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https://github.com/smyalygames/FiniteVolumeGPU_HIP.git
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652 lines
28 KiB
Python
652 lines
28 KiB
Python
# -*- coding: utf-8 -*-
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"""
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This python module implements the 2nd order HLL flux
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Copyright (C) 2016 SINTEF ICT
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This program is free software: you can redistribute it and/or modify
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it under the terms of the GNU General Public License as published by
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the Free Software Foundation, either version 3 of the License, or
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(at your option) any later version.
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This program is distributed in the hope that it will be useful,
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but WITHOUT ANY WARRANTY; without even the implied warranty of
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MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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GNU General Public License for more details.
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You should have received a copy of the GNU General Public License
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along with this program. If not, see <http://www.gnu.org/licenses/>.
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"""
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import os
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import sys
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#Import packages we need
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from GPUSimulators import Simulator, Common
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from GPUSimulators.Simulator import BaseSimulator, BoundaryCondition
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import numpy as np
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import ctypes
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#from pycuda import gpuarray
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from hip import hip,hiprtc
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from hip import hipblas
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def hip_check(call_result):
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err = call_result[0]
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result = call_result[1:]
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if len(result) == 1:
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result = result[0]
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if isinstance(err, hip.hipError_t) and err != hip.hipError_t.hipSuccess:
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raise RuntimeError(str(err))
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elif (
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isinstance(err, hiprtc.hiprtcResult)
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and err != hiprtc.hiprtcResult.HIPRTC_SUCCESS
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):
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raise RuntimeError(str(err))
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return result
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"""
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Class that solves the SW equations using the Forward-Backward linear scheme
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"""
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class EE2D_KP07_dimsplit (BaseSimulator):
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"""
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Initialization routine
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rho: Density
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rho_u: Momentum along x-axis
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rho_v: Momentum along y-axis
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E: energy
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nx: Number of cells along x-axis
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ny: Number of cells along y-axis
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dx: Grid cell spacing along x-axis
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dy: Grid cell spacing along y-axis
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dt: Size of each timestep
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g: Gravitational constant
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gamma: Gas constant
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p: pressure
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"""
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def __init__(self,
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context,
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rho, rho_u, rho_v, E,
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nx, ny,
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dx, dy,
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g,
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gamma,
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theta=1.3,
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cfl_scale=0.9,
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boundary_conditions=BoundaryCondition(),
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block_width=16, block_height=8):
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# Call super constructor
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super().__init__(context,
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nx, ny,
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dx, dy,
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boundary_conditions,
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cfl_scale,
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2,
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block_width, block_height)
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self.g = np.float32(g)
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self.gamma = np.float32(gamma)
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self.theta = np.float32(theta)
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#Get cuda kernels
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""" Cuda
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module = context.get_module("cuda/EE2D_KP07_dimsplit.cu.hip",
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defines={
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'BLOCK_WIDTH': self.block_size[0],
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'BLOCK_HEIGHT': self.block_size[1]
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},
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compile_args={
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'no_extern_c': True,
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'options': ["--use_fast_math"],
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},
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jit_compile_args={})
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#compile and load to the device
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self.kernel = module.get_function("KP07DimsplitKernel")
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self.kernel.prepare("iiffffffiiPiPiPiPiPiPiPiPiPiiii")
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"""
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current_dir = os.path.dirname(os.path.abspath(__file__))
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# Specify the relative path to the "cuda" directory
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cuda_dir = os.path.join(current_dir, 'cuda')
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#source code
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kernel_file_path = os.path.abspath(os.path.join(cuda_dir, 'EE2D_KP07_dimsplit.cu.hip'))
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with open(kernel_file_path, 'r') as file:
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kernel_source = file.read()
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#headers
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#common.h
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header_file_path = os.path.abspath(os.path.join(cuda_dir, 'common.h'))
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with open(header_file_path, 'r') as file:
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header_common = file.read()
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#EulerCommon.h
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header_file_path = os.path.abspath(os.path.join(cuda_dir, 'EulerCommon.h'))
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with open(header_file_path, 'r') as file:
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header_EulerCommon = file.read()
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#limiters.h
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header_file_path = os.path.abspath(os.path.join(cuda_dir, 'limiters.h'))
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with open(header_file_path, 'r') as file:
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header_limiters = file.read()
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#hip.hiprtc.hiprtcCreateProgram(const char *src, const char *name, int numHeaders, headers, includeNames)
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prog = hip_check(hiprtc.hiprtcCreateProgram(kernel_source.encode(), b"KP07DimsplitKernel", 3, [header_common.encode(),header_EulerCommon.encode(),header_limiters.encode()], [b"common.h",b"EulerCommon.h",b"limiters.h"]))
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# Check if the program is created successfully
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if prog is not None:
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print("--This is <EE2D_KP07_dimsplit.cu.hip>")
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print("--HIPRTC program created successfully")
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print()
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else:
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print("--Failed to create HIPRTC program")
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print("--I stop:", err)
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exit()
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#extract the arch of the device
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props = hip.hipDeviceProp_t()
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hip_check(hip.hipGetDeviceProperties(props,0)) #only one device 0
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arch = props.gcnArchName
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print(f"Compiling kernel for {arch}")
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cflags = [b"--offload-arch="+arch, b"-O2", b"-D BLOCK_WIDTH="+ str(self.block_size[0]).encode(), b"-D BLOCK_HEIGHT=" + str(self.block_size[1]).encode()]
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err, = hiprtc.hiprtcCompileProgram(prog, len(cflags), cflags)
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# Check if the program is compiled successfully
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if err is not None:
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print("--Compilation:", err)
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print("--The program is compiled successfully")
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else:
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print("--Compilation:", err)
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print("--Failed to compile the program")
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print("--I stop:", err)
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if err != hiprtc.hiprtcResult.HIPRTC_SUCCESS:
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log_size = hip_check(hiprtc.hiprtcGetProgramLogSize(prog))
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log = bytearray(log_size)
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hip_check(hiprtc.hiprtcGetProgramLog(prog, log))
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raise RuntimeError(log.decode())
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code_size = hip_check(hiprtc.hiprtcGetCodeSize(prog))
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code = bytearray(code_size)
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hip_check(hiprtc.hiprtcGetCode(prog, code))
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#Load the code as a module
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self.module = hip_check(hip.hipModuleLoadData(code))
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#Get the device kernel named "KP07DimsplitKernel"
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self.kernel = hip_check(hip.hipModuleGetFunction(self.module, b"KP07DimsplitKernel"))
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print()
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print("--Get the device kernel *KP07DimsplitKernel* is created successfully--")
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print("--kernel", self.kernel)
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print()
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#Create data by uploading to device
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self.u0 = Common.ArakawaA2D(self.stream,
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nx, ny,
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2, 2,
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[rho, rho_u, rho_v, E])
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self.u1 = Common.ArakawaA2D(self.stream,
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nx, ny,
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2, 2,
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[None, None, None, None])
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#self.cfl_data = gpuarray.GPUArray(self.grid_size, dtype=np.float32)
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# init device array cfl_data
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dt_x = np.min(self.dx / (np.abs(rho_u/rho) + np.sqrt(gamma*rho)))
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dt_y = np.min(self.dy / (np.abs(rho_v/rho) + np.sqrt(gamma*rho)))
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self.dt = min(dt_x, dt_y)
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#in HIP, the "DeviceArray" object doesn't have a 'fill' attribute
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#self.cfl_data.fill(self.dt, stream=self.stream)
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grid_dim_x, grid_dim_y, grid_dim_z = self.grid_size
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data_h = np.zeros((grid_dim_x, grid_dim_y), dtype=np.float32)
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num_bytes = data_h.size * data_h.itemsize
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data_h.fill(self.dt)
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self.cfl_data = hip_check(hip.hipMalloc(num_bytes)).configure(
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typestr="float32",shape=(grid_dim_x, grid_dim_y))
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hip_check(hip.hipMemcpyAsync(self.cfl_data,data_h,num_bytes,hip.hipMemcpyKind.hipMemcpyHostToDevice,self.stream))
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#sets the memory region pointed to by x_d to zero asynchronously
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#initiates the memset operation asynchronously
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#hip_check(hip.hipMemsetAsync(self.cfl_data,0,num_bytes,self.stream))
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def substep(self, dt, step_number, external=True, internal=True):
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self.substepDimsplit(0.5*dt, step_number, external, internal)
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def substepDimsplit(self, dt, substep, external, internal):
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u00_strides0 = self.u0[0].data.shape[0]*np.float32().itemsize
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u01_strides0 = self.u0[1].data.shape[0]*np.float32().itemsize
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u02_strides0 = self.u0[2].data.shape[0]*np.float32().itemsize
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u03_strides0 = self.u0[3].data.shape[0]*np.float32().itemsize
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u10_strides0 = self.u1[0].data.shape[0]*np.float32().itemsize
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u11_strides0 = self.u1[1].data.shape[0]*np.float32().itemsize
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u12_strides0 = self.u1[2].data.shape[0]*np.float32().itemsize
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u13_strides0 = self.u1[3].data.shape[0]*np.float32().itemsize
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if external and internal:
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#print("COMPLETE DOMAIN (dt=" + str(dt) + ")")
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""" Cuda
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self.kernel.prepared_async_call(self.grid_size, self.block_size, self.stream,
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self.nx, self.ny,
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self.dx, self.dy, dt,
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self.g,
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self.gamma,
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self.theta,
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substep,
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self.boundary_conditions,
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self.u0[0].data.gpudata, self.u0[0].data.strides[0],
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self.u0[1].data.gpudata, self.u0[1].data.strides[0],
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self.u0[2].data.gpudata, self.u0[2].data.strides[0],
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self.u0[3].data.gpudata, self.u0[3].data.strides[0],
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self.u1[0].data.gpudata, self.u1[0].data.strides[0],
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self.u1[1].data.gpudata, self.u1[1].data.strides[0],
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self.u1[2].data.gpudata, self.u1[2].data.strides[0],
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self.u1[3].data.gpudata, self.u1[3].data.strides[0],
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self.cfl_data.gpudata,
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0, 0,
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self.nx, self.ny)
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"""
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#hip.hipModuleLaunchKernel(f, unsigned int gridDimX, unsigned int gridDimY, unsigned int gridDimZ, unsigned int blockDimX, unsigned int blockDimY, unsigned int blockDimZ, unsigned int sharedMemBytes, stream, kernelParams, extra)
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#The argument grid/block requires 3 components x,y and z. in 2D z=1.
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hip_check(
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hip.hipModuleLaunchKernel(
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self.kernel,
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*self.grid_size, #grid
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*self.block_size, #block
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sharedMemBytes=0, #65536,
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stream=self.stream,
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kernelParams=None,
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extra=( # pass kernel's arguments
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ctypes.c_int(self.nx), ctypes.c_int(self.ny),
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ctypes.c_float(self.dx), ctypes.c_float(self.dy), ctypes.c_float(dt),
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ctypes.c_float(self.g),
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ctypes.c_float(self.gamma),
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ctypes.c_float(self.theta),
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ctypes.c_int(substep),
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ctypes.c_int(self.boundary_conditions),
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self.u0[0].data, ctypes.c_int(u00_strides0),
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self.u0[1].data, ctypes.c_int(u01_strides0),
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self.u0[2].data, ctypes.c_int(u02_strides0),
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self.u0[3].data, ctypes.c_int(u03_strides0),
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self.u1[0].data, ctypes.c_int(u10_strides0),
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self.u1[1].data, ctypes.c_int(u11_strides0),
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self.u1[2].data, ctypes.c_int(u12_strides0),
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self.u1[3].data, ctypes.c_int(u13_strides0),
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self.cfl_data,
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ctypes.c_int(0), ctypes.c_int(0),
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ctypes.c_int(self.nx), ctypes.c_int(self.ny),
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)
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)
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)
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#print("--External & Internal: Launching Kernel is ok")
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return
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if external and not internal:
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###################################
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# XXX: Corners are treated twice! #
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###################################
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ns_grid_size = (self.grid_size[0], 1, 1)
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# NORTH
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# (x0, y0) x (x1, y1)
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# (0, ny-y_halo) x (nx, ny)
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""" Cuda
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self.kernel.prepared_async_call(ns_grid_size, self.block_size, self.stream,
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self.nx, self.ny,
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self.dx, self.dy, dt,
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self.g,
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self.gamma,
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self.theta,
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substep,
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self.boundary_conditions,
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self.u0[0].data.gpudata, self.u0[0].data.strides[0],
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self.u0[1].data.gpudata, self.u0[1].data.strides[0],
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self.u0[2].data.gpudata, self.u0[2].data.strides[0],
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self.u0[3].data.gpudata, self.u0[3].data.strides[0],
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self.u1[0].data.gpudata, self.u1[0].data.strides[0],
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self.u1[1].data.gpudata, self.u1[1].data.strides[0],
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self.u1[2].data.gpudata, self.u1[2].data.strides[0],
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self.u1[3].data.gpudata, self.u1[3].data.strides[0],
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self.cfl_data.gpudata,
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0, self.ny - int(self.u0[0].y_halo),
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self.nx, self.ny)
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"""
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hip_check(
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hip.hipModuleLaunchKernel(
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self.kernel,
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*ns_grid_size, #grid
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*self.block_size, #block
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sharedMemBytes=0, #65536,
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stream=self.stream,
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kernelParams=None,
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extra=( # pass kernel's arguments
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ctypes.c_int(self.nx), ctypes.c_int(self.ny),
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ctypes.c_float(self.dx), ctypes.c_float(self.dy), ctypes.c_float(dt),
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ctypes.c_float(self.g),
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ctypes.c_float(self.gamma),
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ctypes.c_float(self.theta),
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ctypes.c_int(substep),
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ctypes.c_int(self.boundary_conditions),
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self.u0[0].data, ctypes.c_int(u00_strides0),
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self.u0[1].data, ctypes.c_int(u01_strides0),
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self.u0[2].data, ctypes.c_int(u02_strides0),
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self.u0[3].data, ctypes.c_int(u03_strides0),
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self.u1[0].data, ctypes.c_int(u10_strides0),
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self.u1[1].data, ctypes.c_int(u11_strides0),
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self.u1[2].data, ctypes.c_int(u12_strides0),
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self.u1[3].data, ctypes.c_int(u13_strides0),
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self.cfl_data,
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ctypes.c_int(0), ctypes.c_int(self.ny - self.u0[0].y_halo),
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ctypes.c_int(self.nx), ctypes.c_int(self.ny),
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)
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)
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)
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hip_check(hip.hipStreamSynchronize(self.stream))
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#print()
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#print("--I m at the NORTH:")
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#print()
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# SOUTH
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# (x0, y0) x (x1, y1)
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# (0, 0) x (nx, y_halo)
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""" Cuda
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self.kernel.prepared_async_call(ns_grid_size, self.block_size, self.stream,
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self.nx, self.ny,
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self.dx, self.dy, dt,
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self.g,
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self.gamma,
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self.theta,
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substep,
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self.boundary_conditions,
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self.u0[0].data.gpudata, self.u0[0].data.strides[0],
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self.u0[1].data.gpudata, self.u0[1].data.strides[0],
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self.u0[2].data.gpudata, self.u0[2].data.strides[0],
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self.u0[3].data.gpudata, self.u0[3].data.strides[0],
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self.u1[0].data.gpudata, self.u1[0].data.strides[0],
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self.u1[1].data.gpudata, self.u1[1].data.strides[0],
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self.u1[2].data.gpudata, self.u1[2].data.strides[0],
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self.u1[3].data.gpudata, self.u1[3].data.strides[0],
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self.cfl_data.gpudata,
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0, 0,
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self.nx, int(self.u0[0].y_halo))
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"""
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hip_check(
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hip.hipModuleLaunchKernel(
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self.kernel,
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*ns_grid_size, #grid
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*self.block_size, #block
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sharedMemBytes=0, #65536,
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stream=self.stream,
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kernelParams=None,
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extra=( # pass kernel's arguments
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ctypes.c_int(self.nx), ctypes.c_int(self.ny),
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ctypes.c_float(self.dx), ctypes.c_float(self.dy), ctypes.c_float(dt),
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ctypes.c_float(self.g),
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ctypes.c_float(self.gamma),
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ctypes.c_float(self.theta),
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ctypes.c_int(substep),
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ctypes.c_int(self.boundary_conditions),
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self.u0[0].data, ctypes.c_int(u00_strides0),
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self.u0[1].data, ctypes.c_int(u01_strides0),
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self.u0[2].data, ctypes.c_int(u02_strides0),
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self.u0[3].data, ctypes.c_int(u03_strides0),
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self.u1[0].data, ctypes.c_int(u10_strides0),
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self.u1[1].data, ctypes.c_int(u11_strides0),
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self.u1[2].data, ctypes.c_int(u12_strides0),
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self.u1[3].data, ctypes.c_int(u13_strides0),
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self.cfl_data,
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ctypes.c_int(0), ctypes.c_int(0),
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ctypes.c_int(self.nx), ctypes.c_int(self.u0[0].y_halo),
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)
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)
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)
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hip_check(hip.hipStreamSynchronize(self.stream))
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|
|
|
we_grid_size = (1, self.grid_size[1], 1)
|
|
# WEST
|
|
# (x0, y0) x (x1, y1)
|
|
# (0, 0) x (x_halo, ny)
|
|
""" Cuda
|
|
self.kernel.prepared_async_call(we_grid_size, self.block_size, self.stream,
|
|
self.nx, self.ny,
|
|
self.dx, self.dy, dt,
|
|
self.g,
|
|
self.gamma,
|
|
self.theta,
|
|
substep,
|
|
self.boundary_conditions,
|
|
self.u0[0].data.gpudata, self.u0[0].data.strides[0],
|
|
self.u0[1].data.gpudata, self.u0[1].data.strides[0],
|
|
self.u0[2].data.gpudata, self.u0[2].data.strides[0],
|
|
self.u0[3].data.gpudata, self.u0[3].data.strides[0],
|
|
self.u1[0].data.gpudata, self.u1[0].data.strides[0],
|
|
self.u1[1].data.gpudata, self.u1[1].data.strides[0],
|
|
self.u1[2].data.gpudata, self.u1[2].data.strides[0],
|
|
self.u1[3].data.gpudata, self.u1[3].data.strides[0],
|
|
self.cfl_data.gpudata,
|
|
0, 0,
|
|
int(self.u0[0].x_halo), self.ny)
|
|
"""
|
|
|
|
hip_check(
|
|
hip.hipModuleLaunchKernel(
|
|
self.kernel,
|
|
*we_grid_size, #grid
|
|
*self.block_size, #block
|
|
sharedMemBytes=0, #65536,
|
|
stream=self.stream,
|
|
kernelParams=None,
|
|
extra=( # pass kernel's arguments
|
|
ctypes.c_int(self.nx), ctypes.c_int(self.ny),
|
|
ctypes.c_float(self.dx), ctypes.c_float(self.dy), ctypes.c_float(dt),
|
|
ctypes.c_float(self.g),
|
|
ctypes.c_float(self.gamma),
|
|
ctypes.c_float(self.theta),
|
|
ctypes.c_int(substep),
|
|
ctypes.c_int(self.boundary_conditions),
|
|
self.u0[0].data, ctypes.c_int(u00_strides0),
|
|
self.u0[1].data, ctypes.c_int(u01_strides0),
|
|
self.u0[2].data, ctypes.c_int(u02_strides0),
|
|
self.u0[3].data, ctypes.c_int(u03_strides0),
|
|
self.u1[0].data, ctypes.c_int(u10_strides0),
|
|
self.u1[1].data, ctypes.c_int(u11_strides0),
|
|
self.u1[2].data, ctypes.c_int(u12_strides0),
|
|
self.u1[3].data, ctypes.c_int(u13_strides0),
|
|
self.cfl_data,
|
|
ctypes.c_int(0), ctypes.c_int(0),
|
|
ctypes.c_int(self.u0[0].x_halo), ctypes.c_int(self.ny),
|
|
)
|
|
)
|
|
)
|
|
|
|
hip_check(hip.hipStreamSynchronize(self.stream))
|
|
|
|
# EAST
|
|
# (x0, y0) x (x1, y1)
|
|
# (nx-x_halo, 0) x (nx, ny)
|
|
""" Cuda
|
|
self.kernel.prepared_async_call(we_grid_size, self.block_size, self.stream,
|
|
self.nx, self.ny,
|
|
self.dx, self.dy, dt,
|
|
self.g,
|
|
self.gamma,
|
|
self.theta,
|
|
substep,
|
|
self.boundary_conditions,
|
|
self.u0[0].data.gpudata, self.u0[0].data.strides[0],
|
|
self.u0[1].data.gpudata, self.u0[1].data.strides[0],
|
|
self.u0[2].data.gpudata, self.u0[2].data.strides[0],
|
|
self.u0[3].data.gpudata, self.u0[3].data.strides[0],
|
|
self.u1[0].data.gpudata, self.u1[0].data.strides[0],
|
|
self.u1[1].data.gpudata, self.u1[1].data.strides[0],
|
|
self.u1[2].data.gpudata, self.u1[2].data.strides[0],
|
|
self.u1[3].data.gpudata, self.u1[3].data.strides[0],
|
|
self.cfl_data.gpudata,
|
|
self.nx - int(self.u0[0].x_halo), 0,
|
|
self.nx, self.ny)
|
|
"""
|
|
|
|
hip_check(
|
|
hip.hipModuleLaunchKernel(
|
|
self.kernel,
|
|
*we_grid_size, #grid
|
|
*self.block_size, #block
|
|
sharedMemBytes=0, #65536,
|
|
stream=self.stream,
|
|
kernelParams=None,
|
|
extra=( # pass kernel's arguments
|
|
ctypes.c_int(self.nx), ctypes.c_int(self.ny),
|
|
ctypes.c_float(self.dx), ctypes.c_float(self.dy), ctypes.c_float(dt),
|
|
ctypes.c_float(self.g),
|
|
ctypes.c_float(self.gamma),
|
|
ctypes.c_float(self.theta),
|
|
ctypes.c_int(substep),
|
|
ctypes.c_int(self.boundary_conditions),
|
|
self.u0[0].data, ctypes.c_int(u00_strides0),
|
|
self.u0[1].data, ctypes.c_int(u01_strides0),
|
|
self.u0[2].data, ctypes.c_int(u02_strides0),
|
|
self.u0[3].data, ctypes.c_int(u03_strides0),
|
|
self.u1[0].data, ctypes.c_int(u10_strides0),
|
|
self.u1[1].data, ctypes.c_int(u11_strides0),
|
|
self.u1[2].data, ctypes.c_int(u12_strides0),
|
|
self.u1[3].data, ctypes.c_int(u13_strides0),
|
|
self.cfl_data,
|
|
ctypes.c_int(self.nx - self.u0[0].x_halo), ctypes.c_int(0),
|
|
ctypes.c_int(self.nx), ctypes.c_int(self.ny),
|
|
)
|
|
)
|
|
)
|
|
|
|
# print("--External and not Internal: Launching Kernel is ok")
|
|
return
|
|
|
|
if internal and not external:
|
|
|
|
# INTERNAL DOMAIN
|
|
# (x0, y0) x (x1, y1)
|
|
# (x_halo, y_halo) x (nx - x_halo, ny - y_halo)
|
|
"""
|
|
self.kernel.prepared_async_call(self.grid_size, self.block_size, self.internal_stream,
|
|
self.nx, self.ny,
|
|
self.dx, self.dy, dt,
|
|
self.g,
|
|
self.gamma,
|
|
self.theta,
|
|
substep,
|
|
self.boundary_conditions,
|
|
self.u0[0].data.gpudata, self.u0[0].data.strides[0],
|
|
self.u0[1].data.gpudata, self.u0[1].data.strides[0],
|
|
self.u0[2].data.gpudata, self.u0[2].data.strides[0],
|
|
self.u0[3].data.gpudata, self.u0[3].data.strides[0],
|
|
self.u1[0].data.gpudata, self.u1[0].data.strides[0],
|
|
self.u1[1].data.gpudata, self.u1[1].data.strides[0],
|
|
self.u1[2].data.gpudata, self.u1[2].data.strides[0],
|
|
self.u1[3].data.gpudata, self.u1[3].data.strides[0],
|
|
self.cfl_data.gpudata,
|
|
int(self.u0[0].x_halo), int(self.u0[0].y_halo),
|
|
self.nx - int(self.u0[0].x_halo), self.ny - int(self.u0[0].y_halo))
|
|
"""
|
|
|
|
hip_check(
|
|
hip.hipModuleLaunchKernel(
|
|
self.kernel,
|
|
*self.grid_size, #grid
|
|
*self.block_size, #block
|
|
sharedMemBytes=0, #65536,
|
|
stream=self.internal_stream,
|
|
kernelParams=None,
|
|
extra=( # pass kernel's arguments
|
|
ctypes.c_int(self.nx), ctypes.c_int(self.ny),
|
|
ctypes.c_float(self.dx), ctypes.c_float(self.dy), ctypes.c_float(dt),
|
|
ctypes.c_float(self.g),
|
|
ctypes.c_float(self.gamma),
|
|
ctypes.c_float(self.theta),
|
|
ctypes.c_int(substep),
|
|
ctypes.c_int(self.boundary_conditions),
|
|
self.u0[0].data, ctypes.c_int(u00_strides0),
|
|
self.u0[1].data, ctypes.c_int(u01_strides0),
|
|
self.u0[2].data, ctypes.c_int(u02_strides0),
|
|
self.u0[3].data, ctypes.c_int(u03_strides0),
|
|
self.u1[0].data, ctypes.c_int(u10_strides0),
|
|
self.u1[1].data, ctypes.c_int(u11_strides0),
|
|
self.u1[2].data, ctypes.c_int(u12_strides0),
|
|
self.u1[3].data, ctypes.c_int(u13_strides0),
|
|
self.cfl_data,
|
|
ctypes.c_int(self.u0[0].x_halo), ctypes.c_int(self.u0[0].y_halo),
|
|
ctypes.c_int(self.nx - self.u0[0].x_halo), ctypes.c_int(self.ny - self.u0[0].y_halo),
|
|
)
|
|
)
|
|
)
|
|
|
|
# print("--Internal and not External: Launching Kernel is ok")
|
|
return
|
|
|
|
def swapBuffers(self):
|
|
self.u0, self.u1 = self.u1, self.u0
|
|
return
|
|
|
|
def getOutput(self):
|
|
return self.u0
|
|
|
|
def check(self):
|
|
self.u0.check()
|
|
self.u1.check()
|
|
return
|
|
|
|
# computing min with hipblas: the output is an index
|
|
def min_hipblas(self, num_elements, cfl_data, stream):
|
|
num_bytes = num_elements * np.dtype(np.float32).itemsize
|
|
num_bytes_i = np.dtype(np.int32).itemsize
|
|
indx_d = hip_check(hip.hipMalloc(num_bytes_i))
|
|
indx_h = np.zeros(1, dtype=np.int32)
|
|
x_temp = np.zeros(num_elements, dtype=np.float32)
|
|
|
|
#print("--size.data:", cfl_data.size)
|
|
handle = hip_check(hipblas.hipblasCreate())
|
|
|
|
#hip_check(hipblas.hipblasGetStream(handle, stream))
|
|
#"incx" [int] specifies the increment for the elements of x. incx must be > 0.
|
|
hip_check(hipblas.hipblasIsamin(handle, num_elements, cfl_data, 1, indx_d))
|
|
|
|
# destruction of handle
|
|
hip_check(hipblas.hipblasDestroy(handle))
|
|
|
|
# copy result (stored in indx_d) back to the host (store in indx_h)
|
|
hip_check(hip.hipMemcpyAsync(indx_h,indx_d,num_bytes_i,hip.hipMemcpyKind.hipMemcpyDeviceToHost,stream))
|
|
hip_check(hip.hipMemcpyAsync(x_temp,cfl_data,num_bytes,hip.hipMemcpyKind.hipMemcpyDeviceToHost,stream))
|
|
#hip_check(hip.hipMemsetAsync(cfl_data,0,num_bytes,self.stream))
|
|
hip_check(hip.hipStreamSynchronize(stream))
|
|
|
|
min_value = x_temp.flatten()[indx_h[0]-1]
|
|
|
|
# clean up
|
|
hip_check(hip.hipStreamDestroy(stream))
|
|
hip_check(hip.hipFree(cfl_data))
|
|
return min_value
|
|
|
|
def computeDt(self):
|
|
#max_dt = gpuarray.min(self.cfl_data, stream=self.stream).get();
|
|
max_dt = self.min_hipblas(self.cfl_data.size, self.cfl_data, self.stream)
|
|
return max_dt*0.5
|