diff --git a/test/test_advect.py b/test/test_advect.py
deleted file mode 100644
index 7932141d05c986268e9f0951198cd607f005aed0..0000000000000000000000000000000000000000
--- a/test/test_advect.py
+++ /dev/null
@@ -1,116 +0,0 @@
-
-def test_advect(ctx_factory):
-
- dtype = np.float32
- ctx = ctx_factory()
-
- order = "C"
- queue = cl.CommandQueue(ctx,
- properties=cl.command_queue_properties.PROFILING_ENABLE)
-
- N = 8
-
- from pymbolic import var
- K_sym = var("K")
-
- field_shape = (K_sym, N, N, N)
-
- # 1. direction-by-direction similarity transform on u
- # 2. invert diagonal
- # 3. transform back (direction-by-direction)
-
- # K - run-time symbolic
-
- # A. updated for CSE: notation.
- # B. fixed temp indexing and C ordering
- # load: 3+9 fields + 1/N D entry
- # store: 3 fields
- # perform: N*2*6 + 3*5 + 3*5 flops
- # ratio: (12*N+30)/15 flops per 4 bytes on bus
- # ~ 8.4 FLOPS per 4 bytes at N=8
- # ~ 300 GFLOPS max on a 150GB/s device at N=8 if done perfectly
- knl = lp.make_kernel(ctx.devices[0],
- "[K] -> {[i,j,k,m,e]: 0<=i,j,k,m<%d AND 0<=e<K}" %N
- [
- # differentiate u
- "CSE: ur[i,j,k] = sum_float32(@m, D[i,m]*u[e,m,j,k])",
- "CSE: us[i,j,k] = sum_float32(@m, D[j,m]*u[e,i,m,k])",
- "CSE: ut[i,j,k] = sum_float32(@m, D[k,m]*u[e,i,j,m])",
-
- # differentiate v
- "CSE: vr[i,j,k] = sum_float32(@m, D[i,m]*v[e,m,j,k])",
- "CSE: vs[i,j,k] = sum_float32(@m, D[j,m]*v[e,i,m,k])",
- "CSE: vt[i,j,k] = sum_float32(@m, D[k,m]*v[e,i,j,m])",
-
- # differentiate w
- "CSE: wr[i,j,k] = sum_float32(@m, D[i,m]*w[e,m,j,k])",
- "CSE: ws[i,j,k] = sum_float32(@m, D[j,m]*w[e,i,m,k])",
- "CSE: wt[i,j,k] = sum_float32(@m, D[k,m]*w[e,i,j,m])",
-
- # find velocity in (r,s,t) coordinates
- # CSE?
- "Vr[i,j,k] = G[0,e,i,j,k]*u[e,i,j,k] + G[1,e,i,j,k]*v[e,i,j,k] + G[2,e,i,j,k]*w[e,i,j,k]",
- "Vs[i,j,k] = G[3,e,i,j,k]*u[e,i,j,k] + G[4,e,i,j,k]*v[e,i,j,k] + G[5,e,i,j,k]*w[e,i,j,k]",
- "Vt[i,j,k] = G[6,e,i,j,k]*u[e,i,j,k] + G[7,e,i,j,k]*v[e,i,j,k] + G[8,e,i,j,k]*w[e,i,j,k]",
-
- # form nonlinear term on integration nodes
- "Nu[e,i,j,k] = Vr[i,j,k]*ur[i,j,k]+Vs[i,j,k]*us[i,j,k]+Vt[i,j,k]*ut[i,j,k]",
- "Nv[e,i,j,k] = Vr[i,j,k]*vr[i,j,k]+Vs[i,j,k]*vs[i,j,k]+Vt[i,j,k]*vt[i,j,k]",
- "Nw[e,i,j,k] = Vr[i,j,k]*wr[i,j,k]+Vs[i,j,k]*ws[i,j,k]+Vt[i,j,k]*wt[i,j,k]",
- ],
- [
- lp.ArrayArg("u", dtype, shape=field_shape, order=order),
- lp.ArrayArg("v", dtype, shape=field_shape, order=order),
- lp.ArrayArg("w", dtype, shape=field_shape, order=order),
- lp.ArrayArg("Nu", dtype, shape=field_shape, order=order),
- lp.ArrayArg("Nv", dtype, shape=field_shape, order=order),
- lp.ArrayArg("Nw", dtype, shape=field_shape, order=order),
- lp.ArrayArg("G", dtype, shape=(6,)+field_shape, order=order),
- lp.ArrayArg("D", dtype, shape=(N, N), order=order),
- lp.ScalarArg("K", np.int32, approximately=1000),
- ],
- name="sem_advect", assumptions="K>=1")
-
- print knl
- 1/0
-
- knl = lp.split_dimension(knl, "e", 16, outer_tag="g.0")#, slabs=(0, 1))
-
- knl = lp.tag_dimensions(knl, dict(i="l.0", j="l.1"))
-
- print knl
- #1/0
-
- kernel_gen = lp.generate_loop_schedules(knl)
- kernel_gen = lp.check_kernels(kernel_gen, dict(K=1000), kill_level_min=5)
-
- 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())
-
- def launcher(kernel, gsize, lsize, check):
- evt = kernel(queue, gsize(), lsize(), a.data, b.data, 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)
-
-
-
-
-if __name__ == "__main__":
- # make sure that import failures get reported, instead of skipping the
- # tests.
- import pyopencl as cl
-
- import sys
- if len(sys.argv) > 1:
- exec(sys.argv[1])
- else:
- from py.test.cmdline import main
- main([__file__])
diff --git a/test/test_loopy.py b/test/test_loopy.py
index 31bf59d4bd0ed70f84b98efd729e54ab9d3c78d1..71e93909b01d28f3d634b27babe1e8efbd3abf04 100644
--- a/test/test_loopy.py
+++ b/test/test_loopy.py
@@ -128,7 +128,7 @@ def test_stencil(ctx_factory):
" + cse(a[i,j-1])"
" + cse(a[i,j+1])"
" + cse(a[i-1,j])"
- " + cse(a[i+1,j])" # watch out: i!
+ " + cse(a[i+1,j])"
],
[
lp.ArrayArg("a", np.float32, shape=(32,32,))
diff --git a/test/test_sem.py b/test/test_sem.py
index 19f854ce9b25f6fb7a2df0b3a2c849ddc1c225c8..22dfbf233bcb80c82528ca659116651e4cbfda8f 100644
--- a/test/test_sem.py
+++ b/test/test_sem.py
@@ -1,9 +1,7 @@
from __future__ import division
import numpy as np
-import numpy.linalg as la
import pyopencl as cl
-import pyopencl.array as cl_array
import loopy as lp
from pyopencl.tools import pytest_generate_tests_for_pyopencl \
@@ -12,7 +10,7 @@ from pyopencl.tools import pytest_generate_tests_for_pyopencl \
-def test_sem_3d(ctx_factory):
+def test_laplacian(ctx_factory):
dtype = np.float32
ctx = ctx_factory()
order = "C"
@@ -33,16 +31,21 @@ def test_sem_3d(ctx_factory):
# K - run-time symbolic
knl = lp.make_kernel(ctx.devices[0],
- "[K] -> {[i,j,k,e,m,o,gi]: 0<=i,j,k,m,o<%d and 0<=e<K and 0<=gi<6}" % n,
+ "[K] -> {[i,j,k,e,m,o1,o2,o3,gi]: 0<=i,j,k,m,o1,o2,o3<%d and 0<=e<K and 0<=gi<6}" % n,
[
- "CSE: ur(i,j,k) = sum_float32(@o, D[i,o]*u[e,o,j,k])",
- "CSE: us(i,j,k) = sum_float32(@o, D[j,o]*u[e,i,o,k])",
- "CSE: ut(i,j,k) = sum_float32(@o, D[k,o]*u[e,i,j,o])",
+ "CSE: ur(i,j,k) = sum_float32(o1, D[i,o1]*cse(u[e,o1,j,k], urf))",
+ "CSE: us(i,j,k) = sum_float32(o2, D[j,o2]*cse(u[e,i,o2,k], usf))",
+ "CSE: ut(i,j,k) = sum_float32(o3, D[k,o3]*cse(u[e,i,j,o3], utf))",
+
+ # define function
+ "CSE: Gu(i,j,k) = G[0,e,i,j,k]*ur(i,j,k) + G[1,e,i,j,k]*us(i,j,k) + G[2,e,i,j,k]*ut(i,j,k)",
+ "CSE: Gv(i,j,k) = G[1,e,i,j,k]*ur(i,j,k) + G[3,e,i,j,k]*us(i,j,k) + G[4,e,i,j,k]*ut(i,j,k)",
+ "CSE: Gw(i,j,k) = G[2,e,i,j,k]*ur(i,j,k) + G[4,e,i,j,k]*us(i,j,k) + G[5,e,i,j,k]*ut(i,j,k)",
"lap[e,i,j,k] = "
- " sum_float32(m, D[m,i]*(G[0,e,m,j,k]*ur(m,j,k) + G[1,e,m,j,k]*us(m,j,k) + G[2,e,m,j,k]*ut(m,j,k)))"
- "+ sum_float32(m, D[m,j]*(G[1,e,i,m,k]*ur(i,m,k) + G[3,e,i,m,k]*us(i,m,k) + G[4,e,i,m,k]*ut(i,m,k)))"
- "+ sum_float32(m, D[m,k]*(G[2,e,i,j,m]*ur(i,j,m) + G[4,e,i,j,m]*us(i,j,m) + G[5,e,i,j,m]*ut(i,j,m)))"
+ " sum_float32(m, D[m,i]*Gu(m,j,k))"
+ "+ sum_float32(m, D[m,j]*Gv(i,m,k))"
+ "+ sum_float32(m, D[m,k]*Gw(i,j,m))"
],
[
lp.ArrayArg("u", dtype, shape=field_shape, order=order),
@@ -53,15 +56,28 @@ def test_sem_3d(ctx_factory):
],
name="semlap", assumptions="K>=1")
+ #print lp.preprocess_kernel(knl, cse_ok=True)
+ #1/0
+ #
+ #print knl
+ #1/0
+ knl = lp.realize_cse(knl, "urf", np.float32, ["o1"])
+ knl = lp.realize_cse(knl, "usf", np.float32, ["o2"])
+ knl = lp.realize_cse(knl, "utf", np.float32, ["o3"])
+
+ knl = lp.realize_cse(knl, "Gu", np.float32, ["m", "j", "k"])
+ knl = lp.realize_cse(knl, "Gv", np.float32, ["i", "m", "k"])
+ knl = lp.realize_cse(knl, "Gw", np.float32, ["i", "j", "m"])
knl = lp.realize_cse(knl, "ur", np.float32, ["k", "j", "m"])
knl = lp.realize_cse(knl, "us", np.float32, ["i", "m", "k"])
knl = lp.realize_cse(knl, "ut", np.float32, ["i", "j", "m"])
if 0:
- seq_knl = lp.add_prefetch(knl, "G", ["gi", "m", "j", "k"], "G[gi,e,m,j,k]")
- seq_knl = lp.add_prefetch(seq_knl, "D", ["m", "j"])
- seq_knl = lp.add_prefetch(seq_knl, "u", ["i", "j", "k"], "u[*,i,j,k]")
+ pass
+ #seq_knl = lp.add_prefetch(knl, "G", ["gi", "m", "j", "k"], "G[gi,e,m,j,k]")
+ #seq_knl = lp.add_prefetch(seq_knl, "D", ["m", "j"])
+ #seq_knl = lp.add_prefetch(seq_knl, "u", ["i", "j", "k"], "u[*,i,j,k]")
else:
seq_knl = knl
@@ -69,7 +85,8 @@ def test_sem_3d(ctx_factory):
knl = lp.add_prefetch(knl, "G", ["gi", "m", "j", "k"], "G[gi,e,m,j,k]")
knl = lp.add_prefetch(knl, "D", ["m", "j"])
- knl = lp.add_prefetch(knl, "u", ["i", "j", "k"], "u[*,i,j,k]")
+ #knl = lp.add_prefetch(knl, "u", ["i", "j", "k"], "u[*,i,j,k]")
+
#knl = lp.split_dimension(knl, "e_inner", 4, inner_tag="ilp")
#print seq_knl
@@ -78,7 +95,8 @@ def test_sem_3d(ctx_factory):
knl = lp.tag_dimensions(knl, dict(i="l.0", j="l.1"))
- kernel_gen = lp.generate_loop_schedules(knl)
+ kernel_gen = lp.generate_loop_schedules(knl,
+ loop_priority=["m_fetch_G", "i_fetch_u"])
kernel_gen = lp.check_kernels(kernel_gen, dict(K=1000))
K = 1000
@@ -89,6 +107,99 @@ def test_sem_3d(ctx_factory):
+
+def test_advect(ctx_factory):
+ dtype = np.float32
+ ctx = ctx_factory()
+
+ order = "C"
+
+ N = 8
+
+ from pymbolic import var
+ K_sym = var("K")
+
+ field_shape = (K_sym, N, N, N)
+
+ # 1. direction-by-direction similarity transform on u
+ # 2. invert diagonal
+ # 3. transform back (direction-by-direction)
+
+ # K - run-time symbolic
+
+ # A. updated for CSE: notation.
+ # B. fixed temp indexing and C ordering
+ # load: 3+9 fields + 1/N D entry
+ # store: 3 fields
+ # perform: N*2*6 + 3*5 + 3*5 flops
+ # ratio: (12*N+30)/15 flops per 4 bytes on bus
+ # ~ 8.4 FLOPS per 4 bytes at N=8
+ # ~ 300 GFLOPS max on a 150GB/s device at N=8 if done perfectly
+ knl = lp.make_kernel(ctx.devices[0],
+ "[K] -> {[i,j,k,m,e]: 0<=i,j,k,m<%d AND 0<=e<K}" % N,
+ [
+ # differentiate u
+ "CSE: ur(i,j,k) = sum_float32(@m, D[i,m]*u[e,m,j,k])",
+ "CSE: us(i,j,k) = sum_float32(@m, D[j,m]*u[e,i,m,k])",
+ "CSE: ut(i,j,k) = sum_float32(@m, D[k,m]*u[e,i,j,m])",
+
+ # differentiate v
+ "CSE: vr(i,j,k) = sum_float32(@m, D[i,m]*v[e,m,j,k])",
+ "CSE: vs(i,j,k) = sum_float32(@m, D[j,m]*v[e,i,m,k])",
+ "CSE: vt(i,j,k) = sum_float32(@m, D[k,m]*v[e,i,j,m])",
+
+ # differentiate w
+ "CSE: wr(i,j,k) = sum_float32(@m, D[i,m]*w[e,m,j,k])",
+ "CSE: ws(i,j,k) = sum_float32(@m, D[j,m]*w[e,i,m,k])",
+ "CSE: wt(i,j,k) = sum_float32(@m, D[k,m]*w[e,i,j,m])",
+
+ # find velocity in (r,s,t) coordinates
+ # CSE?
+ "CSE: Vr(i,j,k) = G[0,e,i,j,k]*u[e,i,j,k] + G[1,e,i,j,k]*v[e,i,j,k] + G[2,e,i,j,k]*w[e,i,j,k]",
+ "CSE: Vs(i,j,k) = G[3,e,i,j,k]*u[e,i,j,k] + G[4,e,i,j,k]*v[e,i,j,k] + G[5,e,i,j,k]*w[e,i,j,k]",
+ "CSE: Vt(i,j,k) = G[6,e,i,j,k]*u[e,i,j,k] + G[7,e,i,j,k]*v[e,i,j,k] + G[8,e,i,j,k]*w[e,i,j,k]",
+
+ # form nonlinear term on integration nodes
+ "Nu[e,i,j,k] = Vr(i,j,k)*ur(i,j,k)+Vs(i,j,k)*us(i,j,k)+Vt(i,j,k)*ut(i,j,k)",
+ "Nv[e,i,j,k] = Vr(i,j,k)*vr(i,j,k)+Vs(i,j,k)*vs(i,j,k)+Vt(i,j,k)*vt(i,j,k)",
+ "Nw[e,i,j,k] = Vr(i,j,k)*wr(i,j,k)+Vs(i,j,k)*ws(i,j,k)+Vt(i,j,k)*wt(i,j,k)",
+ ],
+ [
+ lp.ArrayArg("u", dtype, shape=field_shape, order=order),
+ lp.ArrayArg("v", dtype, shape=field_shape, order=order),
+ lp.ArrayArg("w", dtype, shape=field_shape, order=order),
+ lp.ArrayArg("Nu", dtype, shape=field_shape, order=order),
+ lp.ArrayArg("Nv", dtype, shape=field_shape, order=order),
+ lp.ArrayArg("Nw", dtype, shape=field_shape, order=order),
+ lp.ArrayArg("G", dtype, shape=(9,)+field_shape, order=order),
+ lp.ArrayArg("D", dtype, shape=(N, N), order=order),
+ lp.ScalarArg("K", np.int32, approximately=1000),
+ ],
+ name="sem_advect", assumptions="K>=1")
+
+ print knl
+ 1/0
+
+ seq_knl = knl
+
+ knl = lp.split_dimension(knl, "e", 16, outer_tag="g.0")#, slabs=(0, 1))
+
+ knl = lp.tag_dimensions(knl, dict(i="l.0", j="l.1"))
+
+
+ kernel_gen = lp.generate_loop_schedules(knl)
+ kernel_gen = lp.check_kernels(kernel_gen, dict(K=1000), kill_level_min=5)
+
+
+ K = 1000
+ lp.auto_test_vs_seq(seq_knl, ctx, kernel_gen,
+ op_count=0,
+ op_label="GFlops",
+ parameters={"K": K}, print_seq_code=True,)
+
+
+
+
if __name__ == "__main__":
import sys
if len(sys.argv) > 1: