Pull in basics from symlbm_playground

It's time to extract the generator-part of my GPU LBM playground and turn it
into a nice reusable library. The goal is to produce a framework that can be
used to generate collision and streaming programs from symbolic descriptions.

i.e. it should be possible to select a LB model, the desired boundary
conditions as well as a data structure / streaming model and use this
information to automatically generate matching OpenCL / CUDA / C++
programs.

2 files changed, 24 insertions, 0 deletions

diff --git a/boltzgen/utility/ndindex.py b/boltzgen/utility/ndindex.py
new file mode 100644
index 0000000..0c5ed9b
--- /dev/null
+++ b/boltzgen/utility/ndindex.py
@@ -0,0 +1,14 @@
+import numpy
+from numpy.lib.stride_tricks import as_strided
+import numpy.core.numeric as _nx
+
+class ndindex(numpy.ndindex):
+    pass
+
+    def __init__(self, *shape, order):
+        if len(shape) == 1 and isinstance(shape[0], tuple):
+            shape = shape[0]
+        x = as_strided(_nx.zeros(1), shape=shape,
+                       strides=_nx.zeros_like(shape))
+        self._it = _nx.nditer(x, flags=['multi_index', 'zerosize_ok'],
+                              order=order)
diff --git a/boltzgen/utility/optimizations.py b/boltzgen/utility/optimizations.py
new file mode 100644
index 0000000..93dad09
--- /dev/null
+++ b/boltzgen/utility/optimizations.py
@@ -0,0 +1,10 @@
+from sympy import *
+
+from sympy.codegen.rewriting import ReplaceOptim
+
+expand_square = ReplaceOptim(
+    lambda e: e.is_Pow and e.exp.is_integer and e.exp == 2,
+    lambda p: UnevaluatedExpr(Mul(p.base, p.base, evaluate = False))
+)
+
+custom = [ (expand_square, expand_square) ] + cse_main.basic_optimizations