def test_atomic_xor(self):
expected_result: int = self.i_1 ^ self.i_2
pk.parallel_for(self.range_policy, self.functor.atomic_xor)
result: int = self.functor.view1D_xor[0]
self.assertEqual(expected_result, result)
def run() -> None:
parser = argparse.ArgumentParser()
parser.add_argument('iterations', type=int)
parser.add_argument('length', type=int)
parser.add_argument('offset', nargs='?', type=int, default=0)
args = parser.parse_args()
iterations = args.iterations
length = args.length
offset = args.offset
scalar = 3
if iterations < 1:
sys.exit("ERROR: iterations must be >= 1")
if length <= 0:
sys.exit("ERROR: vector length must be positive")
# emulate cpp example
if length <= 0:
sys.exit("ERROR: offset must be nonnegative")
print("Number of iterations = ", iterations)
print("Vector length = ", length)
print("Offset = ", offset)
p = pk.RangePolicy(pk.ExecutionSpace.OpenMP, 0, length)
w = Workload(iterations, length, offset, scalar)
pk.parallel_for(p, w.init_views)
# pk.fence()
timer = pk.Timer()
for i in range(iterations):
pk.parallel_for(p, w.nstream)
# pk.fence()
nstream_time = timer.seconds()
# verify correctness
ar: float = 0
br: float = 2
cr: float = 2
for i in range(iterations):
ar += br + scalar * cr
ar *= length
asum = pk.parallel_reduce(p, w.res_reduce)
# pk.fence()
episilon: float = 1.0e-8
if (abs(ar - asum) / asum > episilon):
print("ERROR: Failed Valication on output array")
else:
avgtime: float = nstream_time / iterations
nbytes: float = 4.0 * length * 4
print("Solution validates")
print("Rate (MB/s): %.2f" % (1.e-6 * nbytes / avgtime))
print("Avg time (ms): %f" % (avgtime / 1.e-3))
def test_atomic_min(self):
expected_result: float = min(self.f_1, self.f_2)
pk.parallel_for(self.range_policy, self.functor.atomic_min)
result: float = self.functor.view1D_min[0]
self.assertEqual(expected_result, result)
def test_atomic_sub(self):
expected_result: float = self.f_1 - self.f_2
pk.parallel_for(self.range_policy, self.functor.atomic_sub)
result: float = self.functor.view1D_sub[0]
self.assertEqual(expected_result, result)
def benchmark(team: pk.TeamMember, A: pk.View3D[pk.double],
B: pk.View3D[pk.double], C: pk.View3D[pk.double], R: int, F: int,
K: int):
n: int = team.league_rank()
for r in range(R):
def team_for(i: int):
a1: pk.double = A[n][i][0]
b: pk.double = B[n][i][0]
a2: pk.double = a1 * 1.3
a3: pk.double = a2 * 1.1
a4: pk.double = a3 * 1.1
a5: pk.double = a4 * 1.3
a6: pk.double = a5 * 1.1
a7: pk.double = a6 * 1.1
a8: pk.double = a7 * 1.1
for f in range(F):
a1 += b * a1
a2 += b * a2
a3 += b * a3
a4 += b * a4
a5 += b * a5
a6 += b * a6
a7 += b * a7
a8 += b * a8
C[n][i][0] = a1 + a2 + a3 + a4 + a5 + a6 + a7 + a8
pk.parallel_for(pk.TeamThreadRange(team, K), team_for)
def run(self):
timer = pk.Timer()
for r in range(self.R):
pk.parallel_for("gather", self.N, self.benchmark)
pk.fence()
self.seconds = timer.seconds()
def benchmark(self, team: pk.TeamMember):
n: int = team.league_rank()
for r in range(self.R):
def team_for(i: int):
a1: pk.double = self.A[n][i][0]
b: pk.double = self.B[n][i][0]
a2: pk.double = a1 * 1.3
a3: pk.double = a2 * 1.1
a4: pk.double = a3 * 1.1
a5: pk.double = a4 * 1.3
a6: pk.double = a5 * 1.1
a7: pk.double = a6 * 1.1
a8: pk.double = a7 * 1.1
for f in range(self.F):
a1 += b * a1
a2 += b * a2
a3 += b * a3
a4 += b * a4
a5 += b * a5
a6 += b * a6
a7 += b * a7
a8 += b * a8
self.C[n][i][0] = a1 + a2 + a3 + a4 + a5 + a6 + a7 + a8
pk.parallel_for(pk.TeamThreadRange(team, self.K), team_for)
def run(self) -> None:
nbin: List[int] = [self.nbinx, self.nbiny, self.nbinz]
min_values: List[float] = [self.minx, self.miny, self.miny]
max_values: List[float] = [self.maxx, self.maxy, self.maxz]
x_sub = self.x[self.range_min:self.range_max, :]
binop = pk.BinOp3D(x_sub, nbin, min_values, max_values)
sorter = pk.BinSort(x_sub, binop)
sorter.create_permute_vector()
self.permute_vector = sorter.get_permute_vector()
self.bin_count_1d = sorter.get_bin_count()
self.bin_offsets_1d = sorter.get_bin_offsets()
pk.parallel_for("Binning::AssignOffsets",
self.nbinx * self.nbiny * self.nbinz,
self.assign_offsets)
if self.sort:
sorter.sort(x_sub)
v_sub = self.v[self.range_min:self.range_max, :]
sorter.sort(v_sub)
f_sub = self.f[self.range_min:self.range_max, :]
sorter.sort(f_sub)
sorter.sort(self.type)
sorter.sort(self.id)
sorter.sort(self.q)
def test_v1d(self):
pk.parallel_for(self.range_policy, self.functor.v1d)
for i in range(self.i_1):
expected_result: int = self.i_4 + i
self.assertEqual(expected_result, self.functor.view1D[i])
self.assertEqual(expected_result, self.functor.myView1D[i])
def run(self):
pk.parallel_for(self.N, lambda i: i, self.A)
timer = pk.Timer()
self.result = pk.parallel_scan(self.N, self.scan)
self.timer_result = timer.seconds()
def test_v2d(self):
pk.parallel_for(self.range_policy, self.functor.v2d)
for i in range(self.i_1):
for j in range(self.i_2):
expected_result: int = self.i_4 + i + j
self.assertEqual(expected_result, self.functor.view2D[i][j])
self.assertEqual(expected_result, self.functor.myView2D[i][j])
def run(self):
timer = pk.Timer()
pk.parallel_for(self.N, self.matrix_init)
for i in range(self.nrepeat):
self.result = pk.parallel_reduce("04", self.N, self.yAx)
self.timer_result = timer.seconds()
def test_outer_for(self):
expected_result: float = 0
for i in range(self.M):
expected_result += self.value
pk.parallel_for(pk.TeamPolicy(self.N, pk.AUTO, space=self.execution_space), self.functor.outer_for)
for i in range(self.N):
result: int = self.functor.for_view[i]
self.assertEqual(expected_result, result)
def test_subscript(self):
expected_result = self.i_1
pk.parallel_for(self.range_policy, self.functor.subscript)
for i in range(self.threads):
self.assertEqual(expected_result, self.functor.view1D[i])
for j in range(self.threads):
self.assertEqual(expected_result, self.functor.view2D[i][j])
for k in range(self.threads):
self.assertEqual(expected_result,
self.functor.view3D[i][j][k])
def run(self) -> None:
x: List[int] = [self.x_0, 2, 3]
pk.parallel_for(self.total_threads, self.work)
bin_op = pk.BinOp1D(self.view, (self.total_threads // 2),
self.total_threads, self.total_threads * 2 - 1)
bin_sort = pk.BinSort(self.view, bin_op)
bin_sort.create_permute_vector()
self.permute_vector = bin_sort.get_permute_vector()
self.bin_offsets = bin_sort.get_bin_offsets()
self.bin_count = bin_sort.get_bin_count()
bin_sort.sort(self.view)
def yAx_plus1(self, team_member: pk.TeamMember, acc: pk.Acc[pk.double]) -> None:
j: int = team_member.league_rank()
def inner_reduce(i: int, inner_acc: pk.Acc[pk.double]):
inner_acc += self.A[j][i] * self.x[i]
def inner_for(i: int):
self.yprime[j][i] += 1
temp2: float = pk.parallel_reduce(pk.TeamThreadRange(team_member, self.M), inner_reduce)
pk.parallel_for(pk.TeamThreadRange(team_member, self.N), inner_for)
if team_member.team_rank() == 0:
acc += self.yprime[j][j] * temp2
def run(self):
t: int = tile_size
r: int = radius
pk.parallel_for(pk.MDRangePolicy([0, 0], [n, n], [t, t]), self.init)
pk.fence()
timer = pk.Timer()
for i in range(iterations):
if (i == 1):
pk.fence()
if r == 1:
# star1 stencil
pk.parallel_for(
"stencil", pk.MDRangePolicy([r, r], [n - r, n - r],
[t, t]), self.star1)
elif r == 2:
# star2 stencil
pk.parallel_for(
"stencil", pk.MDRangePolicy([r, r], [n - r, n - r],
[t, t]), self.star2)
else:
# star3 stencil
pk.parallel_for(
"stencil", pk.MDRangePolicy([r, r], [n - r, n - r],
[t, t]), self.star3)
pk.parallel_for(pk.MDRangePolicy([0, 0], [n, n], [t, t]),
self.increment)
pk.fence()
self.stencil_time = timer.seconds()
active_points: int = (n - 2 * r) * (n - 2 * r)
# verify correctness
self.norm = pk.parallel_reduce(
pk.MDRangePolicy([r, r], [n - r, n - r], [t, t]), self.norm_reduce)
pk.fence()
self.norm /= active_points
episilon: float = 1.0e-8
reference_norm: float = 2 * (iterations)
if (abs(self.norm - reference_norm) > episilon):
pk.printf("ERROR: L1 norm != Reference norm err=%.2f\n",
abs(self.norm - reference_norm))
else:
pk.printf("Solution validates\n")
def run() -> None:
random.seed(1010101)
indices = 8192
data = 33554432
repeats = 10
space = pk.ExecutionSpace.OpenMP
parser = argparse.ArgumentParser()
parser.add_argument("--indices", type=int)
parser.add_argument("--data", type=int)
parser.add_argument("--repeats", type=int)
parser.add_argument("--atomics", action="store_true")
parser.add_argument("--execution_space", type=str)
args = parser.parse_args()
if args.indices:
indices = args.indices
if args.data:
data = args.data
if args.repeats:
repeats = args.repeats
use_atomics = args.atomics
if args.execution_space:
space = pk.ExecutionSpace(args.execution_space)
pk.set_default_space(space)
w = Benchmark(indices, data, repeats, use_atomics)
range_indices = pk.RangePolicy(pk.get_default_space(), 0, indices)
range_data = pk.RangePolicy(pk.get_default_space(), 0, data)
print("Reports fastest timing per kernel")
print("Creating Views...")
print("Memory Sizes:")
print(f"- Elements: {data} ({1e-6*data*8} MB)")
print(f"- Indices: {indices} ({1e-6*indices*8} MB)")
print(f"- Atomics: {'yes' if use_atomics else 'no'}")
print(f"Benchmark kernels will be performed for {repeats} iterations")
print("Initializing Views...")
pk.parallel_for(range_data, w.init_data)
pk.parallel_for(range_indices, w.init_indices)
print("Starting benchmarking...")
timer = pk.Timer()
for i in range(repeats):
for i in range(indices):
w.indices[i] = random.randrange(data)
if use_atomics:
pk.parallel_for(range_indices, w.run_gups_atomic)
else:
pk.parallel_for(range_indices, w.run_gups)
gupsTime = timer.seconds()
print(f"GUP/s Random: {1e-9 * repeats * indices / gupsTime}")
print(w.data)
def run(self) -> None:
if self.parallel_for:
if self.half_neigh:
pk.parallel_for("ForceLJNeigh::compute", self.N_local,
self.halfneigh_for)
else:
pk.parallel_for("ForceLJNeigh::compute", self.N_local,
self.fullneigh_for)
else:
if self.half_neigh:
self.energy = pk.parallel_reduce(
"ForceLJNeigh::compute_energy", self.N_local,
self.halfneigh_reduce)
else:
self.energy = pk.parallel_reduce(
"ForceLJNeigh::compute_energy", self.N_local,
self.fullneigh_reduce)
def run(self) -> None:
if self.workunit_id == 0:
pk.parallel_for("CommSerial::exchange_self", self.N_local,
self.tag_exchange_self)
elif self.workunit_id == 1:
pk.parallel_for("CommSerial::halo_exchange_self", self.nparticles,
self.tag_halo_self)
elif self.workunit_id == 2:
pk.parallel_for("CommSerial::halo_update_self",
self.update_threads, self.tag_halo_update_self)
elif self.workunit_id == 3:
pk.parallel_for("CommSerial::halo_force_self", self.force_threads,
self.tag_halo_force_self)
def test_real(self):
pk.set_default_precision(pk.int32)
view: pk.View1d = pk.View([self.threads])
self.assertTrue(view.dtype is pk.DataType.int32)
self.assertTrue(
pk.View._get_dtype_name(str(type(view.array))) == "int32")
f = RealViewTestFunctor(view)
w = RealViewTestWorkload(self.threads, view)
pk.parallel_for(self.threads, f.pfor)
pk.execute(pk.ExecutionSpace.Default, w)
view.set_precision(pk.float)
self.assertTrue(view.dtype is pk.DataType.float)
self.assertTrue(
pk.View._get_dtype_name(str(type(view.array))) == "float32")
pk.parallel_for(self.threads, f.pfor)
pk.execute(pk.ExecutionSpace.Default, w)
def run() -> None:
values: Tuple[int, int, int, int, int, bool] = parse_args()
N: int = values[0]
M: int = values[1]
nrepeat: int = 1
print(f"Total size S = {N * M} N = {N} M = {M}")
y = pk.View([N], pk.double)
x = pk.View([M], pk.double)
A = pk.View([N * M], pk.double)
p = pk.RangePolicy(pk.get_default_space(), 0, N)
pk.parallel_for(p, y_init, y=y)
pk.parallel_for(pk.RangePolicy(pk.get_default_space(), 0, M), y_init, y=x)
pk.parallel_for(p, matrix_init, M=M, A=A)
timer = pk.Timer()
for i in range(nrepeat):
result = pk.parallel_reduce(p, yAx, M=M, y=y, x=x, A=A)
timer_result = timer.seconds()
print(f"Computed result for {N} x {M} is {result}")
solution = N * M
if result != solution:
pk.printf("Error: result (%lf) != solution (%lf)\n", result, solution)
print(f"N({N}) M({M}) nrepeat({nrepeat}) problem(MB) time({timer_result}) bandwidth(GB/s)")
def run() -> None:
values: Tuple[int, int, int, int, int, bool] = parse_args()
N: int = values[0]
M: int = values[1]
nrepeat: int = 100
print(f"Total size S = {N * M} N = {N} M = {M}")
p = pk.RangePolicy(pk.get_default_space(), 0, N)
w = Workload(N, M)
pk.parallel_for(p, w.y_init)
pk.parallel_for(pk.RangePolicy(pk.get_default_space(), 0, M), w.x_init)
pk.parallel_for(p, w.matrix_init)
timer = pk.Timer()
for i in range(nrepeat):
result = pk.parallel_reduce(p, w.yAx)
timer_result = timer.seconds()
print(f"Computed result for {N} x {M} is {result}")
solution = N * M
if result != solution:
pk.printf("Error: result (%lf) != solution (%lf)\n", result, solution)
print(f"N({N}) M({M}) nrepeat({nrepeat}) problem(MB) time({timer_result}) bandwidth(GB/s)")
def run(self):
pk.parallel_for(self.array_size, self.init_arrays)
timer = pk.Timer()
for i in range(self.num_times):
pk.parallel_for("babel_stream", self.array_size, self.copy)
pk.fence()
# self.runtimes[0][i] = timer.seconds()
# timer.reset()
pk.parallel_for("babel_stream", self.array_size, self.mul)
pk.fence()
# self.runtimes[1][i] = timer.seconds()
# timer.reset()
pk.parallel_for("babel_stream", self.array_size, self.add)
pk.fence()
pk.parallel_for("babel_stream", self.array_size, self.triad)
pk.fence()
self.sum = pk.parallel_reduce("babel_stream", self.array_size,
self.dot)
self.runtime = timer.seconds()
def run(self):
pk.parallel_for(self.length, self.init)
# pk.parallel_for(self.length, lambda i: 0, self.A)
# pk.parallel_for(self.length, lambda i: 2, self.B)
# pk.parallel_for(self.length, lambda i: 2, self.C)
pk.fence()
timer = pk.Timer()
for i in range(self.iterations):
pk.parallel_for("nstream", self.length, self.nstream)
pk.fence()
self.nstream_time = timer.seconds()
# verify correctness
ar: float = 0
br: float = 2
cr: float = 2
for i in range(self.iterations):
ar += br + self.scalar * cr
ar *= self.length
self.asum = pk.parallel_reduce(self.length,
lambda i, acc: acc + abs(self.A[i]))
pk.fence()
episilon: float = 1.0e-8
if (abs(ar - self.asum) / self.asum > episilon):
pk.printf("ERROR: Failed Valication on output array\n")
else:
avgtime: float = self.nstream_time / self.iterations
nbytes: float = 4.0 * self.length * 4
pk.printf("Solution validates\n")
pk.printf("Rate (MB/s): %.2f\n", 1.e-6 * nbytes / avgtime)
pk.printf("Avg time (ms): %f\n", avgtime / 1.e-3)
def run(self):
pk.parallel_for(N, self.init_y)
pk.parallel_for(M, self.init_x)
pk.parallel_for(pk.MDRangePolicy([0, 0], [self.N, self.M]),
self.init_A)
timer = pk.Timer()
for i in range(self.nrepeat):
self.result = pk.parallel_reduce("mdrange", self.N, self.yAx)
self.timer_result = timer.seconds()
def run(self):
pk.parallel_for(self.N, self.y_init)
# pk.parallel_for(self.N, lambda i : self.y[i] = 1)
pk.parallel_for(self.M, self.x_init)
# pk.parallel_for(self.N, lambda i : self.x[i] = 1)
pk.parallel_for(self.N, self.matrix_init)
timer = pk.Timer()
for i in range(self.nrepeat):
self.result = pk.parallel_reduce("01", self.N, self.yAx)
self.timer_result = timer.seconds()
def run() -> None:
values: Tuple[int, int, int, int, int, bool] = parse_args()
N: int = values[0]
M: int = values[1]
fill: bool = values[-1]
nrepeat: int = 100
print(f"Total size S = {N * M} N = {N} M = {M}")
pk.set_default_space(pk.ExecutionSpace.Cuda)
y: pk.View1D = pk.View([N], pk.double)
x: pk.View1D = pk.View([M], pk.double)
A: pk.View2D = pk.View([N, M], pk.double)
p = pk.RangePolicy(pk.get_default_space(), 0, N)
pk.parallel_for(p, y_init, y=y)
pk.parallel_for(pk.RangePolicy(pk.get_default_space(), 0, M), y_init, y=x)
pk.parallel_for(p, matrix_init, M=M, A=A)
# if fill:
# y.fill(1)
# x.fill(1)
# A.fill(1)
# else:
# for i in range(N):
# y[i] = 1
# for i in range(M):
# x[i] = 1
# for j in range(N):
# for i in range(M):
# A[j][i] = 1
timer = pk.Timer()
for i in range(nrepeat):
result = pk.parallel_reduce(p, yAx, M=M, y=y, x=x, A=A)
timer_result = timer.seconds()
print(f"Computed result for {N} x {M} is {result}")
solution: float = N * M
if result != solution:
pk.printf("Error: result (%lf) != solution (%lf)\n", result, solution)
print(
f"N({N}) M({M}) nrepeat({nrepeat}) problem(MB) time({timer_result}) bandwidth(GB/s)"
)
def run(self):
printf("Initializing Views...\n")
pk.parallel_for(self.dataCount, self.init_data)
pk.parallel_for(self.indicesCount, self.init_indices)
printf("Starting benchmarking...\n")
pk.fence()
timer = pk.Timer()
for i in range(self.repeats):
# FIXME: randomize indices
# for i in range(self.indicesCount):
# self.indices[i] = random.randrange(self.dataCount)
if self.use_atomics:
pk.parallel_for("gups", self.indicesCount,
self.run_gups_atomic)
else:
pk.parallel_for("gups", self.indicesCount, self.run_gups)
pk.fence()
self.gupsTime = timer.seconds()
def run(self):
pk.parallel_for(
pk.MDRangePolicy([0, 0], [self.order, self.order],
[self.tile_size, self.tile_size]), self.init)
pk.fence()
timer = pk.Timer()
for i in range(self.iterations):
if self.permute:
pk.parallel_for(
"transpose",
pk.MDRangePolicy([0, 0], [self.order, self.order],
[self.tile_size, self.tile_size],
rank=pk.Rank(2, pk.Iterate.Left,
pk.Iterate.Right)),
self.tranpose)
else:
pk.parallel_for(
"transpose",
pk.MDRangePolicy([0, 0], [self.order, self.order],
[self.tile_size, self.tile_size],
rank=pk.Rank(2, pk.Iterate.Right,
pk.Iterate.Left)),
self.tranpose)
self.transpose_time = timer.seconds()
self.abserr = pk.parallel_reduce(
pk.MDRangePolicy([0, 0], [self.order, self.order],
[self.tile_size, self.tile_size]),
self.abserr_reduce)
pk.printf("%f\n", self.abserr)
episilon: float = 1.0e-8
if (self.abserr > episilon):
pk.printf(
"ERROR: aggregated squared error exceeds threshold %.2f\n",
self.abserr)
else:
pk.printf("Solution validates %2.f\n", self.abserr)
