From 697d8cdfd03327a9ab4611e5f3dee7811cf73a88 Mon Sep 17 00:00:00 2001
From: Martin Bauer <martin.bauer@fau.de>
Date: Tue, 26 Sep 2017 14:31:16 +0200
Subject: [PATCH] waLBerla code gen: better handling of ghost layers
- support for kernels that run over walberla ghost layers
---
astnodes.py | 3 ++-
cpu/kernelcreation.py | 2 +-
gpucuda/kernelcreation.py | 4 ++--
transformations.py | 2 +-
types.py | 34 +++++-----------------------------
5 files changed, 11 insertions(+), 34 deletions(-)
diff --git a/astnodes.py b/astnodes.py
index f5489b62f..25d4ba4a0 100644
--- a/astnodes.py
+++ b/astnodes.py
@@ -156,13 +156,14 @@ class KernelFunction(Node):
def __repr__(self):
return '<{0} {1}>'.format(self.dtype, self.name)
- def __init__(self, body, functionName="kernel"):
+ def __init__(self, body, ghostLayers=None, functionName="kernel"):
super(KernelFunction, self).__init__()
self._body = body
body.parent = self
self._parameters = None
self.functionName = functionName
self._body.parent = self
+ self.ghostLayers = ghostLayers
# these variables are assumed to be global, so no automatic parameter is generated for them
self.globalVariables = set()
diff --git a/cpu/kernelcreation.py b/cpu/kernelcreation.py
index 18d382d72..718dd1cb9 100644
--- a/cpu/kernelcreation.py
+++ b/cpu/kernelcreation.py
@@ -120,7 +120,7 @@ def createIndexedKernel(listOfEquations, indexFields, functionName="kernel", typ
loopBody.append(assignment)
functionBody = Block([loopNode])
- ast = KernelFunction(functionBody, functionName)
+ ast = KernelFunction(functionBody, functionName=functionName)
fixedCoordinateMapping = {f.name: coordinateTypedSymbols for f in nonIndexFields}
resolveFieldAccesses(ast, set(['indexField']), fieldToFixedCoordinates=fixedCoordinateMapping)
diff --git a/gpucuda/kernelcreation.py b/gpucuda/kernelcreation.py
index fd0c17564..fa0673488 100644
--- a/gpucuda/kernelcreation.py
+++ b/gpucuda/kernelcreation.py
@@ -35,7 +35,7 @@ def createCUDAKernel(listOfEquations, functionName="kernel", typeForSymbol=None,
block = Block(assignments)
block = indexing.guard(block, commonShape)
- ast = KernelFunction(block, functionName)
+ ast = KernelFunction(block, functionName=functionName, ghostLayers=ghostLayers)
ast.globalVariables.update(indexing.indexVariables)
coordMapping = indexing.coordinates
@@ -95,7 +95,7 @@ def createdIndexedCUDAKernel(listOfEquations, indexFields, functionName="kernel"
functionBody = Block(coordinateSymbolAssignments + assignments)
functionBody = indexing.guard(functionBody, getCommonShape(indexFields))
- ast = KernelFunction(functionBody, functionName)
+ ast = KernelFunction(functionBody, functionName=functionName)
ast.globalVariables.update(indexing.indexVariables)
coordMapping = indexing.coordinates
diff --git a/transformations.py b/transformations.py
index 917a705b5..0975e96aa 100644
--- a/transformations.py
+++ b/transformations.py
@@ -101,7 +101,7 @@ def makeLoopOverDomain(body, functionName, iterationSlice=None, ghostLayers=None
assignment = ast.SympyAssignment(ast.LoopOverCoordinate.getLoopCounterSymbol(loopCoordinate),
sp.sympify(sliceComponent))
currentBody.insertFront(assignment)
- return ast.KernelFunction(currentBody, functionName)
+ return ast.KernelFunction(currentBody, ghostLayers=ghostLayers, functionName=functionName)
def createIntermediateBasePointer(fieldAccess, coordinates, previousPtr):
diff --git a/types.py b/types.py
index 2fb1fcf93..86ad051b3 100644
--- a/types.py
+++ b/types.py
@@ -1,8 +1,8 @@
import ctypes
import sympy as sp
import numpy as np
-# import llvmlite.ir as ir
from sympy.core.cache import cacheit
+from pystencils.cache import memorycache
class TypedSymbol(sp.Symbol):
@@ -115,7 +115,6 @@ def toCtypes(dataType):
else:
return toCtypes.map[dataType.numpyDtype]
-
toCtypes.map = {
np.dtype(np.int8): ctypes.c_int8,
np.dtype(np.int16): ctypes.c_int16,
@@ -132,33 +131,10 @@ toCtypes.map = {
}
-#def to_llvmlite_type(data_type):
-# """
-# Transforms a given type into ctypes
-# :param data_type: Subclass of Type
-# :return: llvmlite type object
-# """
-# if isinstance(data_type, PointerType):
-# return to_llvmlite_type.map[data_type.baseType].as_pointer()
-# else:
-# return to_llvmlite_type.map[data_type.numpyDType]
-#
-#to_llvmlite_type.map = {
-# np.dtype(np.int8): ir.IntType(8),
-# np.dtype(np.int16): ir.IntType(16),
-# np.dtype(np.int32): ir.IntType(32),
-# np.dtype(np.int64): ir.IntType(64),
-#
-# # TODO llvmlite doesn't seem to differentiate between Int types
-# np.dtype(np.uint8): ir.IntType(8),
-# np.dtype(np.uint16): ir.IntType(16),
-# np.dtype(np.uint32): ir.IntType(32),
-# np.dtype(np.uint64): ir.IntType(64),
-#
-# np.dtype(np.float32): ir.FloatType(),
-# np.dtype(np.float64): ir.DoubleType(),
-# # TODO const, restrict, void
-#}
+def getTypeOfExpression(expr):
+ if isinstance(expr, TypedSymbol):
+ return expr.dtype
+
class Type(sp.Basic):
--
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