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
@@ -34,7 +34,6 @@ import pycuda.compiler as cuda_compiler
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import pycuda.gpuarray
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import pycuda.driver as cuda
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from GPUSimulators import Autotuner
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"""
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Class which keeps track of time spent for a section of code
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@@ -58,225 +57,6 @@ class Timer(object):
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"""
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Class which keeps track of the CUDA context and some helper functions
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"""
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class CudaContext(object):
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def __init__(self, blocking=False, use_cache=True, autotuning=True):
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self.blocking = blocking
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self.use_cache = use_cache
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self.logger = logging.getLogger(__name__)
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self.kernels = {}
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self.module_path = os.path.dirname(os.path.realpath(__file__))
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#Initialize cuda (must be first call to PyCUDA)
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cuda.init(flags=0)
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self.logger.info("PyCUDA version %s", str(pycuda.VERSION_TEXT))
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#Print some info about CUDA
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self.logger.info("CUDA version %s", str(cuda.get_version()))
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self.logger.info("Driver version %s", str(cuda.get_driver_version()))
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self.cuda_device = cuda.Device(0)
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self.logger.info("Using '%s' GPU", self.cuda_device.name())
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self.logger.debug(" => compute capability: %s", str(self.cuda_device.compute_capability()))
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# Create the CUDA context
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if (self.blocking):
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self.cuda_context = self.cuda_device.make_context(flags=cuda.ctx_flags.SCHED_BLOCKING_SYNC)
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self.logger.warning("Using blocking context")
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else:
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self.cuda_context = self.cuda_device.make_context(flags=cuda.ctx_flags.SCHED_AUTO)
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free, total = cuda.mem_get_info()
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self.logger.debug(" => memory: %d / %d MB available", int(free/(1024*1024)), int(total/(1024*1024)))
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self.logger.info("Created context handle <%s>", str(self.cuda_context.handle))
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#Create cache dir for cubin files
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if (self.use_cache):
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self.cache_path = os.path.join(self.module_path, "cuda_cache")
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if not os.path.isdir(self.cache_path):
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os.mkdir(self.cache_path)
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self.logger.info("Using CUDA cache dir %s", self.cache_path)
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self.autotuner = None
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if (autotuning):
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self.logger.info("Autotuning enabled. It may take several minutes to run the code the first time: have patience")
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self.autotuner = Autotuner.Autotuner()
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def __del__(self, *args):
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self.logger.info("Cleaning up CUDA context handle <%s>", str(self.cuda_context.handle))
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# Loop over all contexts in stack, and remove "this"
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other_contexts = []
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while (cuda.Context.get_current() != None):
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context = cuda.Context.get_current()
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if (context.handle != self.cuda_context.handle):
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self.logger.debug("<%s> Popping <%s> (*not* ours)", str(self.cuda_context.handle), str(context.handle))
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other_contexts = [context] + other_contexts
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cuda.Context.pop()
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else:
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self.logger.debug("<%s> Popping <%s> (ours)", str(self.cuda_context.handle), str(context.handle))
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cuda.Context.pop()
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# Add all the contexts we popped that were not our own
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for context in other_contexts:
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self.logger.debug("<%s> Pushing <%s>", str(self.cuda_context.handle), str(context.handle))
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cuda.Context.push(context)
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self.logger.debug("<%s> Detaching", str(self.cuda_context.handle))
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self.cuda_context.detach()
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def __str__(self):
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return "CudaContext id " + str(self.cuda_context.handle)
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def hash_kernel(kernel_filename, include_dirs):
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# Generate a kernel ID for our caches
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num_includes = 0
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max_includes = 100
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kernel_hasher = hashlib.md5()
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logger = logging.getLogger(__name__)
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# Loop over file and includes, and check if something has changed
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files = [kernel_filename]
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while len(files):
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if (num_includes > max_includes):
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raise("Maximum number of includes reached - circular include in {:}?".format(kernel_filename))
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filename = files.pop()
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#logger.debug("Hashing %s", filename)
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modified = os.path.getmtime(filename)
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# Open the file
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with io.open(filename, "r") as file:
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# Search for #inclue <something> and also hash the file
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file_str = file.read()
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kernel_hasher.update(file_str.encode('utf-8'))
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kernel_hasher.update(str(modified).encode('utf-8'))
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#Find all includes
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includes = re.findall('^\W*#include\W+(.+?)\W*$', file_str, re.M)
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# Loop over everything that looks like an include
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for include_file in includes:
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#Search through include directories for the file
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file_path = os.path.dirname(filename)
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for include_path in [file_path] + include_dirs:
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# If we find it, add it to list of files to check
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temp_path = os.path.join(include_path, include_file)
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if (os.path.isfile(temp_path)):
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files = files + [temp_path]
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num_includes = num_includes + 1 #For circular includes...
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break
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return kernel_hasher.hexdigest()
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"""
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Reads a text file and creates an OpenCL kernel from that
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"""
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def get_prepared_kernel(self, kernel_filename, kernel_function_name, \
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prepared_call_args, \
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include_dirs=[], no_extern_c=True,
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**kwargs):
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"""
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Helper function to print compilation output
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"""
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def cuda_compile_message_handler(compile_success_bool, info_str, error_str):
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self.logger.debug("Compilation returned %s", str(compile_success_bool))
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if info_str:
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self.logger.debug("Info: %s", info_str)
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if error_str:
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self.logger.debug("Error: %s", error_str)
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#self.logger.debug("Getting %s", kernel_filename)
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# Create a hash of the kernel (and its includes)
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kwargs_hasher = hashlib.md5()
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kwargs_hasher.update(str(kwargs).encode('utf-8'));
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kwargs_hash = kwargs_hasher.hexdigest()
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kwargs_hasher = None
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root, ext = os.path.splitext(kernel_filename)
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kernel_hash = root \
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+ "_" + CudaContext.hash_kernel( \
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os.path.join(self.module_path, kernel_filename), \
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include_dirs=[self.module_path] + include_dirs) \
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+ "_" + kwargs_hash \
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+ ext
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cached_kernel_filename = os.path.join(self.cache_path, kernel_hash)
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# If we have the kernel in our hashmap, return it
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if (kernel_hash in self.kernels.keys()):
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self.logger.debug("Found kernel %s cached in hashmap (%s)", kernel_filename, kernel_hash)
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return self.kernels[kernel_hash]
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# If we have it on disk, return it
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elif (self.use_cache and os.path.isfile(cached_kernel_filename)):
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self.logger.debug("Found kernel %s cached on disk (%s)", kernel_filename, kernel_hash)
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with io.open(cached_kernel_filename, "rb") as file:
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file_str = file.read()
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module = cuda.module_from_buffer(file_str, message_handler=cuda_compile_message_handler)
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kernel = module.get_function(kernel_function_name)
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kernel.prepare(prepared_call_args)
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self.kernels[kernel_hash] = kernel
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return kernel
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# Otherwise, compile it from source
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else:
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self.logger.debug("Compiling %s (%s)", kernel_filename, kernel_hash)
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#Create kernel string
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kernel_string = ""
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for key, value in kwargs.items():
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kernel_string += "#define {:s} {:s}\n".format(str(key), str(value))
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kernel_string += '#include "{:s}"'.format(os.path.join(self.module_path, kernel_filename))
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if (self.use_cache):
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with io.open(cached_kernel_filename + ".txt", "w") as file:
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file.write(kernel_string)
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with Timer("compiler") as timer:
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cubin = cuda_compiler.compile(kernel_string, include_dirs=include_dirs, no_extern_c=no_extern_c, cache_dir=False)
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module = cuda.module_from_buffer(cubin, message_handler=cuda_compile_message_handler)
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if (self.use_cache):
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with io.open(cached_kernel_filename, "wb") as file:
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file.write(cubin)
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kernel = module.get_function(kernel_function_name)
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kernel.prepare(prepared_call_args)
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self.kernels[kernel_hash] = kernel
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return kernel
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"""
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Clears the kernel cache (useful for debugging & development)
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"""
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def clear_kernel_cache(self):
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self.logger.debug("Clearing cache")
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self.kernels = {}
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gc.collect()
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"""
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Synchronizes all streams etc
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"""
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def synchronize(self):
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self.cuda_context.synchronize()
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