file bitreverse.cu, line 21.
  4. Run the CUDA application, and it executes until it reaches the first breakpoint (main) set in the previous step.
    (cuda-gdb) run
    Starting program: /Users/CUDA_User1/docs/bitreverse
    Reading symbols for shared libraries
    ..++........................................................... done
    Breakpoint 1, main () at bitreverse.cu:25
    25 void *d = NULL; int i;
  5. At this point, commands can be entered to advance execution or to print the program state. For this walkthrough, let’s continue until the device kernel is launched.
    (cuda-gdb) continue
    Continuing.
    Reading symbols for shared libraries .. done
    Reading symbols for shared libraries .. done
    [Context Create of context 0x80f200 on Device 0]
    [Launch of CUDA Kernel 0 (bitreverse<<<(1,1,1),(256,1,1)>>>) on Device 0]
    Breakpoint 3 at 0x8667b8: file bitreverse.cu, line 21.
    [Switching focus to CUDA kernel 0, grid 1, block (0,0,0), thread (0,0,0), device 0, sm 0, warp 0, lane 0]
    Breakpoint 2, bitreverse<<<(1,1,1),(256,1,1)>>> (data=0x110000) at bitreverse.cu:9
    9 unsigned int idata = (unsigned int)data;
    CUDA−GDB has detected that a CUDA device kernel has been reached. The debugger prints the current CUDA thread of focus.
  6. Verify the CUDA thread of focus with the info cuda threads command and switch between host thread and the CUDA threads:
    (cuda-gdb) info cuda threads
    BlockIdx ThreadIdx To BlockIdx ThreadIdx Count Virtual PC
    Filename Line
    Kernel 0

#0 0x000019d5 in main () at bitreverse.cu:34
34 bitreverse<<<1, N, N*sizeof(int)>>>(d);
(cuda-gdb) backtrace
#0 0x000019d5 in main () at bitreverse.cu:34
(cuda-gdb) info cuda kernels
Kernel Dev Grid SMs Mask GridDim BlockDim Name Args
0 0 1 0x00000001 (1,1,1) (256,1,1) bitreverse data=0x110000
(cuda-gdb) cuda kernel 0
[Switching focus to CUDA kernel 0, grid 1, block (0,0,0), thread (0,0,0), device 0, sm 0, warp 0, lane 0]
9 unsigned int idata = (unsigned int)data;
(cuda-gdb) backtrace
#0 bitreverse<<<(1,1,1),(256,1,1)>>> (data=0x110000) at bitreverse.cu:9 7. Corroborate this information by printing the block and thread indexes:
(cuda-gdb) print blockIdx
$1 = {x = 0, y = 0}
(cuda-gdb) print threadIdx
$2 = {x = 0, y = 0, z = 0) 8. The grid and block dimensions can also be printed:
(cuda-gdb) print gridDim
$3 = {x = 1, y = 1}
(cuda-gdb) print blockDim
$4 = {x = 256, y = 1, z = 1) 9. Advance kernel execution and verify some data:
(cuda-gdb) next
12 array[threadIdx.x] = idata[threadIdx.x];
(cuda-gdb) next
14 array[threadIdx.x] = ((0xf0f0f0f0 & array[threadIdx.x]) >> 4) |
(cuda-gdb) next
16 array[threadIdx.x] = ((0xcccccccc & array[threadIdx.x]) >> 2) |
(cuda-gdb) next
18 array[threadIdx.x] = ((0xaaaaaaaa & array[threadIdx.x]) >> 1) |
(cuda-gdb) next
Breakpoint 3, bitreverse <<<(1,1),(256,1,1)>>> (data=0x100000) at bitreverse.cu:21
21 idata[threadIdx.x] = array[threadIdx.x];
(cuda-gdb) print array[0]@12
$7 = {0, 128, 64, 192, 32, 160, 96, 224, 16, 144, 80, 208}
(cuda-gdb) print/x array[0]@12
$8 = {0x0, 0x80, 0x40, 0xc0, 0x20, 0xa0, 0x60, 0xe0, 0x10, 0x90, 0x50,
0xd0}
(cuda-gdb) print &data
$9 = (@global void * @parameter *) 0x10
(cuda-gdb) print *(@global void * @parameter *) 0x10
$10 = (@global void * @parameter) 0x100000
The resulting output depends on the current content of the memory location. 10. Since thread (0,0,0) reverses the value of 0, switch to a different thread to show more interesting data:
(cuda-gdb) cuda thread 170
[Switching focus to CUDA kernel 0, grid 1, block (0,0,0), thread
(170,0,0), device 0, sm 0, warp 5, lane 10] 11. Delete the breakpoints and continue the program to completion:
(cuda-gdb) delete breakpoints
Delete all breakpoints? (y or n) y
(cuda-gdb) continue
Continuing.
Program exited normally.
(cuda-gdb)

11.2. Example: autostep

This section shows how to use the autostep command and demonstrates how it helps increase the precision of memory error reporting.

Source Code

1 #define NUM_BLOCKS 8 2 #define THREADS_PER_BLOCK 64 3 4 global void example(int **data) { 5 int value1, value2, value3, value4, value5; 6 int idx1, idx2, idx3; 7 8 idx1 = blockIdx.x * blockDim.x; 9 idx2 = threadIdx.x; 10 idx3 = idx1 + idx2; 11 value1 = *(data[idx1]); 12 value2 = *(data[idx2]); 13 value3 = value1 + value2; 14 value4 = value1 * value2; 15 value5 = value3 + value4; 16 *(data[idx3]) = value5; 17 *(data[idx1]) = value3; 18 *(data[idx2]) = value4; 19 idx1 = idx2 = idx3 = 0; 20 } 21 22 int main(int argc, char *argv[]) { 23 int *host_data[NUM_BLOCKS * THREADS_PER_BLOCK]; 24 int *dev_data; 25 const int zero = 0; 26 27 / Allocate an integer for each thread in each block */ 28 for (int block = 0; block < NUM_BLOCKS; block++) { 29 for (int thread = 0; thread < THREADS_PER_BLOCK; thread++) { 30 int idx = thread + block * THREADS_PER_BLOCK; 31 cudaMalloc(&host_data[idx], sizeof(int)); 32 cudaMemcpy(host_data[idx], &zero, sizeof(int), 33 cudaMemcpyHostToDevice); 34 } 35 } 36 37 /* This inserts an error into block 3, thread 39*/ 38 host_data[3*THREADS_PER_BLOCK + 39] = NULL; 39 40 /* Copy the array of pointers to the device */ 41 cudaMalloc((void**)&dev_data, sizeof(host_data)); 42 cudaMemcpy(dev_data, host_data, sizeof(host_data), cudaMemcpyHostToDevice); 43 44 /* Execute example */ 45 example <<< NUM_BLOCKS, THREADS_PER_BLOCK >>> (dev_data); 46 cudaThreadSynchronize(); 47 }

In this small example, we have an array of pointers to integers, and we want to do some operations on the integers. Suppose, however, that one of the pointers is NULL as shown in line 38. This will cause CUDA_EXCEPTION_10 "Device Illegal Address" to be thrown when we try to access the integer that corresponds with block 3, thread 39. This exception should occur at line 16 when we try to write to that value.

11.2.1. Debugging with Autosteps

  1. Compile the example and start CUDA−GDB as normal. We begin by running the program:
    (cuda-gdb) run
    Starting program: /home/jitud/cudagdb_test/autostep_ex/example
    [Thread debugging using libthread_db enabled] [New Thread 0x7ffff5688700 (LWP 9083)]
    [Context Create of context 0x617270 on Device 0]
    [Launch of CUDA Kernel 0 (example<<<(8,1,1),(64,1,1)>>>) on Device 0]
    Program received signal CUDA_EXCEPTION_10, Device Illegal Address.
    [Switching focus to CUDA kernel 0, grid 1, block (1,0,0), thread (0,0,0), device 0, sm 1, warp 0, lane 0]
    0x0000000000796f60 in example (data=0x200300000) at example.cu:17
    17 *(data[idx1]) = value3;

As expected, we received a CUDA_EXCEPTION_10. However, the reported thread is block 1, thread 0 and the line is 17. Since CUDA_EXCEPTION_10 is a Global error, there is no thread information that is reported, so we would manually have to inspect all 512 threads. 2. Set autosteps. To get more accurate information, we reason that since CUDA_EXCEPTION_10 is a memory access error, it must occur on code that accesses memory. This happens on lines 11, 12, 16, 17, and 18, so we set two autostep windows for those areas:
(cuda-gdb) autostep 11 for 2 lines
Breakpoint 1 at 0x796d18: file example.cu, line 11.
Created autostep of length 2 lines
(cuda-gdb) autostep 16 for 3 lines
Breakpoint 2 at 0x796e90: file example.cu, line 16.
Created autostep of length 3 lines 3. Finally, we run the program again with these autosteps:
(cuda-gdb) run
The program being debugged has been started already.
Start it from the beginning? (y or n) y
[Termination of CUDA Kernel 0 (example<<<(8,1,1),(64,1,1)>>>) on Device 0]
Starting program: /home/jitud/cudagdb_test/autostep_ex/example
[Thread debugging using libthread_db enabled]
[New Thread 0x7ffff5688700 (LWP 9089)]
[Context Create of context 0x617270 on Device 0]
[Launch of CUDA Kernel 1 (example<<<(8,1,1),(64,1,1)>>>) on Device 0]
[Switching focus to CUDA kernel 1, grid 1, block (0,0,0), thread (0,0,0),
device 0, sm 0, warp 0, lane 0]
Program received signal CUDA_EXCEPTION_10, Device Illegal Address.
[Current focus set to CUDA kernel 1, grid 1, block (3,0,0), thread
(32,0,0), device 0, sm 1, warp 3, lane 0]
Autostep precisely caught exception at example.cu:16 (0x796e90)
This time we correctly caught the exception at line 16. Even though CUDA_EXCEPTION_10 is a global error, we have now narrowed it down to a warp error, so we now know that the thread that threw the exception must have been in the same warp as block 3, thread 32.

In this example, we have narrowed down the scope of the error from 512 threads down to 32 threads just by setting two autosteps and re−running the program.

11.3. Example: MPI CUDA Application

For large scale MPI CUDA application debugging, NVIDIA recommends using parallel debuggers supplied by our partners Allinea and Totalview. Both make excellent parallel debuggers with extended support for CUDA. However, for debugging smaller applications, or for debugging just a few processes in a large application, CUDA-GDB can be used.

If the cluster nodes have xterm support, launch CUDA-GDB in the same way you would launch gdb with your job launcher. For example:

$ mpirun -np 4 -host nv1,nv2 xterm -e cuda-gdb a.out

You may have to export the DISPLAY variable to make sure that the xterm finds its way back to your display. For example:

$ mpirun -np 4 -host nv1,nv2 -x DISPLAY=host.nvidia.com:0 xterm -e cuda-gdb a.out

Job launchers have different ways of exporting environment variables to the cluster nodes. Consult your job launcher documentation for more details.

When xterm is not supported by your cluster environment, you can insert a spin loop inside your program, ssh to the compute node(s), and attach onto the MPI processes. Somewhere near the start of your program, add a code snippet similar to the following:

{ int i = 0; char host[256]; printf("PID %d on node %s is ready for attach\n", getpid(), host); fflush(stdout); while (0 == i) { sleep(5); } }

Recompile and launch the application. After it starts, ssh to the node(s) of interest and attach to the process using CUDA-GDB. Set the variable i to 1 to break out of the loop:

$ mpirun -np 2 -host nv1,nv2 a.out PID 20060 on node nv1 is ready for attach PID 5488 on node nv2 is ready for attach

$ ssh nv1 [nv1]$ cuda-gdb --pid 5488

$ ssh nv2 [nv2]$ cuda-gdb --pid 20060

For larger applications, you can conditionalize the spin loop based on the MPI rank using the MPI_Comm_rank function.

For devices with compute capability below 6.0, the software preemption workaround described in Multiple Debuggers does not work with MPI applications. For those GPUs, ensure each MPI rank targets a unique GPU.

If CUDA_VISIBLE_DEVICES is set, it may cause problems with the GPU selection logic in the MPI application. It may also prevent CUDA IPC working between GPUs on a node.

12. Tips and Tricks

This section serves as reference to advanced settings and various tips and tricks users of CUDA-GDB can utilize which are not documented elsewhere.

12.1. set cuda break_on_launch

To break on the first instruction of every launched kernel, set the break_on_launch option to application:

(cuda-gdb) set cuda break_on_launch application

Possible options are:

none

no kernel, application or system (default)

application

kernel launched by the user application

system

any kernel launched by the driver, such as memset

all

any kernel, application and system

Those automatic breakpoints are not displayed by the info breakpoints command and are managed separately from individual breakpoints. Turning off the option will not delete other individual breakpoints set to the same address and vice-versa.

12.2. set cuda launch_blocking

When enabled, the kernel launches are synchronous as if the environment variable CUDA_LAUNCH_BLOCKING had been set to 1. Once blocking, the launches are effectively serialized and may be easier to debug.

12.3. set cuda notify

Any time a CUDA event occurs, the debugger needs to be notified. The notification takes place in the form of a signal being sent to a host thread. The host thread to receive that special signal is determined with the set cuda notify option.

12.4. set cuda ptx_cache

Before accessing the value of a variable, the debugger checks whether the variable is live or not at the current PC. On CUDA devices, the variables may not be live all the time and will be reported as “Optimized Out”.

CUDA-GDB offers an option to circumvent this limitation by caching the value of the variable at the PTX register level. Each source variable is compiled into a PTX register, which is later mapped to one or more hardware registers. Using the debug information emitted by the compiler, the debugger may be able cache the value of a PTX register based on the latest hardware register it was mapped to at an earlier time.

This optimization is always correct. When enabled, the cached value will be displayed as the normal value read from an actual hardware register and indicated with the (cached) prefix. The optimization will only kick in while single-stepping the code.

12.5. set cuda single_stepping_optimizations

Single-stepping can take a lot of time. When enabled, this option tells the debugger to use safe tricks to accelerate single-stepping.

12.6. set cuda thread_selection

When the debugger must choose an active thread to focus on, the decision is guided by a heuristics. The set cuda thread_selection guides those heuristics.

12.8. Debugging Docker Containers

When debugging an application within a Docker container, the PTRACE capability needs to be enabled. The user needs to also ensure that the root file system has both read/write permissions set.

To enable the PTRACE capability, add the following to your Docker run command:

12.9. Thread Block Clusters

CUDA applications that make use of Thread Block Clusters will see the cluster index displayed in the CUDA focus. Both cluster index and cluster dimension can be queried by printing the convenience variables clusterIdx and clusterDim.

12.10. Debugging OptiX/RTCore applications

When debugging programs built with OptiX/RTCore, it may be necessary to set the environment variable OPTIX_FORCE_DEPRECATED_LAUNCHER to 1. If breakpoints are unable to be hit, try setting this environment variable before starting your application.

12.11. Debugging on Windows Subsystem for Linux

If you are unable to use the debugger on Windows Subsystem for Linux, make sure the debug interface is enabled by setting the registry key >HKEY_LOCAL_MACHINE\SOFTWARE\NVIDIA Corporation\GPUDebugger\EnableInterface to (DWORD) 1

12.12. Accessing unmanaged device memory from host thread

Special consideration is necessary when accessing unmanaged device memory that was created via the CUDA memory allocation APIs. Consider the following:

CUdeviceptr d_global; cuMemAlloc(&d_global, sizeof(uint32_t));

uint32_t h_global_32 = 0x1234; cuMemcpyHtoD(d_global, &h_global, sizeof(uint32_t));

When examining the contents of d_global directly while focused on a host thread, we will observe incorrect values:

(cuda-gdb) print /x *d_global $1 = 0x0

Users are required to explicitly type cast to the correct address space identifier to examine the unmanaged memory on the device:

(cuda-gdb) print *(@global unsigned long long *)d_global $2 = 0x1234

When focused on a CUDA thread, explicit casts are unnecessary.

13. Supported Platforms

Host Platform Requirements

CUDA-GDB is supported on all the platforms supported by the CUDA toolkit with which it is shipped. See the CUDA Toolkit release notes for more information.

GPU Requirements

Debugging is supported on all CUDA-capable GPUs supported by the current CUDA release.

GDB Python integration

GDB Python integration is supported in cuda-gdb with a multiple builds mechanism in order to support multiple python3 interpreters across different platforms. The cuda-gdb program is a shell script that selects the associated supported cuda-gdb binary based on the version of python available on the system. Support exists for the following Python versions: Python 3.8, Python 3.9, Python 3.10, Python 3.11, and Python 3.12

Windows Subsystem for Linux (WSL)

14. Common Issues on Supported Operating Systems

The following are known issues with the current release on supported operating systems and how to fix them.

Python not initialized

This happens due to a missing Python 3.x library on the machine, installing it fixes the issue. This can also be caused by having a mismatched major.minor version of libpython installed with the default python3 interpreter in PATH. A libpython version matching the default python3 interpreter in PATH must be available. The libpython version can be determined with the python3 --version command. For example, the following command would tell us that a libpython3.8.so* needs to be installed in a default library search path:

$ python3 --version Python 3.8.10

Specific commands to install the proper libpython are below.

RHEL 8/9

$ sudo yum -y install python3-libs

Debian 10/11/12

$ sudo apt-get -y install libpython3-stdlib

Fedora 39

$ sudo yum -y install python3-libs

OpenSUSE 15

$ sudo zypper install -y libpython3

Ubuntu 20.04/22.04

$ sudo apt-get -y install python3.8 $ sudo apt-get -y install libpython3.8

15. Known Issues

The following are known issues with the current release.

Because inlined code blocks may have multiple exit points, under the hood, the debugger steps every single instruction until an exit point is reached, which incurs considerable cost for large routines. The following actions are recommended to avoid this problem:

16. Notices

16.1. Notice

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16.2. OpenCL

OpenCL is a trademark of Apple Inc. used under license to the Khronos Group Inc.

16.3. Trademarks

NVIDIA and the NVIDIA logo are trademarks or registered trademarks of NVIDIA Corporation in the U.S. and other countries. Other company and product names may be trademarks of the respective companies with which they are associated.