def index_to_ijk(self, index: List[int]):
"""
Creates a string of the access (for variable name generation).
:param index: access
:return: created string
"""
# current implementation only supports 3 dimension (default)
if len(index) == 3:
"""
# v1:
return "[i{},j{},k{}]".format(
"" if index[0] == 0 else "+{}".format(index[0]),
"" if index[1] == 0 else "+{}".format(index[1]),
"" if index[2] == 0 else "+{}".format(index[2])
)
# v2:
return "_{}_{}_{}".format(index[0], index[1], index[2])
"""
# compute absolute index
ind = stencilflow.convert_3d_to_1d(dimensions=self.dimensions,
index=index)
# return formatted string
return "_{}".format(ind) if ind >= 0 else "_n{}".format(abs(ind))
else:
raise NotImplementedError(
"Method index_to_ijk has not been implemented for |indices|!=3, here: |indices|={}"
.format(len(index)))
def set_up_dist_to_center(self):
"""
Computes for all fields/channels the distance from the furthest field access to the center of the stencil
([0,0,0,]).
"""
for item in self.graph.accesses:
furthest = max(self.graph.accesses[item])
self.dist_to_center[item] = stencilflow.convert_3d_to_1d(
dimensions=self.dimensions, index=furthest)
def setup_internal_buffers(self) -> None:
"""
Create and split the internal buffers according to the pipline model (see paper example ref# TODO)
:return:
"""
# remove duplicate accesses
for item in self.graph.accesses:
self.graph.accesses[item] = self.remove_duplicate_accesses(
self.graph.accesses[item])
# slice the internal buffer into junks of accesses
for buf_name in self.graph.buffer_size:
# create empty list and sort the accesses according to their relative position
self.internal_buffer[buf_name]: List[BoundedQueue] = list()
list.sort(self.graph.accesses[buf_name], reverse=True)
# split according to the cases
if len(self.graph.accesses[buf_name]) == 0: # empty list
pass
elif len(self.graph.accesses[buf_name]) == 1: # single entry list
# this line would add an additional internal buffer for fields that only have a single access
self.internal_buffer[buf_name].append(
BoundedQueue(name=buf_name, maxsize=1, collection=[None]))
else: # many entry list
# iterate through all of them and split them into correct sizes
itr = self.graph.accesses[buf_name].__iter__()
pre = itr.__next__()
for item in itr:
curr = item
# calculate size of buffer
diff = abs(
stencilflow.convert_3d_to_1d(
index=stencilflow.list_subtract_cwise(pre, curr),
dimensions=self.dimensions))
if diff == 0: # two accesses on same field
pass
else:
self.internal_buffer[buf_name].append(
BoundedQueue(name=buf_name,
maxsize=diff,
collection=[None] * diff))
pre = curr
def __init__(self,
name: str,
kernel_string: str,
dimensions: List[int],
data_type: dace.dtypes.typeclass,
boundary_conditions: Dict[str, Dict[str, str]],
raw_inputs,
vectorization: int = 1,
plot_graph: bool = False,
verbose: bool = False) -> None:
"""
:param name: name of the kernel
:param kernel_string: mathematical expression representing the stencil computation
:param dimensions: global dimensions / problem size (i.e. size of the input array
:param data_type: data type of the result produced by this kernel
:param boundary_conditions: dictionary of the boundary condition for each input channel/field
:param plot_graph: flag indicating whether the underlying graph is being drawn
:param verbose: flag for console output logging
"""
# initialize the superclass
super().__init__(name, BoundedQueue(name="dummy", maxsize=0), data_type)
# store arguments
self.kernel_string: str = kernel_string # raw kernel string input
self.raw_inputs = raw_inputs
self.dimensions: List[
int] = dimensions # input array dimensions [dimX, dimY, dimZ]
self.boundary_conditions: Dict[str, Dict[
str, str]] = boundary_conditions # boundary_conditions[field_name]
self.verbose = verbose
self.vectorization = vectorization
# read static parameters from config
self.config: Dict = stencilflow.parse_json("kernel.config")
self.calculator: Calculator = Calculator()
# set simulator initial parameters
self.all_available = False
self.not_available = set()
# analyze input
self.graph: ComputeGraph = ComputeGraph(vectorization=vectorization,
dimensions=dimensions,
raw_inputs=raw_inputs)
self.graph.generate_graph(
kernel_string
) # generate the ast computation graph from the mathematical expression
self.graph.calculate_latency(
) # calculate the latency in the computation tree to find the critical path
self.graph.determine_inputs_outputs(
) # sort out input nodes (field accesses and constant values) and output
# nodes
self.graph.setup_internal_buffers()
# set plot path (if plot is set to True)
if plot_graph:
self.graph.plot_graph(name + ".png")
# init sim specific params
self.var_map: Dict[str, float] = dict(
) # mapping between variable names and its (current) value: var_map[var_name] =
# var_value
self.read_success: bool = False # flag indicating if read has been successful from all input nodes (=> ready
# to execute)
self.exec_success: bool = False # flag indicating if the execution has been successful
self.result: float = float(
'nan'
) # execution result of current iteration (see program counter)
self.outputs: Dict[str, BoundedQueue] = dict()
# output delay queue: for simulation of calculation latency, fill it up with bubbles
self.out_delay_queue: BoundedQueue = BoundedQueue(
name="delay_output",
maxsize=self.graph.max_latency + 1,
collection=[None] * self.graph.max_latency)
# setup internal buffer queues
self.internal_buffer: Dict[str, BoundedQueue] = dict()
self.setup_internal_buffers()
# this method takes care of the (falsely) executed kernel in case of not having a field access at [0,0,0]
# present and the implication that there might be only fields out of bound s.t. there is a result produced,
# but there should not be a result yet (see paper example ref# TODO)
self.dist_to_center: Dict = dict()
self.set_up_dist_to_center()
self.center_reached = False
# add performance metric fields
self.max_del_buf_usage = dict()
# for mean
self.buf_usage_sum = dict()
self.buf_usage_num = dict()
self.init_metric = False
self.PC_exec_start = stencilflow.convert_3d_to_1d(
dimensions=self.dimensions, index=self.dimensions) # upper bound
self.PC_exec_end = 0 # lower bound
def iter_comp_tree(self,
node: BaseOperationNodeClass,
index_relative_to_center=True,
replace_negative_index=False,
python_syntax=False,
flatten_index=True,
output_dimensions=None) -> str:
"""
Iterate through the computation tree in order to generate the kernel string (according to some properties
e.g. relative to center or replace negative index.
:param node: current node in the tree
:param index_relative_to_center: indication wheter the zero index should be at the center of the stencil or the
furthest element
:param replace_negative_index: replace the negativ sign '-' by n in order to create variable names that are not
being split up by the python expression parser (Calculator)
:return: computation string of the subgraph
"""
# get predecessor list
pred = list(self.graph.graph.pred[node])
# differentiate cases for each node type
if isinstance(node, BinOp): # binary operation
# extract expression elements
if len(pred) == 1: # lhs == rhs:
lhs, rhs = pred[0], pred[0]
else:
lhs = pred[0] # left hand side
rhs = pred[1] # right hand side
# recursively compute the child string
lhs_str = self.iter_comp_tree(lhs, index_relative_to_center,
replace_negative_index, python_syntax,
flatten_index, output_dimensions)
rhs_str = self.iter_comp_tree(rhs, index_relative_to_center,
replace_negative_index, python_syntax,
flatten_index, output_dimensions)
# return formatted string
return "({} {} {})".format(lhs_str, node.generate_op_sym(), rhs_str)
elif isinstance(node, Call): # function call
# extract expression element
expr = pred[0]
# recursively compute the child string
expr_str = self.iter_comp_tree(expr, index_relative_to_center,
replace_negative_index,
python_syntax)
# return formatted string
return "{}({})".format(node.name, expr_str)
elif isinstance(node, Name) or isinstance(node, Num):
# return formatted string
return str(node.name) # variable name
elif isinstance(node, Subscript):
# compute correct indexing according to the flag
if index_relative_to_center:
dim_index = node.index
else:
dim_index = stencilflow.list_subtract_cwise(
node.index, self.graph.max_index[node.name])
# break down index from 3D (i.e. [X,Y,Z]) to 1D
if flatten_index:
# TODO
if node.name in self.input_paths and self.inputs[
node.name]["input_dims"] is not None:
ind = [
x if x in self.inputs[node.name]["input_dims"] else None
for x in stencilflow.ITERATORS
]
num_dim = stencilflow.num_dims(ind)
#dim_index = dim_index[len(self.dimensions) - num_dim:]
new_ind, i = list(), 0
for entry in ind:
if entry is None:
new_ind.append(None)
else:
new_ind.append(dim_index[i])
i += 1
dim_index = dim_index #list(map(lambda x, y: y if x is not None else None, ind, new_ind))
word_index = stencilflow.convert_3d_to_1d(
dimensions=self.dimensions, index=dim_index)
# replace negative sign if the flag is set
if replace_negative_index and word_index < 0:
return node.name + "[" + "n" + str(abs(word_index)) + "]"
else:
return node.name + "[" + str(word_index) + "]"
else:
try:
dim_index = [
dim_index[stencilflow.ITERATORS.index(i)]
for i in self.inputs[node.name]["input_dims"]
]
except (KeyError, TypeError):
pass # input_dim not defined or is None
if len(dim_index) > output_dimensions:
for i in range(3 - output_dimensions):
if dim_index[i] != 0:
raise ValueError("Removed used index dimension")
dim_index = dim_index[3 - output_dimensions:]
return node.name + str(dim_index)
elif isinstance(
node, Ternary
): # ternary operator of the form true_expr if comp else false_expr
# extract expression elements
compare = [x for x in pred if type(x) == Compare][0] # comparison
lhs = [x for x in pred if type(x) != Compare][0] # left hand side
rhs = [x for x in pred if type(x) != Compare][1] # right hand side
# recursively compute the child string
compare_str = self.iter_comp_tree(compare, index_relative_to_center,
replace_negative_index,
python_syntax, flatten_index,
output_dimensions)
lhs_str = self.iter_comp_tree(lhs, index_relative_to_center,
replace_negative_index, python_syntax,
flatten_index, output_dimensions)
rhs_str = self.iter_comp_tree(rhs, index_relative_to_center,
replace_negative_index, python_syntax,
flatten_index, output_dimensions)
# return formatted string
if python_syntax:
return "(({}) if ({}) else ({}))".format(
lhs_str, compare_str, rhs_str)
else: # C++ ternary operator syntax
return "(({}) ? ({}) : ({}))".format(compare_str, lhs_str,
rhs_str)
elif isinstance(node, Compare): # comparison
# extract expression element
lhs = pred[0]
rhs = pred[1]
# recursively compute the child string
lhs_str = self.iter_comp_tree(lhs, index_relative_to_center,
replace_negative_index, python_syntax,
flatten_index, output_dimensions)
rhs_str = self.iter_comp_tree(rhs, index_relative_to_center,
replace_negative_index, python_syntax,
flatten_index, output_dimensions)
# return formatted string
return "{} {} {}".format(lhs_str, str(node.name), rhs_str)
elif isinstance(node, UnaryOp): # unary operations e.g. negation
# extract expression element
expr = pred[0]
# recursively compute the child string
expr_str = self.iter_comp_tree(
node=expr,
index_relative_to_center=index_relative_to_center,
replace_negative_index=replace_negative_index,
python_syntax=python_syntax,
flatten_index=flatten_index,
output_dimensions=output_dimensions)
# return formatted string
return "({}{})".format(node.generate_op_sym(), expr_str)
else:
raise NotImplementedError(
"iter_comp_tree is not implemented for node type {}".format(
type(node)))
def compute_critical_path(self) -> int:
"""
Computes the max latency critical path through the graph in scalar format.
"""
return stencilflow.convert_3d_to_1d(
index=self.compute_critical_path_dim(), dimensions=self.dimensions)
def compute_delay_buffer(self) -> None:
"""
Computes the delay buffer sizes in the graph by propagating all paths from the input arrays to the successors in
topological order. Delay buffer entries should be of the format: kernel.input_paths:{
"in1": [[a,b,c, pred1], [d,e,f, pred2],
...],
"in2": [ ... ],
...
}
where inX are input arrays to the stencil chain and predY are the kernel predecessors/inputs
"""
# get topological order for top-down walk through of the graph
try:
order = list(nx.topological_sort(self.graph))
except nx.exception.NetworkXUnfeasible:
cycle = next(nx.algorithms.cycles.simple_cycles(self.graph))
raise ValueError("Cycle detected: {}".format(
[c.name for c in cycle]))
# go through all nodes
for node in order:
# process delay buffer (no additional delay buffer will appear because of the topological order)
for inp in node.input_paths:
# compute maximum delay size per input
max_delay = max(node.input_paths[inp])
max_delay[
2] += 1 # add an extra delay cycle for the processing in the kernel node
# loop over all inputs and set their size relative to the max size to have data ready at the exact
# same time
for entry in node.input_paths[inp]:
name = entry[-1]
max_size = stencilflow.convert_3d_to_1d(
dimensions=self.dimensions,
index=stencilflow.list_subtract_cwise(
max_delay[:-1], entry[:-1]))
node.delay_buffer[name] = BoundedQueue(name=name,
maxsize=max_size)
node.delay_buffer[name].import_data(
[None] * node.delay_buffer[name].maxsize)
# set input node delay buffers to 1
if isinstance(node, Input):
node.delay_buffer = BoundedQueue(name=node.name,
maxsize=1,
collection=[None])
# propagate the path lengths (from input arrays over all ways) to the successors
for succ in self.graph.successors(node):
# add input node to all as direct input (=0 delay buffer)
if isinstance(node, Input):
# add emtpy list dictionary entry for enabling list append()
if node.name not in succ.input_paths:
succ.input_paths[node.name] = []
successor = [0] * len(self.dimensions)
successor = successor + [node.name]
succ.input_paths[node.name].append(successor)
# add kernel node to all, but calculate the length first (predecessor + delay + internal, ..)
elif isinstance(node, Kernel): # add KERNEL
# add latency, internal_buffer, delay_buffer
internal_buffer = [0] * 3
for item in node.graph.accesses:
internal_buffer = max(
node.graph.accesses[item]
) if KernelChainGraph.greater(
max(node.graph.accesses[item]),
internal_buffer) else internal_buffer
# latency
latency = self.kernel_nodes[node.name].graph.max_latency
# compute delay buffer and create entry
for entry in node.input_paths:
# the first entry has to initialize the structure
if entry not in succ.input_paths:
succ.input_paths[entry] = []
# compute the actual delay buffer
delay_buffer = max(node.input_paths[entry][:])
# merge them together
total = [
i + d if i is not None else d
for i, d in zip(internal_buffer, delay_buffer)
]
# add the latency too
total[-1] += latency
total.append(node.name)
# add entry to paths
succ.input_paths[entry].append(total)
else: # NodeType.OUTPUT: do nothing
continue