diff --git a/examples/matrix-ops.py b/examples/matrix-ops.py
index 1334bc5da7981dd4b6b70c73990e69de71137ff1..592cfa1aa3941ef7ef3a76d37500e5221ac33769 100644
--- a/examples/matrix-ops.py
+++ b/examples/matrix-ops.py
@@ -261,6 +261,68 @@ def dg_matrix_mul(ctx_factory=cl.create_some_context):
+def image_matrix_mul_ilp(ctx_factory=cl.create_some_context):
+ dtype = np.float32
+ ctx = ctx_factory()
+ order = "C"
+ queue = cl.CommandQueue(ctx,
+ properties=cl.command_queue_properties.PROFILING_ENABLE)
+
+ n = 16*100
+ from pymbolic import var
+ a, b, c, i, j, k, n_sym = [var(s) for s in "abcijkn"]
+
+ knl = lp.LoopKernel(ctx.devices[0],
+ "{[i,j,k]: 0<=i,j,k<%d}" % n,
+ [
+ (c[i, j], a[i, k]*b[k, j])
+ ],
+ [
+ lp.ImageArg("a", dtype, 2),
+ lp.ImageArg("b", dtype, 2),
+ #lp.ArrayArg("a", dtype, shape=(n, n), order=order),
+ #lp.ArrayArg("b", dtype, shape=(n, n), order=order),
+ lp.ArrayArg("c", dtype, shape=(n, n), order=order),
+ ],
+ name="matmul")
+
+ ilp = 4
+ knl = lp.split_dimension(knl, "i", 16, outer_tag="g.0", inner_tag="l.1")
+ knl = lp.split_dimension(knl, "j", ilp*16, outer_tag="g.1")
+ knl = lp.split_dimension(knl, "j_inner", ilp, outer_tag="unr", inner_tag="l.0")
+ knl = lp.split_dimension(knl, "k", 32)
+ # conflict-free
+ knl = lp.add_prefetch(knl, 'a', ["i_inner", "k_inner"])
+ knl = lp.add_prefetch(knl, 'b', ["j_inner_outer", "j_inner_inner", "k_inner"])
+ assert knl.get_invalid_reason() is None
+
+ kernel_gen = (lp.insert_register_prefetches(knl)
+ for knl in lp.generate_loop_schedules(knl))
+
+ a = make_well_conditioned_dev_matrix(queue, n, dtype=dtype, order=order)
+ b = make_well_conditioned_dev_matrix(queue, n, dtype=dtype, order=order)
+ c = cl_array.empty_like(a)
+ refsol = np.dot(a.get(), b.get())
+ a_img = cl.image_from_array(ctx, a.get(), 1)
+ b_img = cl.image_from_array(ctx, b.get(), 1)
+
+ def launcher(kernel, gsize, lsize, check):
+ evt = kernel(queue, gsize(), lsize(), a_img, b_img, c.data,
+ g_times_l=True)
+
+ if check:
+ check_error(refsol, c.get())
+
+ return evt
+
+ lp.drive_timing_run(kernel_gen, queue, launcher, 2*n**3,
+ options=FAST_OPTIONS + ["-cl-nv-verbose"],
+ force_rebuild=True)
+
+
+
+
+
def fancy_matrix_mul(ctx_factory=cl.create_some_context):
dtype = np.float32
ctx = ctx_factory()
@@ -403,4 +465,4 @@ if __name__ == "__main__":
if len(sys.argv) > 1:
exec(sys.argv[1])
else:
- image_matrix_mul()
+ plain_matrix_mul()
diff --git a/loopy/__init__.py b/loopy/__init__.py
index d863c43ca7b576628b53879d9fc0806047f4bfd3..aadb8c8b3df4c6dfa9ab7f9c99402ae3dffeadab 100644
--- a/loopy/__init__.py
+++ b/loopy/__init__.py
@@ -22,14 +22,12 @@ register_mpz_with_pymbolic()
-# TODO: ILP Unroll
-
+# TODO: Fetch dim merging
# TODO: Try, fix reg. prefetch (DG example) / CSEs
# TODO: Custom reductions per red. axis
# TODO: Functions
# TODO: Common subexpressions
# TODO: Parse ops from string
-# TODO: Why u take so long?
# TODO: Condition hoisting
# TODO: Don't emit spurious barriers (no for scheduled before)
@@ -44,8 +42,10 @@ register_mpz_with_pymbolic()
# TODO: User control over schedule
# TODO: Separate all-bulk from non-bulk kernels.
-# TODO Tim: implement efficient div_ceil?
-# TODO Tim: why are corner cases inefficient?
+# TODO: ILP Unroll
+
+# TODO: implement efficient div_ceil?
+# TODO: why are corner cases inefficient?
@@ -100,13 +100,13 @@ class TAG_ILP_UNROLL(IndexTag):
class BaseUnrollTag(IndexTag):
pass
-class TAG_UNROLL_ONE_VAR(BaseUnrollTag):
+class TAG_UNROLL_STATIC(BaseUnrollTag):
def __repr__(self):
- return "TAG_UNROLL_1V"
+ return "TAG_UNROLL_STATIC"
-class TAG_UNROLL_MULTI_VAR(BaseUnrollTag):
+class TAG_UNROLL_INCR(BaseUnrollTag):
def __repr__(self):
- return "TAG_UNROLL_MV"
+ return "TAG_UNROLL_INCR"
def parse_tag(tag):
if tag is None:
@@ -118,10 +118,10 @@ def parse_tag(tag):
if not isinstance(tag, str):
raise ValueError("cannot parse tag: %s" % tag)
- if tag == "unr1":
- return TAG_UNROLL_ONE_VAR()
- elif tag == "unrm":
- return TAG_UNROLL_MULTI_VAR()
+ if tag in ["unrs", "unr"]:
+ return TAG_UNROLL_STATIC()
+ elif tag == "unri":
+ return TAG_UNROLL_INCR()
elif tag == "ilp":
return TAG_ILP_UNROLL
elif tag.startswith("g."):
@@ -1254,6 +1254,7 @@ def make_fetch_loop_nest(flnd, pf_iname_idx, pf_dim_exprs, pf_idx_subst_map,
pf = flnd.prefetch
ccm = flnd.c_code_mapper
no_pf_ccm = flnd.no_prefetch_c_code_mapper
+ kernel = flnd.kernel
from pymbolic import var
from cgen import Assign, For, If
@@ -1346,8 +1347,8 @@ def make_fetch_loop_nest(flnd, pf_iname_idx, pf_dim_exprs, pf_idx_subst_map,
lb_cns, ub_cns = flnd.kernel.get_projected_bounds_constraints(pf_iname)
loop_slab = (isl.Set.universe(flnd.kernel.space)
- .add_constraint(lb_cns)
- .add_constraint(ub_cns))
+ .add_constraint(cast_constraint_to_space(lb_cns, kernel.space))
+ .add_constraint(cast_constraint_to_space(ub_cns, kernel.space)))
new_impl_domain = implemented_domain.intersect(loop_slab)
pf_idx_subst_map = pf_idx_subst_map.copy()
@@ -1563,10 +1564,7 @@ def generate_loop_dim_code(cgs, kernel, sched_index,
cfm = CommutativeConstantFoldingMapper()
length = int(cfm(flatten(upper_bound-lower_bound)))
- if isinstance(tag, TAG_UNROLL_ONE_VAR):
- result = [POD(np.int32, iname), Line()]
- else:
- result = []
+ result = [POD(np.int32, iname), Line()]
for i in xrange(length):
slab = (isl.Set.universe(kernel.space)
@@ -1578,15 +1576,12 @@ def generate_loop_dim_code(cgs, kernel, sched_index,
inner = build_loop_nest(cgs, kernel, sched_index+1,
new_impl_domain)
- if isinstance(tag, TAG_UNROLL_ONE_VAR):
+ if isinstance(tag, TAG_UNROLL_STATIC):
result.extend([
Assign(iname, ccm(lower_bound+i)),
Line(), inner])
- else:
- result.append(gen_code_block([
- Initializer(Const(POD(np.int32, iname)),
- ccm(lower_bound+i)),
- Line(), inner]))
+ elif isinstance(tag, TAG_UNROLL_INCR):
+ result.append(S("++%s" % iname))
return gen_code_block(result)
@@ -1695,7 +1690,7 @@ def generate_loop_dim_code(cgs, kernel, sched_index,
if cmt is not None:
result.append(Comment(cmt))
result.append(
- wrap_in_for_from_constraints(ccm, iname, slab,inner))
+ wrap_in_for_from_constraints(ccm, iname, slab, inner))
else:
# parallel loop
nums_of_conditionals.append(inner.num_conditionals)