# Built-in Utilities

## Automatic Initialization

The module `pycuda.autoinit`, when imported, automatically performs all the steps necessary to get CUDA ready for submission of compute kernels. It uses `pycuda.tools.make_default_context()` to create a compute context.

pycuda.autoinit.device

  An instance of `pycuda.driver.Device` that was used for automatic initialization.

pycuda.autoinit.context

  A default-constructed instance of `pycuda.driver.Context` on `device`. This context is created by calling `pycuda.tools.make_default_context()`.

The module `pycuda.autoprimaryctx` is similar to `pycuda.autoinit`, except that it retains the device primary context instead of creating a new context in `pycuda.tools.make_default_context()`. Notably, it also has `device` and `context` attributes.

## Choice of Device

pycuda.tools.make_default_context()

  Return a `pycuda.driver.Context` instance chosen according to the following rules:

  > - If the environment variable `CUDA_DEVICE` is set, its integer value is used as the device number.

  > - If the file `.cuda-device` is present in the user’s home directory, the integer value of its contents is used as the device number.

  > - Otherwise, all available CUDA devices are tried in a round-robin fashion.

  An error is raised if this does not lead to a usable context.

pycuda.tools.get_default_device(default=0)

  Deprecated. Use `make_default_context()`.

  Return a `pycuda.driver.Device` instance chosen according to the following rules:

  > - If the environment variable `CUDA_DEVICE` is set, its integer value is used as the device number.

  > - If the file `.cuda-device` is present in the user’s home directory, the integer value of its contents is used as the device number.

  > - Otherwise, default is used as the device number.

## Kernel Caching

pycuda.tools.context_dependent_memoize(func)

  This decorator caches the result of the decorated function, if a subsequent occurs in the same `pycuda.driver.Context`. This is useful for caching of kernels.

pycuda.tools.clear_context_caches()

  Empties all context-dependent memoization caches. Also releases all held reference contexts. If it is important to you that the program detaches from its context, you might need to call this function to free all remaining references to your context.

## Testing

pycuda.tools.mark_cuda_test(func)

  This function, meant for use with `pytest`, will mark func with a “cuda” tag and make sure it has a CUDA context available when invoked.

## Device Metadata and Occupancy

class pycuda.tools.DeviceData(dev=None)

  Gives access to more information on a device than is available through `pycuda.driver.Device.get_attribute()`. If dev is None, it defaults to the device returned by `pycuda.driver.Context.get_device()`.

  max_threads
  warp_size
  warps_per_mp
  thread_blocks_per_mp
  registers
  shared_memory
  smem_granularity

    The number of threads that participate in banked, simultaneous access to shared memory.

  smem_alloc_granularity

    The size of the smallest possible (non-empty) shared memory allocation.

  align_bytes(word_size=4)

    The distance between global memory base addresses that allow accesses of word-size word_size bytes to get coalesced.

  align(bytes, word_size=4)

    Round up bytes to the next alignment boundary as given by `align_bytes()`.

  align_words(word_size)

    Return self.align_bytes(word_size)/word_size, while checking that the division did not yield a remainder.

  align_dtype(elements, dtype_size)

    Round up elements to the next alignment boundary as given by `align_bytes()`, where each element is assumed to be dtype_size bytes large.

  static make_valid_tex_channel_count(size)

    Round up size to a valid texture channel count.

class pycuda.tools.OccupancyRecord(devdata, threads, shared_mem=0, registers=0)

  Calculate occupancy for a given kernel workload characterized by

  - thread count of threads

  - shared memory use of shared_mem bytes

  - register use of registers 32-bit registers

  tb_per_mp

    How many thread blocks execute on each multiprocessor.

  limited_by

    What `tb_per_mp` is limited by. One of “device”, “warps”, “regs”, “smem”.

  warps_per_mp

    How many warps execute on each multiprocessor.

  occupancy

    A float value between 0 and 1 indicating how much of each multiprocessor’s scheduling capability is occupied by the kernel.

## Memory Pools

The functions `pycuda.driver.mem_alloc()` and `pycuda.driver.pagelocked_empty()` can consume a fairly large amount of processing time if they are invoked very frequently. For example, code based on `pycuda.gpuarray.GPUArray` can easily run into this issue because a fresh memory area is allocated for each intermediate result. Memory pools are a remedy for this problem based on the observation that often many of the block allocations are of the same sizes as previously used ones.

Then, instead of fully returning the memory to the system and incurring the associated reallocation overhead, the pool holds on to the memory and uses it to satisfy future allocations of similarly-sized blocks. The pool reacts appropriately to out-of-memory conditions as long as all memory allocations are made through it. Allocations performed from outside of the pool may run into spurious out-of-memory conditions due to the pool owning much or all of the available memory.

### Device-based Memory Pool

class pycuda.tools.PooledDeviceAllocation

  An object representing a `DeviceMemoryPool`-based allocation of linear device memory. Once this object is deleted, its associated device memory is freed. `PooledDeviceAllocation` instances can be cast to `int`, yielding the starting address of the device memory allocated.

  free()

    Explicitly return the memory held by self to the associated memory pool.

  __len__()

    Return the size of the allocated memory in bytes.

class pycuda.tools.DeviceMemoryPool

  A memory pool for linear device memory as allocated using `pycuda.driver.mem_alloc()`. (see Memory Pools)

  held_blocks

    The number of unused blocks being held by this pool.

  active_blocks

    The number of blocks in active use that have been allocated through this pool.

  managed_bytes

    “Managed” memory is “active” and “held” memory.

  active_bytes

    “Active” bytes are bytes under the control of the application. This may be smaller than the actual allocated size reflected in `managed_bytes`.

  allocate(size)

    Return a `PooledDeviceAllocation` of size bytes.

  free_held()

    Free all unused memory that the pool is currently holding.

  stop_holding()

    Instruct the memory to start immediately freeing memory returned to it, instead of holding it for future allocations. Implicitly calls `free_held()`. This is useful as a cleanup action when a memory pool falls out of use.

### Memory Pool for pagelocked memory

class pycuda.tools.PooledHostAllocation

  An object representing a `PageLockedMemoryPool`-based allocation of linear device memory. Once this object is deleted, its associated device memory is freed.

  free()

    Explicitly return the memory held by self to the associated memory pool.

  __len__()

    Return the size of the allocated memory in bytes.

class pycuda.tools.PageLockedAllocator(flags=0)

  Specifies the set of `pycuda.driver.host_alloc_flags` used in its associated `PageLockedMemoryPool`.

class pycuda.tools.PageLockedMemoryPool(allocator=PageLockedAllocator())

  A memory pool for pagelocked host memory as allocated using `pycuda.driver.pagelocked_empty()`. (see Memory Pools)

  held_blocks

    The number of unused blocks being held by this pool.

  active_blocks

    The number of blocks in active use that have been allocated through this pool.

  allocate(shape, dtype, order='C')

    Return an uninitialized (“empty”) `numpy.ndarray` with the given shape, dtype, and order. This array will be backed by a `PooledHostAllocation`, which can be found as the `.base` attribute of the array.

  free_held()

    Free all unused memory that the pool is currently holding.

  stop_holding()

    Instruct the memory to start immediately freeing memory returned to it, instead of holding it for future allocations. Implicitly calls `free_held()`. This is useful as a cleanup action when a memory pool falls out of use.