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Refactoring

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This commit is contained in:
André R. Brodtkorb 2018-09-04 16:33:50 +02:00
parent 58f281d724
commit 10d8e26108
30 changed files with 774 additions and 840 deletions
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220
GPUSimulators/Common.py
View File

@ -34,7 +34,6 @@ import pycuda.compiler as cuda_compiler
import pycuda.gpuarray
import pycuda.driver as cuda
from GPUSimulators import Autotuner
"""
Class which keeps track of time spent for a section of code
@ -59,225 +58,6 @@ class Timer(object):
"""
Class which keeps track of the CUDA context and some helper functions
"""
class CudaContext(object):
def __init__(self, blocking=False, use_cache=True, autotuning=True):
self.blocking = blocking
self.use_cache = use_cache
self.logger = logging.getLogger(__name__)
self.kernels = {}
self.module_path = os.path.dirname(os.path.realpath(__file__))
#Initialize cuda (must be first call to PyCUDA)
cuda.init(flags=0)
self.logger.info("PyCUDA version %s", str(pycuda.VERSION_TEXT))
#Print some info about CUDA
self.logger.info("CUDA version %s", str(cuda.get_version()))
self.logger.info("Driver version %s", str(cuda.get_driver_version()))
self.cuda_device = cuda.Device(0)
self.logger.info("Using '%s' GPU", self.cuda_device.name())
self.logger.debug(" => compute capability: %s", str(self.cuda_device.compute_capability()))
# Create the CUDA context
if (self.blocking):
self.cuda_context = self.cuda_device.make_context(flags=cuda.ctx_flags.SCHED_BLOCKING_SYNC)
self.logger.warning("Using blocking context")
else:
self.cuda_context = self.cuda_device.make_context(flags=cuda.ctx_flags.SCHED_AUTO)
free, total = cuda.mem_get_info()
self.logger.debug(" => memory: %d / %d MB available", int(free/(1024*1024)), int(total/(1024*1024)))
self.logger.info("Created context handle <%s>", str(self.cuda_context.handle))
#Create cache dir for cubin files
if (self.use_cache):
self.cache_path = os.path.join(self.module_path, "cuda_cache")
if not os.path.isdir(self.cache_path):
os.mkdir(self.cache_path)
self.logger.info("Using CUDA cache dir %s", self.cache_path)
self.autotuner = None
if (autotuning):
self.logger.info("Autotuning enabled. It may take several minutes to run the code the first time: have patience")
self.autotuner = Autotuner.Autotuner()
def __del__(self, *args):
self.logger.info("Cleaning up CUDA context handle <%s>", str(self.cuda_context.handle))
# Loop over all contexts in stack, and remove "this"
other_contexts = []
while (cuda.Context.get_current() != None):
context = cuda.Context.get_current()
if (context.handle != self.cuda_context.handle):
self.logger.debug("<%s> Popping <%s> (*not* ours)", str(self.cuda_context.handle), str(context.handle))
other_contexts = [context] + other_contexts
cuda.Context.pop()
else:
self.logger.debug("<%s> Popping <%s> (ours)", str(self.cuda_context.handle), str(context.handle))
cuda.Context.pop()
# Add all the contexts we popped that were not our own
for context in other_contexts:
self.logger.debug("<%s> Pushing <%s>", str(self.cuda_context.handle), str(context.handle))
cuda.Context.push(context)
self.logger.debug("<%s> Detaching", str(self.cuda_context.handle))
self.cuda_context.detach()
def __str__(self):
return "CudaContext id " + str(self.cuda_context.handle)
def hash_kernel(kernel_filename, include_dirs):
# Generate a kernel ID for our caches
num_includes = 0
max_includes = 100
kernel_hasher = hashlib.md5()
logger = logging.getLogger(__name__)
# Loop over file and includes, and check if something has changed
files = [kernel_filename]
while len(files):
if (num_includes > max_includes):
raise("Maximum number of includes reached - circular include in {:}?".format(kernel_filename))
filename = files.pop()
#logger.debug("Hashing %s", filename)
modified = os.path.getmtime(filename)
# Open the file
with io.open(filename, "r") as file:
# Search for #inclue <something> and also hash the file
file_str = file.read()
kernel_hasher.update(file_str.encode('utf-8'))
kernel_hasher.update(str(modified).encode('utf-8'))
#Find all includes
includes = re.findall('^\W*#include\W+(.+?)\W*$', file_str, re.M)
# Loop over everything that looks like an include
for include_file in includes:
#Search through include directories for the file
file_path = os.path.dirname(filename)
for include_path in [file_path] + include_dirs:
# If we find it, add it to list of files to check
temp_path = os.path.join(include_path, include_file)
if (os.path.isfile(temp_path)):
files = files + [temp_path]
num_includes = num_includes + 1 #For circular includes...
break
return kernel_hasher.hexdigest()
"""
Reads a text file and creates an OpenCL kernel from that
"""
def get_prepared_kernel(self, kernel_filename, kernel_function_name, \
prepared_call_args, \
include_dirs=[], no_extern_c=True,
**kwargs):
"""
Helper function to print compilation output
"""
def cuda_compile_message_handler(compile_success_bool, info_str, error_str):
self.logger.debug("Compilation returned %s", str(compile_success_bool))
if info_str:
self.logger.debug("Info: %s", info_str)
if error_str:
self.logger.debug("Error: %s", error_str)
#self.logger.debug("Getting %s", kernel_filename)
# Create a hash of the kernel (and its includes)
kwargs_hasher = hashlib.md5()
kwargs_hasher.update(str(kwargs).encode('utf-8'));
kwargs_hash = kwargs_hasher.hexdigest()
kwargs_hasher = None
root, ext = os.path.splitext(kernel_filename)
kernel_hash = root \
+ "_" + CudaContext.hash_kernel( \
os.path.join(self.module_path, kernel_filename), \
include_dirs=[self.module_path] + include_dirs) \
+ "_" + kwargs_hash \
+ ext
cached_kernel_filename = os.path.join(self.cache_path, kernel_hash)
# If we have the kernel in our hashmap, return it
if (kernel_hash in self.kernels.keys()):
self.logger.debug("Found kernel %s cached in hashmap (%s)", kernel_filename, kernel_hash)
return self.kernels[kernel_hash]
# If we have it on disk, return it
elif (self.use_cache and os.path.isfile(cached_kernel_filename)):
self.logger.debug("Found kernel %s cached on disk (%s)", kernel_filename, kernel_hash)
with io.open(cached_kernel_filename, "rb") as file:
file_str = file.read()
module = cuda.module_from_buffer(file_str, message_handler=cuda_compile_message_handler)
kernel = module.get_function(kernel_function_name)
kernel.prepare(prepared_call_args)
self.kernels[kernel_hash] = kernel
return kernel
# Otherwise, compile it from source
else:
self.logger.debug("Compiling %s (%s)", kernel_filename, kernel_hash)
#Create kernel string
kernel_string = ""
for key, value in kwargs.items():
kernel_string += "#define {:s} {:s}\n".format(str(key), str(value))
kernel_string += '#include "{:s}"'.format(os.path.join(self.module_path, kernel_filename))
if (self.use_cache):
with io.open(cached_kernel_filename + ".txt", "w") as file:
file.write(kernel_string)
with Timer("compiler") as timer:
cubin = cuda_compiler.compile(kernel_string, include_dirs=include_dirs, no_extern_c=no_extern_c, cache_dir=False)
module = cuda.module_from_buffer(cubin, message_handler=cuda_compile_message_handler)
if (self.use_cache):
with io.open(cached_kernel_filename, "wb") as file:
file.write(cubin)
kernel = module.get_function(kernel_function_name)
kernel.prepare(prepared_call_args)
self.kernels[kernel_hash] = kernel
return kernel
"""
Clears the kernel cache (useful for debugging & development)
"""
def clear_kernel_cache(self):
self.logger.debug("Clearing cache")
self.kernels = {}
gc.collect()
"""
Synchronizes all streams etc
"""
def synchronize(self):
self.cuda_context.synchronize()

263
GPUSimulators/CudaContext.py Normal file
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@ -0,0 +1,263 @@
# -*- coding: utf-8 -*-
"""
This python module implements Cuda context handling
Copyright (C) 2018 SINTEF ICT
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
"""
import os
import numpy as np
import time
import re
import io
import hashlib
import logging
import gc
import pycuda.compiler as cuda_compiler
import pycuda.gpuarray
import pycuda.driver as cuda
from GPUSimulators import Autotuner, Common
"""
Class which keeps track of the CUDA context and some helper functions
"""
class CudaContext(object):
def __init__(self, blocking=False, use_cache=True, autotuning=True):
self.blocking = blocking
self.use_cache = use_cache
self.logger = logging.getLogger(__name__)
self.kernels = {}
self.module_path = os.path.dirname(os.path.realpath(__file__))
#Initialize cuda (must be first call to PyCUDA)
cuda.init(flags=0)
self.logger.info("PyCUDA version %s", str(pycuda.VERSION_TEXT))
#Print some info about CUDA
self.logger.info("CUDA version %s", str(cuda.get_version()))
self.logger.info("Driver version %s", str(cuda.get_driver_version()))
self.cuda_device = cuda.Device(0)
self.logger.info("Using '%s' GPU", self.cuda_device.name())
self.logger.debug(" => compute capability: %s", str(self.cuda_device.compute_capability()))
# Create the CUDA context
if (self.blocking):
self.cuda_context = self.cuda_device.make_context(flags=cuda.ctx_flags.SCHED_BLOCKING_SYNC)
self.logger.warning("Using blocking context")
else:
self.cuda_context = self.cuda_device.make_context(flags=cuda.ctx_flags.SCHED_AUTO)
free, total = cuda.mem_get_info()
self.logger.debug(" => memory: %d / %d MB available", int(free/(1024*1024)), int(total/(1024*1024)))
self.logger.info("Created context handle <%s>", str(self.cuda_context.handle))
#Create cache dir for cubin files
if (self.use_cache):
self.cache_path = os.path.join(self.module_path, "cuda_cache")
if not os.path.isdir(self.cache_path):
os.mkdir(self.cache_path)
self.logger.info("Using CUDA cache dir %s", self.cache_path)
self.autotuner = None
if (autotuning):
self.logger.info("Autotuning enabled. It may take several minutes to run the code the first time: have patience")
self.autotuner = Autotuner.Autotuner()
def __del__(self, *args):
self.logger.info("Cleaning up CUDA context handle <%s>", str(self.cuda_context.handle))
# Loop over all contexts in stack, and remove "this"
other_contexts = []
while (cuda.Context.get_current() != None):
context = cuda.Context.get_current()
if (context.handle != self.cuda_context.handle):
self.logger.debug("<%s> Popping <%s> (*not* ours)", str(self.cuda_context.handle), str(context.handle))
other_contexts = [context] + other_contexts
cuda.Context.pop()
else:
self.logger.debug("<%s> Popping <%s> (ours)", str(self.cuda_context.handle), str(context.handle))
cuda.Context.pop()
# Add all the contexts we popped that were not our own
for context in other_contexts:
self.logger.debug("<%s> Pushing <%s>", str(self.cuda_context.handle), str(context.handle))
cuda.Context.push(context)
self.logger.debug("<%s> Detaching", str(self.cuda_context.handle))
self.cuda_context.detach()
def __str__(self):
return "CudaContext id " + str(self.cuda_context.handle)
def hash_kernel(kernel_filename, include_dirs):
# Generate a kernel ID for our caches
num_includes = 0
max_includes = 100
kernel_hasher = hashlib.md5()
logger = logging.getLogger(__name__)
# Loop over file and includes, and check if something has changed
files = [kernel_filename]
while len(files):
if (num_includes > max_includes):
raise("Maximum number of includes reached - circular include in {:}?".format(kernel_filename))
filename = files.pop()
#logger.debug("Hashing %s", filename)
modified = os.path.getmtime(filename)
# Open the file
with io.open(filename, "r") as file:
# Search for #inclue <something> and also hash the file
file_str = file.read()
kernel_hasher.update(file_str.encode('utf-8'))
kernel_hasher.update(str(modified).encode('utf-8'))
#Find all includes
includes = re.findall('^\W*#include\W+(.+?)\W*$', file_str, re.M)
# Loop over everything that looks like an include
for include_file in includes:
#Search through include directories for the file
file_path = os.path.dirname(filename)
for include_path in [file_path] + include_dirs:
# If we find it, add it to list of files to check
temp_path = os.path.join(include_path, include_file)
if (os.path.isfile(temp_path)):
files = files + [temp_path]
num_includes = num_includes + 1 #For circular includes...
break
return kernel_hasher.hexdigest()
"""
Reads a text file and creates an OpenCL kernel from that
"""
def get_prepared_kernel(self, kernel_filename, kernel_function_name, \
prepared_call_args, \
include_dirs=[], no_extern_c=True,
**kwargs):
"""
Helper function to print compilation output
"""
def cuda_compile_message_handler(compile_success_bool, info_str, error_str):
self.logger.debug("Compilation returned %s", str(compile_success_bool))
if info_str:
self.logger.debug("Info: %s", info_str)
if error_str:
self.logger.debug("Error: %s", error_str)
kernel_filename = os.path.normpath(kernel_filename)
#self.logger.debug("Getting %s", kernel_filename)
# Create a hash of the kernel (and its includes)
kwargs_hasher = hashlib.md5()
kwargs_hasher.update(str(kwargs).encode('utf-8'));
kwargs_hash = kwargs_hasher.hexdigest()
kwargs_hasher = None
root, ext = os.path.splitext(kernel_filename)
kernel_hash = root \
+ "_" + CudaContext.hash_kernel( \
os.path.join(self.module_path, kernel_filename), \
include_dirs=[self.module_path] + include_dirs) \
+ "_" + kwargs_hash \
+ ext
cached_kernel_filename = os.path.join(self.cache_path, kernel_hash)
# If we have the kernel in our hashmap, return it
if (kernel_hash in self.kernels.keys()):
self.logger.debug("Found kernel %s cached in hashmap (%s)", kernel_filename, kernel_hash)
return self.kernels[kernel_hash]
# If we have it on disk, return it
elif (self.use_cache and os.path.isfile(cached_kernel_filename)):
self.logger.debug("Found kernel %s cached on disk (%s)", kernel_filename, kernel_hash)
with io.open(cached_kernel_filename, "rb") as file:
file_str = file.read()
module = cuda.module_from_buffer(file_str, message_handler=cuda_compile_message_handler)
kernel = module.get_function(kernel_function_name)
kernel.prepare(prepared_call_args)
self.kernels[kernel_hash] = kernel
return kernel
# Otherwise, compile it from source
else:
self.logger.debug("Compiling %s (%s)", kernel_filename, kernel_hash)
#Create kernel string
kernel_string = ""
for key, value in kwargs.items():
kernel_string += "#define {:s} {:s}\n".format(str(key), str(value))
kernel_string += '#include "{:s}"'.format(os.path.join(self.module_path, kernel_filename))
if (self.use_cache):
cached_kernel_dir = os.path.dirname(cached_kernel_filename)
if not os.path.isdir(cached_kernel_dir):
os.mkdir(cached_kernel_dir)
with io.open(cached_kernel_filename + ".txt", "w") as file:
file.write(kernel_string)
with Common.Timer("compiler") as timer:
cubin = cuda_compiler.compile(kernel_string, include_dirs=include_dirs, no_extern_c=no_extern_c, cache_dir=False)
module = cuda.module_from_buffer(cubin, message_handler=cuda_compile_message_handler)
if (self.use_cache):
with io.open(cached_kernel_filename, "wb") as file:
file.write(cubin)
kernel = module.get_function(kernel_function_name)
kernel.prepare(prepared_call_args)
self.kernels[kernel_hash] = kernel
return kernel
"""
Clears the kernel cache (useful for debugging & development)
"""
def clear_kernel_cache(self):
self.logger.debug("Clearing cache")
self.kernels = {}
gc.collect()
"""
Synchronizes all streams etc
"""
def synchronize(self):
self.cuda_context.synchronize()

2
GPUSimulators/FORCE.py
View File

@ -66,7 +66,7 @@ class FORCE (Simulator.BaseSimulator):
block_width, block_height);
#Get kernels
self.kernel = context.get_prepared_kernel("FORCE_kernel.cu", "FORCEKernel", \
self.kernel = context.get_prepared_kernel("cuda/SWE_FORCE.cu", "FORCEKernel", \
"iiffffPiPiPiPiPiPi", \
BLOCK_WIDTH=self.local_size[0], \
BLOCK_HEIGHT=self.local_size[1])

2
GPUSimulators/HLL.py
View File

@ -61,7 +61,7 @@ class HLL (Simulator.BaseSimulator):
block_width, block_height);
#Get kernels
self.kernel = context.get_prepared_kernel("HLL_kernel.cu", "HLLKernel", \
self.kernel = context.get_prepared_kernel("cuda/SWE_HLL.cu", "HLLKernel", \
"iiffffPiPiPiPiPiPi", \
BLOCK_WIDTH=self.local_size[0], \
BLOCK_HEIGHT=self.local_size[1])

2
GPUSimulators/HLL2.py
View File

@ -67,7 +67,7 @@ class HLL2 (Simulator.BaseSimulator):
self.theta = np.float32(theta)
#Get kernels
self.kernel = context.get_prepared_kernel("HLL2_kernel.cu", "HLL2Kernel", \
self.kernel = context.get_prepared_kernel("cuda/SWE_HLL2.cu", "HLL2Kernel", \
"iifffffiPiPiPiPiPiPi", \
BLOCK_WIDTH=self.local_size[0], \
BLOCK_HEIGHT=self.local_size[1])

4
GPUSimulators/IPythonMagic.py
View File

@ -25,7 +25,7 @@ from IPython.core import magic_arguments
from IPython.core.magic import line_magic, Magics, magics_class
import pycuda.driver as cuda
from GPUSimulators import Common
from GPUSimulators import Common, CudaContext
@magics_class
@ -53,7 +53,7 @@ class MyIPythonMagic(Magics):
self.logger.debug("Creating context")
use_cache = False if args.no_cache else True
use_autotuning = False if args.no_autotuning else True
self.shell.user_ns[args.name] = Common.CudaContext(blocking=args.blocking, use_cache=use_cache, autotuning=use_autotuning)
self.shell.user_ns[args.name] = CudaContext.CudaContext(blocking=args.blocking, use_cache=use_cache, autotuning=use_autotuning)
# this function will be called on exceptions in any cell
def custom_exc(shell, etype, evalue, tb, tb_offset=None):

2
GPUSimulators/KP07.py
View File

@ -68,7 +68,7 @@ class KP07 (Simulator.BaseSimulator):
self.theta = np.float32(theta)
#Get kernels
self.kernel = context.get_prepared_kernel("KP07_kernel.cu", "KP07Kernel", \
self.kernel = context.get_prepared_kernel("cuda/SWE_KP07.cu", "KP07Kernel", \
"iifffffiPiPiPiPiPiPi", \
BLOCK_WIDTH=self.local_size[0], \
BLOCK_HEIGHT=self.local_size[1])

2
GPUSimulators/KP07_dimsplit.py
View File

@ -68,7 +68,7 @@ class KP07_dimsplit (Simulator.BaseSimulator):
self.theta = np.float32(theta)
#Get kernels
self.kernel = context.get_prepared_kernel("KP07_dimsplit_kernel.cu", "KP07DimsplitKernel", \
self.kernel = context.get_prepared_kernel("cuda/SWE_KP07_dimsplit.cu", "KP07DimsplitKernel", \
"iifffffiPiPiPiPiPiPi", \
BLOCK_WIDTH=self.local_size[0], \
BLOCK_HEIGHT=self.local_size[1])

4
GPUSimulators/LxF.py
View File

@ -21,7 +21,7 @@ along with this program. If not, see <http://www.gnu.org/licenses/>.
"""
#Import packages we need
from GPUSimulators import Simulator, Common
from GPUSimulators import Simulator, Common, CudaContext
@ -62,7 +62,7 @@ class LxF (Simulator.BaseSimulator):
block_width, block_height);
# Get kernels
self.kernel = context.get_prepared_kernel("LxF_kernel.cu", "LxFKernel", \
self.kernel = context.get_prepared_kernel("cuda/SWE_LxF.cu", "LxFKernel", \
"iiffffPiPiPiPiPiPi", \
BLOCK_WIDTH=self.local_size[0], \
BLOCK_HEIGHT=self.local_size[1])

66
GPUSimulators/SWECommon.cu
View File

@ -1,66 +0,0 @@
/*
This OpenCL kernel implements the Kurganov-Petrova numerical scheme
for the shallow water equations, described in
A. Kurganov & Guergana Petrova
A Second-Order Well-Balanced Positivity Preserving Central-Upwind
Scheme for the Saint-Venant System Communications in Mathematical
Sciences, 5 (2007), 133-160.
Copyright (C) 2016 SINTEF ICT
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
__device__ float3 F_func(const float3 Q, const float g) {
float3 F;
F.x = Q.y; //hu
F.y = Q.y*Q.y / Q.x + 0.5f*g*Q.x*Q.x; //hu*hu/h + 0.5f*g*h*h;
F.z = Q.y*Q.z / Q.x; //hu*hv/h;
return F;
}

2
GPUSimulators/WAF.py
View File

@ -61,7 +61,7 @@ class WAF (Simulator.BaseSimulator):
block_width, block_height);
#Get kernels
self.kernel = context.get_prepared_kernel("WAF_kernel.cu", "WAFKernel", \
self.kernel = context.get_prepared_kernel("cuda/SWE_WAF.cu", "WAFKernel", \
"iiffffiPiPiPiPiPiPi", \
BLOCK_WIDTH=self.local_size[0], \
BLOCK_HEIGHT=self.local_size[1])

1
GPUSimulators/EulerCommon.cu → GPUSimulators/cuda/EulerCommon.h
View File

@ -22,6 +22,7 @@ You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
#pragma once

474
GPUSimulators/cuda/SWECommon.h Normal file
View File

@ -0,0 +1,474 @@
/*
This OpenCL kernel implements the Kurganov-Petrova numerical scheme
for the shallow water equations, described in
A. Kurganov & Guergana Petrova
A Second-Order Well-Balanced Positivity Preserving Central-Upwind
Scheme for the Saint-Venant System Communications in Mathematical
Sciences, 5 (2007), 133-160.
Copyright (C) 2016 SINTEF ICT
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
#pragma once
#include "limiters.h"
__device__ float3 F_func(const float3 Q, const float g) {
float3 F;
F.x = Q.y; //hu
F.y = Q.y*Q.y / Q.x + 0.5f*g*Q.x*Q.x; //hu*hu/h + 0.5f*g*h*h;
F.z = Q.y*Q.z / Q.x; //hu*hv/h;
return F;
}
/**
* Superbee flux limiter for WAF.
* Related to superbee limiter so that WAF_superbee(r, c) = 1 - (1-|c|)*superbee(r)
* @param r_ the ratio of upwind change (see Toro 2001, p. 203/204)
* @param c_ the courant number for wave k, dt*S_k/dx
*/
__device__ float WAF_superbee(float r_, float c_) {
// r <= 0.0
if (r_ <= 0.0f) {
return 1.0f;
}
// 0.0 <= r <= 1/2
else if (r_ <= 0.5f) {
return 1.0f - 2.0f*(1.0f - fabsf(c_))*r_;
}
// 1/2 <= r <= 1
else if (r_ <= 1.0f) {
return fabs(c_);
}
// 1 <= r <= 2
else if (r_ <= 2.0f) {
return 1.0f - (1.0f - fabsf(c_))*r_;
}
// r >= 2
else {
return 2.0f*fabsf(c_) - 1.0f;
}
}
__device__ float WAF_albada(float r_, float c_) {
if (r_ <= 0.0f) {
return 1.0f;
}
else {
return 1.0f - (1.0f - fabsf(c_)) * r_ * (1.0f + r_) / (1.0f + r_*r_);
}
}
__device__ float WAF_minbee(float r_, float c_) {
r_ = fmaxf(-1.0f, fminf(2.0f, r_));
if (r_ <= 0.0f) {
return 1.0f;
}
if (r_ >= 0.0f && r_ <= 1.0f) {
return 1.0f - (1.0f - fabsf(c_)) * r_;
}
else {
return fabsf(c_);
}
}
__device__ float WAF_minmod(float r_, float c_) {
return 1.0f - (1.0f - fabsf(c_)) * fmaxf(0.0f, fminf(1.0f, r_));
}
__device__ float limiterToWAFLimiter(float r_, float c_) {
return 1.0f - (1.0f - fabsf(c_))*r_;
}
// Compute h in the "star region", h^dagger
__device__ __inline__ float computeHStar(float h_l, float h_r, float u_l, float u_r, float c_l, float c_r, float g_) {
//This estimate for the h* gives rise to spurious oscillations.
//return 0.5f * (h_l+h_r) - 0.25f * (u_r-u_l)*(h_l+h_r)/(c_l+c_r);
const float h_tmp = 0.5f * (c_l + c_r) + 0.25f * (u_l - u_r);
return h_tmp*h_tmp / g_;
}
/**
* Weighted average flux (Toro 2001, p 200) for interface {i+1/2}
* @param r_ The flux limiter parameter (see Toro 2001, p. 203)
* @param Q_l2 Q_{i-1}
* @param Q_l1 Q_{i}
* @param Q_r1 Q_{i+1}
* @param Q_r2 Q_{i+2}
*/
__device__ float3 WAF_1D_flux(const float3 Q_l2, const float3 Q_l1, const float3 Q_r1, const float3 Q_r2, const float g_, const float dx_, const float dt_) {
const float h_l = Q_l1.x;
const float h_r = Q_r1.x;
const float h_l2 = Q_l2.x;
const float h_r2 = Q_r2.x;
// Calculate velocities
const float u_l = Q_l1.y / h_l;
const float u_r = Q_r1.y / h_r;
const float u_l2 = Q_l2.y / h_l2;
const float u_r2 = Q_r2.y / h_r2;
const float v_l = Q_l1.z / h_l;
const float v_r = Q_r1.z / h_r;
const float v_l2 = Q_l2.z / h_l2;
const float v_r2 = Q_r2.z / h_r2;
// Estimate the potential wave speeds
const float c_l = sqrt(g_*h_l);
const float c_r = sqrt(g_*h_r);
const float c_l2 = sqrt(g_*h_l2);
const float c_r2 = sqrt(g_*h_r2);
// Compute h in the "star region", h^dagger
const float h_dag_l = computeHStar(h_l2, h_l, u_l2, u_l, c_l2, c_l, g_);
const float h_dag = computeHStar( h_l, h_r, u_l, u_r, c_l, c_r, g_);
const float h_dag_r = computeHStar( h_r, h_r2, u_r, u_r2, c_r, c_r2, g_);
const float q_l_tmp = sqrt(0.5f * ( (h_dag+h_l)*h_dag ) ) / h_l;
const float q_r_tmp = sqrt(0.5f * ( (h_dag+h_r)*h_dag ) ) / h_r;
const float q_l = (h_dag > h_l) ? q_l_tmp : 1.0f;
const float q_r = (h_dag > h_r) ? q_r_tmp : 1.0f;
// Compute wave speed estimates
const float S_l = u_l - c_l*q_l;
const float S_r = u_r + c_r*q_r;
const float S_star = ( S_l*h_r*(u_r - S_r) - S_r*h_l*(u_l - S_l) ) / ( h_r*(u_r - S_r) - h_l*(u_l - S_l) );
const float3 Q_star_l = h_l * (S_l - u_l) / (S_l - S_star) * make_float3(1.0, S_star, v_l);
const float3 Q_star_r = h_r * (S_r - u_r) / (S_r - S_star) * make_float3(1.0, S_star, v_r);
// Estimate the fluxes in the four regions
const float3 F_1 = F_func(Q_l1, g_);
const float3 F_4 = F_func(Q_r1, g_);
const float3 F_2 = F_1 + S_l*(Q_star_l - Q_l1);
const float3 F_3 = F_4 + S_r*(Q_star_r - Q_r1);
//const float3 F_2 = F_func(Q_star_l, g_);
//const float3 F_3 = F_func(Q_star_r, g_);
// Compute the courant numbers for the waves
const float c_1 = S_l * dt_ / dx_;
const float c_2 = S_star * dt_ / dx_;
const float c_3 = S_r * dt_ / dx_;
// Compute the "upwind change" vectors for the i-3/2 and i+3/2 interfaces
const float eps = 1.0e-6f;
const float r_1 = desingularize( (c_1 > 0.0f) ? (h_dag_l - h_l2) : (h_dag_r - h_r), eps) / desingularize((h_dag - h_l), eps);
const float r_2 = desingularize( (c_2 > 0.0f) ? (v_l - v_l2) : (v_r2 - v_r), eps ) / desingularize((v_r - v_l), eps);
const float r_3 = desingularize( (c_3 > 0.0f) ? (h_l - h_dag_l) : (h_r2 - h_dag_r), eps ) / desingularize((h_r - h_dag), eps);
// Compute the limiter
// We use h for the nonlinear waves, and v for the middle shear wave
const float A_1 = copysign(1.0f, c_1) * limiterToWAFLimiter(generalized_minmod(r_1, 1.9f), c_1);
const float A_2 = copysign(1.0f, c_2) * limiterToWAFLimiter(generalized_minmod(r_2, 1.9f), c_2);
const float A_3 = copysign(1.0f, c_3) * limiterToWAFLimiter(generalized_minmod(r_3, 1.9f), c_3);
//Average the fluxes
const float3 flux = 0.5f*( F_1 + F_4 )
- 0.5f*( A_1 * (F_2 - F_1)
+ A_2 * (F_3 - F_2)
+ A_3 * (F_4 - F_3) );
return flux;
}
/**
* Central upwind flux function
*/
__device__ float3 CentralUpwindFlux(const float3 Qm, float3 Qp, const float g) {
const float3 Fp = F_func(Qp, g);
const float up = Qp.y / Qp.x; // hu / h
const float cp = sqrt(g*Qp.x); // sqrt(g*h)
const float3 Fm = F_func(Qm, g);
const float um = Qm.y / Qm.x; // hu / h
const float cm = sqrt(g*Qm.x); // sqrt(g*h)
const float am = min(min(um-cm, up-cp), 0.0f); // largest negative wave speed
const float ap = max(max(um+cm, up+cp), 0.0f); // largest positive wave speed
return ((ap*Fm - am*Fp) + ap*am*(Qp-Qm))/(ap-am);
}
/**
* Godunovs centered scheme (Toro 2001, p 165)
*/
__device__ float3 GodC_1D_flux(const float3 Q_l, const float3 Q_r, const float g_, const float dx_, const float dt_) {
const float3 F_l = F_func(Q_l, g_);
const float3 F_r = F_func(Q_r, g_);
const float3 Q_godc = 0.5f*(Q_l + Q_r) + (dt_/dx_)*(F_l - F_r);
return F_func(Q_godc, g_);
}
/**
* Harten-Lax-van Leer with contact discontinuity (Toro 2001, p 180)
*/
__device__ float3 HLL_flux(const float3 Q_l, const float3 Q_r, const float g_) {
const float h_l = Q_l.x;
const float h_r = Q_r.x;
// Calculate velocities
const float u_l = Q_l.y / h_l;
const float u_r = Q_r.y / h_r;
// Estimate the potential wave speeds
const float c_l = sqrt(g_*h_l);
const float c_r = sqrt(g_*h_r);
// Compute h in the "star region", h^dagger
const float h_dag = 0.5f * (h_l+h_r) - 0.25f * (u_r-u_l)*(h_l+h_r)/(c_l+c_r);
const float q_l_tmp = sqrt(0.5f * ( (h_dag+h_l)*h_dag / (h_l*h_l) ) );
const float q_r_tmp = sqrt(0.5f * ( (h_dag+h_r)*h_dag / (h_r*h_r) ) );
const float q_l = (h_dag > h_l) ? q_l_tmp : 1.0f;
const float q_r = (h_dag > h_r) ? q_r_tmp : 1.0f;
// Compute wave speed estimates
const float S_l = u_l - c_l*q_l;
const float S_r = u_r + c_r*q_r;
//Upwind selection
if (S_l >= 0.0f) {
return F_func(Q_l, g_);
}
else if (S_r <= 0.0f) {
return F_func(Q_r, g_);
}
//Or estimate flux in the star region
else {
const float3 F_l = F_func(Q_l, g_);
const float3 F_r = F_func(Q_r, g_);
const float3 flux = (S_r*F_l - S_l*F_r + S_r*S_l*(Q_r - Q_l)) / (S_r-S_l);
return flux;
}
}
/**
* Harten-Lax-van Leer with contact discontinuity (Toro 2001, p 181)
*/
__device__ float3 HLLC_flux(const float3 Q_l, const float3 Q_r, const float g_) {
const float h_l = Q_l.x;
const float h_r = Q_r.x;
// Calculate velocities
const float u_l = Q_l.y / h_l;
const float u_r = Q_r.y / h_r;
// Estimate the potential wave speeds
const float c_l = sqrt(g_*h_l);
const float c_r = sqrt(g_*h_r);
// Compute h in the "star region", h^dagger
const float h_dag = 0.5f * (h_l+h_r) - 0.25f * (u_r-u_l)*(h_l+h_r)/(c_l+c_r);
const float q_l_tmp = sqrt(0.5f * ( (h_dag+h_l)*h_dag / (h_l*h_l) ) );
const float q_r_tmp = sqrt(0.5f * ( (h_dag+h_r)*h_dag / (h_r*h_r) ) );
const float q_l = (h_dag > h_l) ? q_l_tmp : 1.0f;
const float q_r = (h_dag > h_r) ? q_r_tmp : 1.0f;
// Compute wave speed estimates
const float S_l = u_l - c_l*q_l;
const float S_r = u_r + c_r*q_r;
const float S_star = ( S_l*h_r*(u_r - S_r) - S_r*h_l*(u_l - S_l) ) / ( h_r*(u_r - S_r) - h_l*(u_l - S_l) );
const float3 F_l = F_func(Q_l, g_);
const float3 F_r = F_func(Q_r, g_);
//Upwind selection
if (S_l >= 0.0f) {
return F_l;
}
else if (S_r <= 0.0f) {
return F_r;
}
//Or estimate flux in the "left star" region
else if (S_l <= 0.0f && 0.0f <=S_star) {
const float v_l = Q_l.z / h_l;
const float3 Q_star_l = h_l * (S_l - u_l) / (S_l - S_star) * make_float3(1, S_star, v_l);
const float3 flux = F_l + S_l*(Q_star_l - Q_l);
return flux;
}
//Or estimate flux in the "righ star" region
else if (S_star <= 0.0f && 0.0f <=S_r) {
const float v_r = Q_r.z / h_r;
const float3 Q_star_r = h_r * (S_r - u_r) / (S_r - S_star) * make_float3(1, S_star, v_r);
const float3 flux = F_r + S_r*(Q_star_r - Q_r);
return flux;
}
else {
return make_float3(-99999.9f, -99999.9f, -99999.9f); //Something wrong here
}
}
/**
* Lax-Friedrichs flux (Toro 2001, p 163)
*/
__device__ float3 LxF_1D_flux(const float3 Q_l, const float3 Q_r, const float g_, const float dx_, const float dt_) {
const float3 F_l = F_func(Q_l, g_);
const float3 F_r = F_func(Q_r, g_);
return 0.5f*(F_l + F_r) + (dx_/(2.0f*dt_))*(Q_l - Q_r);
}
/**
* Lax-Friedrichs extended to 2D
*/
__device__ float3 LxF_2D_flux(const float3 Q_l, const float3 Q_r, const float g_, const float dx_, const float dt_) {
const float3 F_l = F_func(Q_l, g_);
const float3 F_r = F_func(Q_r, g_);
//Note numerical diffusion for 2D here (0.25)
return 0.5f*(F_l + F_r) + (dx_/(4.0f*dt_))*(Q_l - Q_r);
}
/**
* Richtmeyer / Two-step Lax-Wendroff flux (Toro 2001, p 164)
*/
__device__ float3 LxW2_1D_flux(const float3 Q_l, const float3 Q_r, const float g_, const float dx_, const float dt_) {
const float3 F_l = F_func(Q_l, g_);
const float3 F_r = F_func(Q_r, g_);
const float3 Q_lw2 = 0.5f*(Q_l + Q_r) + (dt_/(2.0f*dx_))*(F_l - F_r);
return F_func(Q_lw2, g_);
}
/**
* First Ordered Centered (Toro 2001, p.163)
*/
__device__ float3 FORCE_1D_flux(const float3 Q_l, const float3 Q_r, const float g_, const float dx_, const float dt_) {
const float3 F_lf = LxF_1D_flux(Q_l, Q_r, g_, dx_, dt_);
const float3 F_lw2 = LxW2_1D_flux(Q_l, Q_r, g_, dx_, dt_);
return 0.5f*(F_lf + F_lw2);
}

5
GPUSimulators/FORCE_kernel.cu → GPUSimulators/cuda/SWE_FORCE.cu
View File

@ -19,9 +19,8 @@ along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
#include "common.cu"
#include "SWECommon.cu"
#include "fluxes/FirstOrderCentered.cu"
#include "common.h"
#include "SWECommon.h"
/**

5
GPUSimulators/HLL_kernel.cu → GPUSimulators/cuda/SWE_HLL.cu
View File

@ -19,9 +19,8 @@ along with this program. If not, see <http://www.gnu.org/licenses/>.
#include "common.cu"
#include "SWECommon.cu"
#include "fluxes/HartenLaxVanLeer.cu"
#include "common.h"
#include "SWECommon.h"

7
GPUSimulators/HLL2_kernel.cu → GPUSimulators/cuda/SWE_HLL2.cu
View File

@ -18,10 +18,9 @@ along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
#include "common.cu"
#include "SWECommon.cu"
#include "limiters.cu"
#include "fluxes/HartenLaxVanLeer.cu"
#include "common.h"
#include "SWECommon.h"
#include "limiters.h"

7
GPUSimulators/KP07_kernel.cu → GPUSimulators/cuda/SWE_KP07.cu
View File

@ -24,10 +24,9 @@ along with this program. If not, see <http://www.gnu.org/licenses/>.
#include "common.cu"
#include "SWECommon.cu"
#include "limiters.cu"
#include "fluxes/CentralUpwind.cu"
#include "common.h"
#include "SWECommon.h"
#include "limiters.h"
__device__

7
GPUSimulators/KP07_dimsplit_kernel.cu → GPUSimulators/cuda/SWE_KP07_dimsplit.cu
View File

@ -24,10 +24,9 @@ along with this program. If not, see <http://www.gnu.org/licenses/>.
#include "common.cu"
#include "SWECommon.cu"
#include "limiters.cu"
#include "fluxes/CentralUpwind.cu"
#include "common.h"
#include "SWECommon.h"
#include "limiters.h"
__device__

5
GPUSimulators/LxF_kernel.cu → GPUSimulators/cuda/SWE_LxF.cu
View File

@ -19,9 +19,8 @@ along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
#include "Common.cu"
#include "SWECommon.cu"
#include "fluxes/LaxFriedrichs.cu"
#include "common.h"
#include "SWECommon.h"
/**

5
GPUSimulators/WAF_kernel.cu → GPUSimulators/cuda/SWE_WAF.cu
View File

@ -24,9 +24,8 @@ along with this program. If not, see <http://www.gnu.org/licenses/>.
#include "common.cu"
#include "SWECommon.cu"
#include "fluxes/WeightedAverageFlux.cu"
#include "common.h"
#include "SWECommon.h"

32
GPUSimulators/common.cu → GPUSimulators/cuda/common.h
View File

@ -22,6 +22,7 @@ You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
#pragma once
/**
@ -50,34 +51,17 @@ inline __device__ __host__ float clamp(const float f, const float a, const float
/**
* Reads a block of data with one ghost cell for the shallow water equations
*/
template<int sm_width, int sm_height, int block_width, int block_height>
inline __device__ void readBlock(float* ptr_, int pitch_,
float (&shmem)[sm_height][sm_width],
const int max_x_, const int max_y_) {
//Index of block within domain
const int bx = blockDim.x * blockIdx.x;
const int by = blockDim.y * blockIdx.y;
//Read into shared memory
for (int j=threadIdx.y; j<sm_height; j+=block_height) {
const int l = clamp(by + j, 0, max_y_-1); // Clamp out of bounds
//Compute the pointer to current row in the arrays
const float* const row = (float*) ((char*) ptr_ + pitch_*l);
for (int i=threadIdx.x; i<sm_width; i+=block_width) {
const int k = clamp(bx + i, 0, max_x_-1); // Clamp out of bounds
shmem[j][i] = row[k];
}
}
__device__ float desingularize(float x_, float eps_) {
return copysign(1.0f, x_)*fmaxf(fabsf(x_), fminf(x_*x_/(2.0f*eps_)+0.5f*eps_, eps_));
}
/**
* Reads a block of data with ghost cells
*/

2
GPUSimulators/limiters.cu → GPUSimulators/cuda/limiters.h
View File

@ -17,7 +17,7 @@ You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
#pragma once

39
GPUSimulators/fluxes/CentralUpwind.cu
View File

@ -1,39 +0,0 @@
/*
This file implements the Central upwind flux
Copyright (C) 2016, 2017, 2018 SINTEF ICT
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
/**
* Central upwind flux function
*/
__device__ float3 CentralUpwindFlux(const float3 Qm, float3 Qp, const float g) {
const float3 Fp = F_func(Qp, g);
const float up = Qp.y / Qp.x; // hu / h
const float cp = sqrt(g*Qp.x); // sqrt(g*h)
const float3 Fm = F_func(Qm, g);
const float um = Qm.y / Qm.x; // hu / h
const float cm = sqrt(g*Qm.x); // sqrt(g*h)
const float am = min(min(um-cm, up-cp), 0.0f); // largest negative wave speed
const float ap = max(max(um+cm, up+cp), 0.0f); // largest positive wave speed
return ((ap*Fm - am*Fp) + ap*am*(Qp-Qm))/(ap-am);
}

33
GPUSimulators/fluxes/FirstOrderCentered.cu
View File

@ -1,33 +0,0 @@
/*
This file implements the First ORder CEntered flux
Copyright (C) 2016, 2017, 2018 SINTEF ICT
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
#include "./LaxFriedrichs.cu"
#include "./LaxWendroff.cu"
/**
* First Ordered Centered (Toro 2001, p.163)
*/
__device__ float3 FORCE_1D_flux(const float3 Q_l, const float3 Q_r, const float g_, const float dx_, const float dt_) {
const float3 F_lf = LxF_1D_flux(Q_l, Q_r, g_, dx_, dt_);
const float3 F_lw2 = LxW2_1D_flux(Q_l, Q_r, g_, dx_, dt_);
return 0.5f*(F_lf + F_lw2);
}

36
GPUSimulators/fluxes/Godunov.cu
View File

@ -1,36 +0,0 @@
/*
This file implements the Godunov flux
Copyright (C) 2016, 2017, 2018 SINTEF ICT
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
/**
* Godunovs centered scheme (Toro 2001, p 165)
*/
__device__ float3 GodC_1D_flux(const float3 Q_l, const float3 Q_r, const float g_, const float dx_, const float dt_) {
const float3 F_l = F_func(Q_l, g_);
const float3 F_r = F_func(Q_r, g_);
const float3 Q_godc = 0.5f*(Q_l + Q_r) + (dt_/dx_)*(F_l - F_r);
return F_func(Q_godc, g_);
}

63
GPUSimulators/fluxes/HartenLaxVanLeer.cu
View File

@ -1,63 +0,0 @@
/*
This file implements the Harten-Lax-van Leer flux
Copyright (C) 2016, 2017, 2018 SINTEF ICT
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
/**
* Harten-Lax-van Leer with contact discontinuity (Toro 2001, p 180)
*/
__device__ float3 HLL_flux(const float3 Q_l, const float3 Q_r, const float g_) {
const float h_l = Q_l.x;
const float h_r = Q_r.x;
// Calculate velocities
const float u_l = Q_l.y / h_l;
const float u_r = Q_r.y / h_r;
// Estimate the potential wave speeds
const float c_l = sqrt(g_*h_l);
const float c_r = sqrt(g_*h_r);
// Compute h in the "star region", h^dagger
const float h_dag = 0.5f * (h_l+h_r) - 0.25f * (u_r-u_l)*(h_l+h_r)/(c_l+c_r);
const float q_l_tmp = sqrt(0.5f * ( (h_dag+h_l)*h_dag / (h_l*h_l) ) );
const float q_r_tmp = sqrt(0.5f * ( (h_dag+h_r)*h_dag / (h_r*h_r) ) );
const float q_l = (h_dag > h_l) ? q_l_tmp : 1.0f;
const float q_r = (h_dag > h_r) ? q_r_tmp : 1.0f;
// Compute wave speed estimates
const float S_l = u_l - c_l*q_l;
const float S_r = u_r + c_r*q_r;
//Upwind selection
if (S_l >= 0.0f) {
return F_func(Q_l, g_);
}
else if (S_r <= 0.0f) {
return F_func(Q_r, g_);
}
//Or estimate flux in the star region
else {
const float3 F_l = F_func(Q_l, g_);
const float3 F_r = F_func(Q_r, g_);
const float3 flux = (S_r*F_l - S_l*F_r + S_r*S_l*(Q_r - Q_l)) / (S_r-S_l);
return flux;
}
}

80
GPUSimulators/fluxes/HartenLaxVanLeerContact.cu
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@ -1,80 +0,0 @@
/*
This file implements the Harten-Lax-van Leer flux with contact discontinuity
Copyright (C) 2016, 2017, 2018 SINTEF ICT
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
/**
* Harten-Lax-van Leer with contact discontinuity (Toro 2001, p 181)
*/
__device__ float3 HLLC_flux(const float3 Q_l, const float3 Q_r, const float g_) {
const float h_l = Q_l.x;
const float h_r = Q_r.x;
// Calculate velocities
const float u_l = Q_l.y / h_l;
const float u_r = Q_r.y / h_r;
// Estimate the potential wave speeds
const float c_l = sqrt(g_*h_l);
const float c_r = sqrt(g_*h_r);
// Compute h in the "star region", h^dagger
const float h_dag = 0.5f * (h_l+h_r) - 0.25f * (u_r-u_l)*(h_l+h_r)/(c_l+c_r);
const float q_l_tmp = sqrt(0.5f * ( (h_dag+h_l)*h_dag / (h_l*h_l) ) );
const float q_r_tmp = sqrt(0.5f * ( (h_dag+h_r)*h_dag / (h_r*h_r) ) );
const float q_l = (h_dag > h_l) ? q_l_tmp : 1.0f;
const float q_r = (h_dag > h_r) ? q_r_tmp : 1.0f;
// Compute wave speed estimates
const float S_l = u_l - c_l*q_l;
const float S_r = u_r + c_r*q_r;
const float S_star = ( S_l*h_r*(u_r - S_r) - S_r*h_l*(u_l - S_l) ) / ( h_r*(u_r - S_r) - h_l*(u_l - S_l) );
const float3 F_l = F_func(Q_l, g_);
const float3 F_r = F_func(Q_r, g_);
//Upwind selection
if (S_l >= 0.0f) {
return F_l;
}
else if (S_r <= 0.0f) {
return F_r;
}
//Or estimate flux in the "left star" region
else if (S_l <= 0.0f && 0.0f <=S_star) {
const float v_l = Q_l.z / h_l;
const float3 Q_star_l = h_l * (S_l - u_l) / (S_l - S_star) * make_float3(1, S_star, v_l);
const float3 flux = F_l + S_l*(Q_star_l - Q_l);
return flux;
}
//Or estimate flux in the "righ star" region
else if (S_star <= 0.0f && 0.0f <=S_r) {
const float v_r = Q_r.z / h_r;
const float3 Q_star_r = h_r * (S_r - u_r) / (S_r - S_star) * make_float3(1, S_star, v_r);
const float3 flux = F_r + S_r*(Q_star_r - Q_r);
return flux;
}
else {
return make_float3(-99999.9f, -99999.9f, -99999.9f); //Something wrong here
}
}

24
GPUSimulators/fluxes/LaxFriedrichs.cu
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/**
* Lax-Friedrichs flux (Toro 2001, p 163)
*/
__device__ float3 LxF_1D_flux(const float3 Q_l, const float3 Q_r, const float g_, const float dx_, const float dt_) {
const float3 F_l = F_func(Q_l, g_);
const float3 F_r = F_func(Q_r, g_);
return 0.5f*(F_l + F_r) + (dx_/(2.0f*dt_))*(Q_l - Q_r);
}
/**
* Lax-Friedrichs extended to 2D
*/
__device__ float3 LxF_2D_flux(const float3 Q_l, const float3 Q_r, const float g_, const float dx_, const float dt_) {
const float3 F_l = F_func(Q_l, g_);
const float3 F_r = F_func(Q_r, g_);
//Note numerical diffusion for 2D here (0.25)
return 0.5f*(F_l + F_r) + (dx_/(4.0f*dt_))*(Q_l - Q_r);
}

33
GPUSimulators/fluxes/LaxWendroff.cu
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@ -1,33 +0,0 @@
/*
This file implements the Lax-Wendroff flux
Copyright (C) 2016, 2017, 2018 SINTEF ICT
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
/**
* Richtmeyer / Two-step Lax-Wendroff flux (Toro 2001, p 164)
*/
__device__ float3 LxW2_1D_flux(const float3 Q_l, const float3 Q_r, const float g_, const float dx_, const float dt_) {
const float3 F_l = F_func(Q_l, g_);
const float3 F_r = F_func(Q_r, g_);
const float3 Q_lw2 = 0.5f*(Q_l + Q_r) + (dt_/(2.0f*dx_))*(F_l - F_r);
return F_func(Q_lw2, g_);
}

187
GPUSimulators/fluxes/WeightedAverageFlux.cu
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@ -1,187 +0,0 @@
/*
This file implements the Weighted Average Flux
Copyright (C) 2016, 2017, 2018 SINTEF ICT
This program is free software: you can redistribute it and/or modify
it under the terms of the GNU General Public License as published by
the Free Software Foundation, either version 3 of the License, or
(at your option) any later version.
This program is distributed in the hope that it will be useful,
but WITHOUT ANY WARRANTY; without even the implied warranty of
MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
GNU General Public License for more details.
You should have received a copy of the GNU General Public License
along with this program. If not, see <http://www.gnu.org/licenses/>.
*/
#include "limiters.cu"
/**
* Superbee flux limiter for WAF.
* Related to superbee limiter so that WAF_superbee(r, c) = 1 - (1-|c|)*superbee(r)
* @param r_ the ratio of upwind change (see Toro 2001, p. 203/204)
* @param c_ the courant number for wave k, dt*S_k/dx
*/
__device__ float WAF_superbee(float r_, float c_) {
// r <= 0.0
if (r_ <= 0.0f) {
return 1.0f;
}
// 0.0 <= r <= 1/2
else if (r_ <= 0.5f) {
return 1.0f - 2.0f*(1.0f - fabsf(c_))*r_;
}
// 1/2 <= r <= 1
else if (r_ <= 1.0f) {
return fabs(c_);
}
// 1 <= r <= 2
else if (r_ <= 2.0f) {
return 1.0f - (1.0f - fabsf(c_))*r_;
}
// r >= 2
else {
return 2.0f*fabsf(c_) - 1.0f;
}
}
__device__ float WAF_albada(float r_, float c_) {
if (r_ <= 0.0f) {
return 1.0f;
}
else {
return 1.0f - (1.0f - fabsf(c_)) * r_ * (1.0f + r_) / (1.0f + r_*r_);
}
}
__device__ float WAF_minbee(float r_, float c_) {
r_ = fmaxf(-1.0f, fminf(2.0f, r_));
if (r_ <= 0.0f) {
return 1.0f;
}
if (r_ >= 0.0f && r_ <= 1.0f) {
return 1.0f - (1.0f - fabsf(c_)) * r_;
}
else {
return fabsf(c_);
}
}
__device__ float WAF_minmod(float r_, float c_) {
return 1.0f - (1.0f - fabsf(c_)) * fmaxf(0.0f, fminf(1.0f, r_));
}
__device__ float limiterToWAFLimiter(float r_, float c_) {
return 1.0f - (1.0f - fabsf(c_))*r_;
}
__device__ float desingularize(float x_, float eps_) {
return copysign(1.0f, x_)*fmaxf(fabsf(x_), fminf(x_*x_/(2.0f*eps_)+0.5f*eps_, eps_));
}
// Compute h in the "star region", h^dagger
__device__ __inline__ float computeHStar(float h_l, float h_r, float u_l, float u_r, float c_l, float c_r, float g_) {
//This estimate for the h* gives rise to spurious oscillations.
//return 0.5f * (h_l+h_r) - 0.25f * (u_r-u_l)*(h_l+h_r)/(c_l+c_r);
const float h_tmp = 0.5f * (c_l + c_r) + 0.25f * (u_l - u_r);
return h_tmp*h_tmp / g_;
}
/**
* Weighted average flux (Toro 2001, p 200) for interface {i+1/2}
* @param r_ The flux limiter parameter (see Toro 2001, p. 203)
* @param Q_l2 Q_{i-1}
* @param Q_l1 Q_{i}
* @param Q_r1 Q_{i+1}
* @param Q_r2 Q_{i+2}
*/
__device__ float3 WAF_1D_flux(const float3 Q_l2, const float3 Q_l1, const float3 Q_r1, const float3 Q_r2, const float g_, const float dx_, const float dt_) {
const float h_l = Q_l1.x;
const float h_r = Q_r1.x;
const float h_l2 = Q_l2.x;
const float h_r2 = Q_r2.x;
// Calculate velocities
const float u_l = Q_l1.y / h_l;
const float u_r = Q_r1.y / h_r;
const float u_l2 = Q_l2.y / h_l2;
const float u_r2 = Q_r2.y / h_r2;
const float v_l = Q_l1.z / h_l;
const float v_r = Q_r1.z / h_r;
const float v_l2 = Q_l2.z / h_l2;
const float v_r2 = Q_r2.z / h_r2;
// Estimate the potential wave speeds
const float c_l = sqrt(g_*h_l);
const float c_r = sqrt(g_*h_r);
const float c_l2 = sqrt(g_*h_l2);
const float c_r2 = sqrt(g_*h_r2);
// Compute h in the "star region", h^dagger
const float h_dag_l = computeHStar(h_l2, h_l, u_l2, u_l, c_l2, c_l, g_);
const float h_dag = computeHStar( h_l, h_r, u_l, u_r, c_l, c_r, g_);
const float h_dag_r = computeHStar( h_r, h_r2, u_r, u_r2, c_r, c_r2, g_);
const float q_l_tmp = sqrt(0.5f * ( (h_dag+h_l)*h_dag ) ) / h_l;
const float q_r_tmp = sqrt(0.5f * ( (h_dag+h_r)*h_dag ) ) / h_r;
const float q_l = (h_dag > h_l) ? q_l_tmp : 1.0f;
const float q_r = (h_dag > h_r) ? q_r_tmp : 1.0f;
// Compute wave speed estimates
const float S_l = u_l - c_l*q_l;
const float S_r = u_r + c_r*q_r;
const float S_star = ( S_l*h_r*(u_r - S_r) - S_r*h_l*(u_l - S_l) ) / ( h_r*(u_r - S_r) - h_l*(u_l - S_l) );
const float3 Q_star_l = h_l * (S_l - u_l) / (S_l - S_star) * make_float3(1.0, S_star, v_l);
const float3 Q_star_r = h_r * (S_r - u_r) / (S_r - S_star) * make_float3(1.0, S_star, v_r);
// Estimate the fluxes in the four regions
const float3 F_1 = F_func(Q_l1, g_);
const float3 F_4 = F_func(Q_r1, g_);
const float3 F_2 = F_1 + S_l*(Q_star_l - Q_l1);
const float3 F_3 = F_4 + S_r*(Q_star_r - Q_r1);
//const float3 F_2 = F_func(Q_star_l, g_);
//const float3 F_3 = F_func(Q_star_r, g_);
// Compute the courant numbers for the waves
const float c_1 = S_l * dt_ / dx_;
const float c_2 = S_star * dt_ / dx_;
const float c_3 = S_r * dt_ / dx_;
// Compute the "upwind change" vectors for the i-3/2 and i+3/2 interfaces
const float eps = 1.0e-6f;
const float r_1 = desingularize( (c_1 > 0.0f) ? (h_dag_l - h_l2) : (h_dag_r - h_r), eps) / desingularize((h_dag - h_l), eps);
const float r_2 = desingularize( (c_2 > 0.0f) ? (v_l - v_l2) : (v_r2 - v_r), eps ) / desingularize((v_r - v_l), eps);
const float r_3 = desingularize( (c_3 > 0.0f) ? (h_l - h_dag_l) : (h_r2 - h_dag_r), eps ) / desingularize((h_r - h_dag), eps);
// Compute the limiter
// We use h for the nonlinear waves, and v for the middle shear wave
const float A_1 = copysign(1.0f, c_1) * limiterToWAFLimiter(generalized_minmod(r_1, 1.9f), c_1);
const float A_2 = copysign(1.0f, c_2) * limiterToWAFLimiter(generalized_minmod(r_2, 1.9f), c_2);
const float A_3 = copysign(1.0f, c_3) * limiterToWAFLimiter(generalized_minmod(r_3, 1.9f), c_3);
//Average the fluxes
const float3 flux = 0.5f*( F_1 + F_4 )
- 0.5f*( A_1 * (F_2 - F_1)
+ A_2 * (F_3 - F_2)
+ A_3 * (F_4 - F_3) );
return flux;
}