Debugging CUDA Python with the the CUDA Simulator¶
Numba includes a CUDA Simulator that implements most of the semantics in CUDA Python using the Python interpreter and some additional Python code. This can be used to debug CUDA Python code, either by adding print statements to your code, or by using the debugger to step through the execution of an individual thread.
Execution of kernels is performed by the simulator one block at a time. One thread is spawned for each thread in the block, and scheduling of the execution of these threads is left up to the operating system.
Using the simulator¶
The simulator is enabled by setting the environment variable
NUMBA_ENABLE_CUDASIM to 1. CUDA Python code may then be executed as
normal. The easiest way to use the debugger inside a kernel is to only stop a
single thread, otherwise the interaction with the debugger is difficult to
handle. For example, the kernel below will stop in the thread
@cuda.jit def vec_add(A, B, out): x = cuda.threadIdx.x bx = cuda.blockIdx.x bdx = cuda.blockDim.x if x == 1 and bx == 3: from pdb import set_trace; set_trace() i = bx * bdx + x out[i] = A[i] + B[i]
when invoked with a one-dimensional grid and one-dimensional blocks.
The simulator aims to provide as complete a simulation of execution on a real GPU as possible - in particular, the following are supported:
- Atomic operations
- Constant memory
- Local memory
- Shared memory: declarations of shared memory arrays must be on separate source lines, since the simulator uses source line information to keep track of allocations of shared memory across threads.
syncthreads()is supported - however, in the case where divergent threads enter different
syncthreads()calls, the launch will not fail, but unexpected behaviour will occur. A future version of the simulator may detect this condition.
- The stream API is supported, but all operations occur sequentially and synchronously, unlike on a real device. Synchronising on a stream is therefore a no-op.
- The event API is also supported, but provides no meaningful timing information.
- Data transfer to and from the GPU - in particular, creating array objects with
device_array_like(). The APIs for pinned memory
pinned_array()are also supported, but no pinning takes place.
- The driver API implementation of the list of GPU contexts (
cuda.cudadrv.devices.gpus) is supported, and reports a single GPU context. This context can be closed and reset as the real one would.
detect()function is supported, and reports one device called SIMULATOR.
Some limitations of the simulator include:
- It does not perform type checking/type inference. If any argument types to a jitted function are incorrect, or if the specification of the type of any local variables are incorrect, this will not be detected by the simulator.
- Only one GPU is simulated.
- Multithreaded accesses to a single GPU are not supported, and will result in unexpected behaviour.
- Most of the driver API is unimplemented.
- It is not possible to link PTX code with CUDA Python functions.
- Warps and warp-level operations are not yet implemented.
Obviously, the speed of the simulator is also much lower than that of a real device. It may be necessary to reduce the size of input data and the size of the CUDA grid in order to make debugging with the simulator tractable.