mirror of
https://github.com/smyalygames/FiniteVolumeGPU.git
synced 2026-01-14 15:48:43 +01:00
Refactoring
This commit is contained in:
André R. Brodtkorb
@@ -55,20 +55,10 @@ class BaseSimulator:
|
||||
#Get logger
|
||||
self.logger = logging.getLogger(__name__ + "." + self.__class__.__name__)
|
||||
|
||||
self.context = context
|
||||
|
||||
if (self.context.autotuner):
|
||||
peak_configuration = self.context.autotuner.get_peak_performance(self.__class__)
|
||||
block_width = int(peak_configuration["block_width"])
|
||||
block_height = int(peak_configuration["block_height"])
|
||||
self.logger.debug("Used autotuning to get block size [%d x %d]", block_width, block_height)
|
||||
|
||||
#Create a CUDA stream
|
||||
self.stream = cuda.Stream()
|
||||
|
||||
#Save input parameters
|
||||
#Notice that we need to specify them in the correct dataformat for the
|
||||
#GPU kernel
|
||||
self.context = context
|
||||
self.nx = np.int32(nx)
|
||||
self.ny = np.int32(ny)
|
||||
self.dx = np.float32(dx)
|
||||
@@ -76,15 +66,25 @@ class BaseSimulator:
|
||||
self.dt = np.float32(dt)
|
||||
self.g = np.float32(g)
|
||||
|
||||
#Handle autotuning block size
|
||||
if (self.context.autotuner):
|
||||
peak_configuration = self.context.autotuner.get_peak_performance(self.__class__)
|
||||
block_width = int(peak_configuration["block_width"])
|
||||
block_height = int(peak_configuration["block_height"])
|
||||
self.logger.debug("Used autotuning to get block size [%d x %d]", block_width, block_height)
|
||||
|
||||
#Compute kernel launch parameters
|
||||
self.block_size = (block_width, block_height, 1)
|
||||
self.grid_size = ( \
|
||||
int(np.ceil(self.nx / float(self.block_size[0]))), \
|
||||
int(np.ceil(self.ny / float(self.block_size[1]))) \
|
||||
)
|
||||
|
||||
#Create a CUDA stream
|
||||
self.stream = cuda.Stream()
|
||||
|
||||
#Keep track of simulation time
|
||||
self.t = 0.0;
|
||||
|
||||
#Compute kernel launch parameters
|
||||
self.local_size = (block_width, block_height, 1)
|
||||
self.global_size = ( \
|
||||
int(np.ceil(self.nx / float(self.local_size[0]))), \
|
||||
int(np.ceil(self.ny / float(self.local_size[1]))) \
|
||||
)
|
||||
|
||||
def __str__(self):
|
||||
return "{:s} [{:d}x{:d}]".format(self.__class__.__name__, self.nx, self.ny)
|
||||
@@ -115,7 +115,7 @@ class BaseSimulator:
|
||||
# Step with forward Euler
|
||||
self.stepEuler(local_dt)
|
||||
|
||||
self.logger.info("%s simulated %f seconds to %f with %d steps in %f seconds (Euler)", self, t_end, self.t, n, t.secs)
|
||||
self.logger.info("%s simulated %f seconds to %f with %d steps (Euler)", self, t_end, self.t, n)
|
||||
return self.t, n
|
||||
|
||||
"""
|
||||
@@ -123,22 +123,21 @@ class BaseSimulator:
|
||||
Requires that the stepRK functionality is implemented in the subclasses
|
||||
"""
|
||||
def simulateRK(self, t_end, order):
|
||||
with Common.Timer(self.__class__.__name__ + ".simulateRK") as t:
|
||||
# Compute number of timesteps to perform
|
||||
n = int(t_end / self.dt + 1)
|
||||
# Compute number of timesteps to perform
|
||||
n = int(t_end / self.dt + 1)
|
||||
|
||||
for i in range(0, n):
|
||||
# Compute timestep for "this" iteration
|
||||
local_dt = np.float32(min(self.dt, t_end-i*self.dt))
|
||||
|
||||
for i in range(0, n):
|
||||
# Compute timestep for "this" iteration
|
||||
local_dt = np.float32(min(self.dt, t_end-i*self.dt))
|
||||
|
||||
# Stop if end reached (should not happen)
|
||||
if (local_dt <= 0.0):
|
||||
break
|
||||
# Stop if end reached (should not happen)
|
||||
if (local_dt <= 0.0):
|
||||
break
|
||||
|
||||
# Perform all the Runge-Kutta substeps
|
||||
self.stepRK(local_dt, order)
|
||||
|
||||
# Perform all the Runge-Kutta substeps
|
||||
self.stepRK(local_dt, order)
|
||||
|
||||
self.logger.info("%s simulated %f seconds to %f with %d steps in %f seconds (RK2)", self, t_end, self.t, n, t.secs)
|
||||
self.logger.info("%s simulated %f seconds to %f with %d steps (RK2)", self, t_end, self.t, n)
|
||||
return self.t, n
|
||||
|
||||
"""
|
||||
@@ -146,23 +145,22 @@ class BaseSimulator:
|
||||
Requires that the stepDimsplitX and stepDimsplitY functionality is implemented in the subclasses
|
||||
"""
|
||||
def simulateDimsplit(self, t_end):
|
||||
with Common.Timer(self.__class__.__name__ + ".simulateDimsplit") as t:
|
||||
# Compute number of timesteps to perform
|
||||
n = int(t_end / (2.0*self.dt) + 1)
|
||||
# Compute number of timesteps to perform
|
||||
n = int(t_end / (2.0*self.dt) + 1)
|
||||
|
||||
for i in range(0, n):
|
||||
# Compute timestep for "this" iteration
|
||||
local_dt = np.float32(0.5*min(2*self.dt, t_end-2*i*self.dt))
|
||||
|
||||
for i in range(0, n):
|
||||
# Compute timestep for "this" iteration
|
||||
local_dt = np.float32(0.5*min(2*self.dt, t_end-2*i*self.dt))
|
||||
|
||||
# Stop if end reached (should not happen)
|
||||
if (local_dt <= 0.0):
|
||||
break
|
||||
|
||||
# Perform the dimensional split substeps
|
||||
self.stepDimsplitXY(local_dt)
|
||||
self.stepDimsplitYX(local_dt)
|
||||
# Stop if end reached (should not happen)
|
||||
if (local_dt <= 0.0):
|
||||
break
|
||||
|
||||
self.logger.info("%s simulated %f seconds to %f with %d steps in %f seconds (dimsplit)", self, t_end, self.t, 2*n, t.secs)
|
||||
# Perform the dimensional split substeps
|
||||
self.stepDimsplitXY(local_dt)
|
||||
self.stepDimsplitYX(local_dt)
|
||||
|
||||
self.logger.info("%s simulated %f seconds to %f with %d steps (dimsplit)", self, t_end, self.t, 2*n)
|
||||
return self.t, 2*n
|
||||
|
||||
|
||||
|
||||
Reference in New Issue
Block a user