smyalygames/FiniteVolumeGPU_HIP
1
0
Fork 0
mirror of https://github.com/smyalygames/FiniteVolumeGPU_HIP.git synced 2025-05-18 14:34:12 +02:00
Code Issues Actions Packages Projects Releases Wiki Activity

Re-write FORCE.py with hip-python

Browse Source
This commit is contained in:
Hicham Agueny 2024-02-20 14:20:12 +01:00
parent def4b9935d
commit e2b56c6ab9
1 changed files with 144 additions and 32 deletions
Show Stats Download Patch File Download Diff File Expand all files Collapse all files

176
GPUSimulators/FORCE.py
View File

@ -25,8 +25,8 @@ from GPUSimulators import Simulator, Common
from GPUSimulators.Simulator import BaseSimulator, BoundaryCondition from GPUSimulators.Simulator import BaseSimulator, BoundaryCondition
import numpy as np import numpy as np
from pycuda import gpuarray #from pycuda import gpuarray
from hip import hip,hiprtc
@ -53,6 +53,20 @@ class FORCE (Simulator.BaseSimulator):
dt: Size of each timestep (90 s) dt: Size of each timestep (90 s)
g: Gravitational accelleration (9.81 m/s^2) g: Gravitational accelleration (9.81 m/s^2)
""" """
def hip_check(call_result):
err = call_result[0]
result = call_result[1:]
if len(result) == 1:
result = result[0]
if isinstance(err, hip.hipError_t) and err != hip.hipError_t.hipSuccess:
raise RuntimeError(str(err))
elif (
isinstance(err, hiprtc.hiprtcResult)
and err != hiprtc.hiprtcResult.HIPRTC_SUCCESS
):
raise RuntimeError(str(err))
return result
def __init__(self, def __init__(self,
context, context,
h0, hu0, hv0, h0, hu0, hv0,
@ -74,19 +88,46 @@ class FORCE (Simulator.BaseSimulator):
self.g = np.float32(g) self.g = np.float32(g)
#Get kernels #Get kernels
module = context.get_module("cuda/SWE2D_FORCE.cu", # module = context.get_module("cuda/SWE2D_FORCE.cu",
defines={ # defines={
'BLOCK_WIDTH': self.block_size[0], # 'BLOCK_WIDTH': self.block_size[0],
'BLOCK_HEIGHT': self.block_size[1] # 'BLOCK_HEIGHT': self.block_size[1]
}, # },
compile_args={ # compile_args={
'no_extern_c': True, # 'no_extern_c': True,
'options': ["--use_fast_math"], # 'options': ["--use_fast_math"],
}, # },
jit_compile_args={}) # jit_compile_args={})
self.kernel = module.get_function("FORCEKernel") # self.kernel = module.get_function("FORCEKernel")
self.kernel.prepare("iiffffiPiPiPiPiPiPiP") # self.kernel.prepare("iiffffiPiPiPiPiPiPiP")
kernel_file_path = os.path.abspath(os.path.join('cuda', 'SWE2D_FORCE.cu'))
with open(kernel_file_path, 'r') as file:
kernel_source = file.read()
prog = hip_check(hiprtc.hiprtcCreateProgram(kernel_source.encode(), b"FORCEKernel", 0, [], []))
props = hip.hipDeviceProp_t()
hip_check(hip.hipGetDeviceProperties(props,0))
arch = props.gcnArchName
print(f"Compiling kernel .FORCEKernel. for {arch}")
cflags = [b"--offload-arch="+arch]
err, = hiprtc.hiprtcCompileProgram(prog, len(cflags), cflags)
if err != hiprtc.hiprtcResult.HIPRTC_SUCCESS:
log_size = hip_check(hiprtc.hiprtcGetProgramLogSize(prog))
log = bytearray(log_size)
hip_check(hiprtc.hiprtcGetProgramLog(prog, log))
raise RuntimeError(log.decode())
code_size = hip_check(hiprtc.hiprtcGetCodeSize(prog))
code = bytearray(code_size)
hip_check(hiprtc.hiprtcGetCode(prog, code))
module = hip_check(hip.hipModuleLoadData(code))
kernel = hip_check(hip.hipModuleGetFunction(module, b"FORCEKernel"))
#Create data by uploading to device #Create data by uploading to device
self.u0 = Common.ArakawaA2D(self.stream, self.u0 = Common.ArakawaA2D(self.stream,
nx, ny, nx, ny,
@ -96,34 +137,105 @@ class FORCE (Simulator.BaseSimulator):
nx, ny, nx, ny,
1, 1, 1, 1,
[None, None, None]) [None, None, None])
self.cfl_data = gpuarray.GPUArray(self.grid_size, dtype=np.float32) #self.cfl_data = gpuarray.GPUArray(self.grid_size, dtype=np.float32)
data_h = np.empty(self.grid_size, dtype=np.float32)
num_bytes = data_h.size * data_h.itemsize
self.cfl_data = hip_check(hip.hipMalloc(num_bytes)).configure(
typestr="float32",shape=self.grid_size)
dt_x = np.min(self.dx / (np.abs(hu0/h0) + np.sqrt(g*h0))) dt_x = np.min(self.dx / (np.abs(hu0/h0) + np.sqrt(g*h0)))
dt_y = np.min(self.dy / (np.abs(hv0/h0) + np.sqrt(g*h0))) dt_y = np.min(self.dy / (np.abs(hv0/h0) + np.sqrt(g*h0)))
dt = min(dt_x, dt_y) dt = min(dt_x, dt_y)
self.cfl_data.fill(dt, stream=self.stream) self.cfl_data.fill(dt, stream=self.stream)
def substep(self, dt, step_number): def substep(self, dt, step_number):
self.kernel.prepared_async_call(self.grid_size, self.block_size, self.stream, # self.kernel.prepared_async_call(self.grid_size, self.block_size, self.stream,
self.nx, self.ny, # self.nx, self.ny,
self.dx, self.dy, dt, # self.dx, self.dy, dt,
self.g, # self.g,
self.boundary_conditions, # self.boundary_conditions,
self.u0[0].data.gpudata, self.u0[0].data.strides[0], # self.u0[0].data.gpudata, self.u0[0].data.strides[0],
self.u0[1].data.gpudata, self.u0[1].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[2].data.gpudata, self.u0[2].data.strides[0],
self.u1[0].data.gpudata, self.u1[0].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[1].data.gpudata, self.u1[1].data.strides[0],
self.u1[2].data.gpudata, self.u1[2].data.strides[0], # self.u1[2].data.gpudata, self.u1[2].data.strides[0],
self.cfl_data.gpudata) # self.cfl_data.gpudata)
self.u0, self.u1 = self.u1, self.u0 # self.u0, self.u1 = self.u1, self.u0
#launch kernel
hip_check(
hip.hipModuleLaunchKernel(
kernel,
*self.grid_size,
*self.block_size,
sharedMemBytes=0,
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(self.dt),
ctypes.c_float(self.g),
ctypes.c_int(self.boundary_conditions),
ctypes.c_float(self.u0[0].data), ctypes.c_float(self.u0[0].data.strides[0]),
ctypes.c_float(self.u0[1].data), ctypes.c_float(self.u0[1].data.strides[0]),
ctypes.c_float(self.u0[2].data), ctypes.c_float(self.u0[2].data.strides[0]),
ctypes.c_float(self.u1[0].data), ctypes.c_float(self.u1[0].data.strides[0]),
ctypes.c_float(self.u1[1].data), ctypes.c_float(self.u1[1].data.strides[0]),
ctypes.c_float(self.u1[2].data), ctypes.c_float(self.u1[2].data.strides[0]),
self.cfl_data
)
)
)
self.u0, self.u1 = self.u1, self.u0
hip_check(hip.hipDeviceSynchronize())
hip_check(hip.hipModuleUnload(module))
hip_check(hip.hipFree(cfl_data))
print("--Launching Kernel .FORCEKernel. is ok")
def getOutput(self): def getOutput(self):
return self.u0 return self.u0
def check(self): def check(self):
self.u0.check() self.u0.check()
self.u1.check() self.u1.check()
# 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): def computeDt(self):
max_dt = gpuarray.min(self.cfl_data, stream=self.stream).get(); #max_dt = gpuarray.min(self.cfl_data, stream=self.stream).get();
return max_dt max_dt = self.min_hipblas(self.cfl_data.size, self.cfl_data, self.stream)
return max_dt