mirror of
https://github.com/smyalygames/FiniteVolumeGPU.git
synced 2025-11-29 04:03:34 +01:00
139 lines
4.5 KiB
Python
139 lines
4.5 KiB
Python
import logging
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import numpy as np
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import pycuda.gpuarray
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import pycuda.driver as cuda
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from pycuda.tools import PageLockedMemoryPool
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class CudaArray2D:
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"""
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Class that holds 2D CUDA data
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"""
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def __init__(self, stream, nx, ny, x_halo, y_halo, cpu_data=None, dtype=np.float32):
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"""
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Uploads initial data to the CUDA device
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"""
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self.logger = logging.getLogger(__name__)
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self.nx = nx
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self.ny = ny
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self.x_halo = x_halo
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self.y_halo = y_halo
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nx_halo = nx + 2 * x_halo
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ny_halo = ny + 2 * y_halo
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# self.logger.debug("Allocating [%dx%d] buffer", self.nx, self.ny)
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# Should perhaps use pycuda.driver.mem_alloc_data.pitch() here
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self.data = pycuda.gpuarray.zeros((ny_halo, nx_halo), dtype)
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# For returning to download
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self.memorypool = PageLockedMemoryPool()
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# If we don't have any data, just allocate and return
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if cpu_data is None:
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return
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# Make sure data is in proper format
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if cpu_data.shape != (ny_halo, nx_halo) and cpu_data.shape != (self.ny, self.nx):
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raise ValueError(
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f"Wrong shape of data {str(cpu_data.shape)} vs {str((self.ny, self.nx))} / {str((ny_halo, nx_halo))}")
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if cpu_data.itemsize != 4:
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raise ValueError("Wrong size of data type")
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if np.isfortran(cpu_data):
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raise TypeError("Wrong datatype (Fortran, expected C)")
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# Create a copy object from host to device
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x = (nx_halo - cpu_data.shape[1]) // 2
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y = (ny_halo - cpu_data.shape[0]) // 2
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self.upload(stream, cpu_data, extent=[x, y, cpu_data.shape[1], cpu_data.shape[0]])
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# self.logger.debug("Buffer <%s> [%dx%d]: Allocated ", int(self.data.gpudata), self.nx, self.ny)
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def __del__(self, *args):
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# self.logger.debug("Buffer <%s> [%dx%d]: Releasing ", int(self.data.gpudata), self.nx, self.ny)
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self.data.gpudata.free()
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self.data = None
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def download(self, stream, cpu_data=None, asynch=False, extent=None):
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"""
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Enables downloading data from GPU to Python
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"""
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if extent is None:
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x = self.x_halo
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y = self.y_halo
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nx = self.nx
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ny = self.ny
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else:
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x, y, nx, ny = extent
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if cpu_data is None:
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# self.logger.debug("Downloading [%dx%d] buffer", self.nx, self.ny)
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# Allocate host memory
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# The following fails, don't know why (crashes python)
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cpu_data = cuda.pagelocked_empty((int(ny), int(nx)), dtype=np.float32,
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mem_flags=cuda.host_alloc_flags.PORTABLE)
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# Non-pagelocked: cpu_data = np.empty((ny, nx), dtype=np.float32)
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# cpu_data = self.memorypool.allocate((ny, nx), dtype=np.float32)
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assert nx == cpu_data.shape[1]
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assert ny == cpu_data.shape[0]
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assert x + nx <= self.nx + 2 * self.x_halo
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assert y + ny <= self.ny + 2 * self.y_halo
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# Create a copy object from device to host
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copy = cuda.Memcpy2D()
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copy.set_src_device(self.data.gpudata)
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copy.set_dst_host(cpu_data)
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# Set offsets and pitch of a source
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copy.src_x_in_bytes = int(x) * self.data.strides[1]
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copy.src_y = int(y)
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copy.src_pitch = self.data.strides[0]
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# Set width in bytes to copy for each row and
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# number of rows to copy
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copy.width_in_bytes = int(nx) * cpu_data.itemsize
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copy.height = int(ny)
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copy(stream)
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if not asynch:
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stream.synchronize()
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return cpu_data
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def upload(self, stream, cpu_data, extent=None):
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if extent is None:
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x = self.x_halo
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y = self.y_halo
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nx = self.nx
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ny = self.ny
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else:
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x, y, nx, ny = extent
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assert (nx == cpu_data.shape[1])
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assert (ny == cpu_data.shape[0])
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assert (x + nx <= self.nx + 2 * self.x_halo)
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assert (y + ny <= self.ny + 2 * self.y_halo)
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# Create a copy object from device to host
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copy = cuda.Memcpy2D()
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copy.set_dst_device(self.data.gpudata)
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copy.set_src_host(cpu_data)
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# Set offsets and pitch of a source
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copy.dst_x_in_bytes = int(x) * self.data.strides[1]
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copy.dst_y = int(y)
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copy.dst_pitch = self.data.strides[0]
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# Set width in bytes to copy for each row and
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# number of rows to copy
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copy.width_in_bytes = int(nx) * cpu_data.itemsize
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copy.height = int(ny)
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copy(stream) |