def test_affine_map_inames():
knl = lp.make_kernel("{[e, i,j,n]: 0<=e<E and 0<=i,j,n<N}",
"rhsQ[e, n+i, j] = rhsQ[e, n+i, j] - D[i, n]*x[i,j]")
knl = lp.affine_map_inames(knl, "i", "i0", "i0 = n+i")
print(knl)
def test_affine_map_inames():
knl = lp.make_kernel(
"{[e, i,j,n]: 0<=e<E and 0<=i,j,n<N}",
"rhsQ[e, n+i, j] = rhsQ[e, n+i, j] - D[i, n]*x[i,j]")
knl = lp.affine_map_inames(knl,
"i", "i0",
"i0 = n+i")
print(knl)
def test_finite_difference_expr_subst(ctx_factory):
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
grid = np.linspace(0, 2*np.pi, 2048, endpoint=False)
h = grid[1] - grid[0]
u = cl.clmath.sin(cl.array.to_device(queue, grid))
fin_diff_knl = lp.make_kernel(
"{[i]: 1<=i<=n}",
"out[i] = -(f[i+1] - f[i-1])/h",
[lp.GlobalArg("out", shape="n+2"), "..."])
flux_knl = lp.make_kernel(
"{[j]: 1<=j<=n}",
"f[j] = u[j]**2/2",
[
lp.GlobalArg("f", shape="n+2"),
lp.GlobalArg("u", shape="n+2"),
])
fused_knl = lp.fuse_kernels([fin_diff_knl, flux_knl],
data_flow=[
("f", 1, 0)
])
fused_knl = lp.set_options(fused_knl, write_cl=True)
evt, _ = fused_knl(queue, u=u, h=np.float32(1e-1))
fused_knl = lp.assignment_to_subst(fused_knl, "f")
fused_knl = lp.set_options(fused_knl, write_cl=True)
# This is the real test here: The automatically generated
# shape expressions are '2+n' and the ones above are 'n+2'.
# Is loopy smart enough to understand that these are equal?
evt, _ = fused_knl(queue, u=u, h=np.float32(1e-1))
fused0_knl = lp.affine_map_inames(fused_knl, "i", "inew", "inew+1=i")
gpu_knl = lp.split_iname(
fused0_knl, "inew", 128, outer_tag="g.0", inner_tag="l.0")
precomp_knl = lp.precompute(
gpu_knl, "f_subst", "inew_inner", fetch_bounding_box=True)
precomp_knl = lp.tag_inames(precomp_knl, {"j_0_outer": "unr"})
precomp_knl = lp.set_options(precomp_knl, return_dict=True)
evt, _ = precomp_knl(queue, u=u, h=h)
def test_finite_difference_expr_subst(ctx_factory):
ctx = ctx_factory()
queue = cl.CommandQueue(ctx)
grid = np.linspace(0, 2 * np.pi, 2048, endpoint=False)
h = grid[1] - grid[0]
u = cl.clmath.sin(cl.array.to_device(queue, grid))
fin_diff_knl = lp.make_kernel("{[i]: 1<=i<=n}",
"out[i] = -(f[i+1] - f[i-1])/h",
[lp.GlobalArg("out", shape="n+2"), "..."])
flux_knl = lp.make_kernel("{[j]: 1<=j<=n}", "f[j] = u[j]**2/2", [
lp.GlobalArg("f", shape="n+2"),
lp.GlobalArg("u", shape="n+2"),
])
fused_knl = lp.fuse_kernels([fin_diff_knl, flux_knl],
data_flow=[("f", 1, 0)])
fused_knl = lp.set_options(fused_knl, write_cl=True)
evt, _ = fused_knl(queue, u=u, h=np.float32(1e-1))
fused_knl = lp.assignment_to_subst(fused_knl, "f")
fused_knl = lp.set_options(fused_knl, write_cl=True)
# This is the real test here: The automatically generated
# shape expressions are '2+n' and the ones above are 'n+2'.
# Is loopy smart enough to understand that these are equal?
evt, _ = fused_knl(queue, u=u, h=np.float32(1e-1))
fused0_knl = lp.affine_map_inames(fused_knl, "i", "inew", "inew+1=i")
gpu_knl = lp.split_iname(fused0_knl,
"inew",
128,
outer_tag="g.0",
inner_tag="l.0")
precomp_knl = lp.precompute(gpu_knl,
"f_subst",
"inew_inner",
fetch_bounding_box=True)
precomp_knl = lp.tag_inames(precomp_knl, {"j_0_outer": "unr"})
precomp_knl = lp.set_options(precomp_knl, return_dict=True)
evt, _ = precomp_knl(queue, u=u, h=h)
