Built-in Utilities

Automatic Initialization

This module, when imported, automatically performs all the steps necessary to get CUDA ready for submission of compute kernels. When imported, this module will automatically initialize CUDA and create a pycuda.driver.Context on the device.

pycuda.autoinit.device
An instance of pycuda.driver.Device that was used for automatic initialization. The appropriate device is found by calling pycuda.tools.get_default_device().
pycuda.autoinit.context
A default-constructed instance of pycuda.driver.Context on device.

Choice of Device

pycuda.tools.get_default_device(default=0)

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.

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.
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 (and long), 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.
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.