def func():
ti.loop_config(parallelize=1)
for i in range(2):
t = 0.0
if x[i] > 0:
t = 1 / x[i]
y[i] = t
def foo():
ti.loop_config(block_dim=32)
for i in range(32):
a[i] = i
ti.simt.warp.sync(ti.u32(0xFFFFFFFF))
for i in range(16):
a[i] = a[i + 16]
def foo():
ti.loop_config(block_dim=32)
for i in range(32):
for j in range(5):
offset = 1 << j
a[i] += ti.simt.warp.shfl_xor_i32(ti.u32(0xFFFFFFFF), a[i],
offset)
def foo():
ti.loop_config(block_dim=32)
for i in range(16):
a[i] = 0
a[i + 16] = 1
for i in range(32):
b[i] = ti.simt.warp.match_any(ti.u32(0xFFFFFFFF), a[i])
def func():
ti.loop_config(serialize=True)
for i in range(n):
for j in range(n):
ti.append(x.parent(), j, i)
for i in range(n):
l[i] = ti.length(x.parent(), i)
def foo():
ti.loop_config(block_dim=N)
for i in range(N):
# Make the 0-th thread runs slower intentionally
for j in range(N - i):
a[i] = j
ti.simt.block.sync()
if i > 0:
a[i] = a[0]
def foo() -> ti.i32:
a = 0
ti.loop_config(serialize=True)
for i in range(100):
a = a + 1
if a == 50:
break
return a
def foo():
ti.loop_config(block_dim=32)
for i in range(32):
a[i] = 1
for i in range(32):
b[i] = ti.simt.warp.match_all(ti.u32(0xFFFFFFFF), a[i])
a[0] = 2
for i in range(32):
c[i] = ti.simt.warp.match_all(ti.u32(0xFFFFFFFF), a[i])
def foo():
block_counter = 0
ti.loop_config(block_dim=BLOCK_SIZE)
for i in range(N):
a[i] = 1
ti.simt.grid.memfence()
# Execute a prefix sum after all blocks finish
actual_order_of_block = ti.atomic_add(block_counter, 1)
if actual_order_of_block == N - 1:
for j in range(1, N):
a[j] += a[j - 1]
def evolve_vectorized(x: ti.template(), y: ti.template()):
ti.loop_config(bit_vectorize=True)
for i, j in x:
num_active_neighbors = 0
num_active_neighbors += ti.cast(x[i - 1, j - 1], ti.u32)
num_active_neighbors += ti.cast(x[i - 1, j], ti.u32)
num_active_neighbors += ti.cast(x[i - 1, j + 1], ti.u32)
num_active_neighbors += ti.cast(x[i, j - 1], ti.u32)
num_active_neighbors += ti.cast(x[i, j + 1], ti.u32)
num_active_neighbors += ti.cast(x[i + 1, j - 1], ti.u32)
num_active_neighbors += ti.cast(x[i + 1, j], ti.u32)
num_active_neighbors += ti.cast(x[i + 1, j + 1], ti.u32)
y[i, j] = (num_active_neighbors == 3) | \
((num_active_neighbors == 2) & (x[i, j] == 1))
def substep(g_x: float, g_y: float, g_z: float):
for I in ti.grouped(F_grid_m):
F_grid_v[I] = ti.zero(F_grid_v[I])
F_grid_m[I] = 0
ti.loop_config(block_dim=n_grid)
for p in F_x:
if F_used[p] == 0:
continue
Xp = F_x[p] / dx
base = int(Xp - 0.5)
fx = Xp - base
w = [0.5 * (1.5 - fx)**2, 0.75 - (fx - 1)**2, 0.5 * (fx - 0.5)**2]
F_dg[p] = (ti.Matrix.identity(float, 3) +
dt * F_C[p]) @ F_dg[p] # deformation gradient update
# Hardening coefficient: snow gets harder when compressed
h = ti.exp(10 * (1.0 - F_Jp[p]))
if F_materials[p] == JELLY: # jelly, make it softer
h = 0.3
mu, la = mu_0 * h, lambda_0 * h
if F_materials[p] == WATER: # liquid
mu = 0.0
U, sig, V = ti.svd(F_dg[p])
J = 1.0
for d in ti.static(range(3)):
new_sig = sig[d, d]
if F_materials[p] == SNOW: # Snow
new_sig = min(max(sig[d, d], 1 - 2.5e-2),
1 + 4.5e-3) # Plasticity
F_Jp[p] *= sig[d, d] / new_sig
sig[d, d] = new_sig
J *= new_sig
if F_materials[p] == WATER:
# Reset deformation gradient to avoid numerical instability
new_F = ti.Matrix.identity(float, 3)
new_F[0, 0] = J
F_dg[p] = new_F
elif F_materials[p] == SNOW:
# Reconstruct elastic deformation gradient after plasticity
F_dg[p] = U @ sig @ V.transpose()
stress = 2 * mu * (F_dg[p] - U @ V.transpose()) @ F_dg[p].transpose(
) + ti.Matrix.identity(float, 3) * la * J * (J - 1)
stress = (-dt * p_vol * 4) * stress / dx**2
affine = stress + p_mass * F_C[p]
for offset in ti.static(ti.grouped(ti.ndrange(*neighbour))):
dpos = (offset - fx) * dx
weight = 1.0
for i in ti.static(range(dim)):
weight *= w[offset[i]][i]
F_grid_v[base +
offset] += weight * (p_mass * F_v[p] + affine @ dpos)
F_grid_m[base + offset] += weight * p_mass
for I in ti.grouped(F_grid_m):
if F_grid_m[I] > 0:
F_grid_v[I] /= F_grid_m[I]
F_grid_v[I] += dt * ti.Vector([g_x, g_y, g_z])
cond = (I < bound) & (F_grid_v[I] < 0) | \
(I > n_grid - bound) & (F_grid_v[I] > 0)
F_grid_v[I] = 0 if cond else F_grid_v[I]
ti.loop_config(block_dim=n_grid)
for p in F_x:
if F_used[p] == 0:
continue
Xp = F_x[p] / dx
base = int(Xp - 0.5)
fx = Xp - base
w = [0.5 * (1.5 - fx)**2, 0.75 - (fx - 1)**2, 0.5 * (fx - 0.5)**2]
new_v = ti.zero(F_v[p])
new_C = ti.zero(F_C[p])
for offset in ti.static(ti.grouped(ti.ndrange(*neighbour))):
dpos = (offset - fx) * dx
weight = 1.0
for i in ti.static(range(dim)):
weight *= w[offset[i]][i]
g_v = F_grid_v[base + offset]
new_v += weight * g_v
new_C += 4 * weight * g_v.outer_product(dpos) / dx**2
F_v[p] = new_v
F_x[p] += dt * F_v[p]
F_C[p] = new_C
def build_pid(self):
ti.loop_config(block_dim=256)
for p in self.x:
base = ti.floor(self.x[p] * self.inv_dx - 0.5).cast(int) + 1
ti.append(self.pid.parent(), base, p)
def assign_vectorized(dx: ti.template(), dy: ti.template()):
ti.loop_config(bit_vectorize=True)
for i, j in x:
y[i, j] = x[i + dx, j + dy]
z[i, j] = x[i + dx, j + dy]
def fill():
ti.loop_config(block_dim_adaptive=False)
for i in range(n):
val[i] = i
def test() -> ti.i32:
hit_time = 1
ti.loop_config(block_dim=8)
for i in range(8):
ti.atomic_min(hit_time, 1)
return hit_time
def fill():
ti.loop_config(parallelize=8, block_dim=8)
for i in range(n):
val[i] = i
def foo():
ti.loop_config(block_dim=16)
for i in range(32):
a[i] = ti.simt.warp.active_mask()
def foo():
ti.loop_config(block_dim=32)
for i in range(32):
a[i] = ti.simt.warp.shfl_sync_f32(ti.u32(0xFFFFFFFF), a[i], 0)
def foo():
ti.loop_config(block_dim=32)
for i in range(32):
a[i] = ti.simt.warp.shfl_down_i32(ti.u32(0xFFFFFFFF), b[i], 1)
def foo():
ti.loop_config(block_dim=32)
for i in range(32):
a[i] = ti.simt.warp.ballot(b[i])
def func():
ti.loop_config(serialize=True)
for i in range(n // 4):
x[i * 4][1, 0] = i
def assign_vectorized():
ti.loop_config(bit_vectorize=True)
for i, j in x:
y[i, j] = x[i, j]
def foo():
ti.loop_config(block_dim=32)
for i in range(32):
a[i] = ti.simt.warp.any_nonzero(ti.u32(0xFFFFFFFF), b[i])
def check():
ti.loop_config(block_dim=32)
for i in range(32):
a[i] = ti.simt.warp.unique(ti.u32(0xFFFFFFFF), b[i])
