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delta sim for improving fault sim performance

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stefan committed 2024-04-08 00:29:12 +09:00
1 parent a4b7364478
commit 4c55dcec60
1 file changed
+35 -16
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src/kyupy/wave_sim.py
+35 -16
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@@ -99,7 +99,7 @@ class WaveSim(sim.SimOps):
self.simctl_int[0] = range(sims) # unique seed for each sim by default, zero this to pick same delays for all sims.
self.simctl_int[1] = 2 # random picking by default.
self.e = np.zeros((self.c_locs_len, sims), dtype=np.uint8) # aux data for each line and sim
self.e = np.zeros((self.c_locs_len, sims, 2), dtype=np.uint8) # aux data for each line and sim
self.error_counts = np.zeros(self.s_len, dtype=np.uint32) # number of capture errors by PPO
self.lsts = np.zeros(self.s_len, dtype=np.float32) # LST by PPO
@@ -123,7 +123,7 @@ class WaveSim(sim.SimOps):
self.c[self.pippi_c_locs+1] = np.choose(cond, [TMAX, TMAX, sins[1], TMAX])
self.c[self.pippi_c_locs+2] = TMAX
def c_prop(self, sims=None, seed=1):
def c_prop(self, sims=None, seed=1, delta=0):
"""Propagates all waveforms from the (pseudo) primary inputs to the (pseudo) primary outputs.
:param sims: Number of parallel simulations to execute. If None, all available simulations are performed.
@@ -131,7 +131,7 @@ class WaveSim(sim.SimOps):
"""
sims = min(sims or self.sims, self.sims)
for op_start, op_stop in zip(self.level_starts, self.level_stops):
level_eval_cpu(self.ops, op_start, op_stop, self.c, self.c_locs, self.c_caps, self.e, self.abuf, 0, sims, self.delays, self.simctl_int, seed)
level_eval_cpu(self.ops, op_start, op_stop, self.c, self.c_locs, self.c_caps, self.e, self.abuf, 0, sims, self.delays, self.simctl_int, seed, delta)
def c_to_s(self, time=TMAX, sd=0.0, seed=1):
"""Simulates a capture operation at all sequential elements and primary outputs.
@@ -159,7 +159,7 @@ class WaveSim(sim.SimOps):
self.s[2, self.ppio_s_locs] = self.s[8, self.ppio_s_locs]
def _wave_eval(op, cbuf, c_locs, c_caps, ebuf, sim, delays, simctl_int, seed):
def _wave_eval(op, cbuf, c_locs, c_caps, ebuf, sim, delays, simctl_int, seed, delta):
overflows = int(0)
lut = op[0]
@@ -169,6 +169,18 @@ def _wave_eval(op, cbuf, c_locs, c_caps, ebuf, sim, delays, simctl_int, seed):
c_idx = op[4]
d_idx = op[5]
input_epoch = (ebuf[a_idx, sim, 1]|
ebuf[b_idx, sim, 1]|
ebuf[c_idx, sim, 1]|
ebuf[d_idx, sim, 1])
output_epoch = ebuf[z_idx, sim, 1]
if (delta):
if input_epoch == 0 and output_epoch == 0: return 0, 0
out_changed = output_epoch
if len(delays) > 1:
if simctl_int[1] == 0:
delays = delays[seed]
@@ -242,6 +254,8 @@ def _wave_eval(op, cbuf, c_locs, c_caps, ebuf, sim, delays, simctl_int, seed):
or (current_t - previous_t) > thresh # -OR- the generated hazard is wider than pulse threshold.
):
if z_cur < (z_cap - 1): # enough space in z_mem?
if delta and (cbuf[z_mem + z_cur, sim] != current_t):
out_changed = 1
cbuf[z_mem + z_cur, sim] = current_t
previous_t = current_t
z_cur += 1
@@ -262,6 +276,9 @@ def _wave_eval(op, cbuf, c_locs, c_caps, ebuf, sim, delays, simctl_int, seed):
current_t = min(a, b, c, d)
if delta and (cbuf[z_mem + z_cur, sim] != TMAX):
out_changed = 1
# generate or propagate overflow flag
cbuf[z_mem + z_cur, sim] = TMAX_OVL if overflows > 0 else max(a, b, c, d)
@@ -272,7 +289,9 @@ def _wave_eval(op, cbuf, c_locs, c_caps, ebuf, sim, delays, simctl_int, seed):
e |= z_val # final value
e |= (nrise + nfall)<<2 # number of transitions
ebuf[z_idx, sim] = e
ebuf[z_idx, sim, 0] = e
ebuf[z_idx, sim, 1] = input_epoch & out_changed
return nrise, nfall
@@ -281,11 +300,11 @@ wave_eval_cpu = numba.njit(_wave_eval)
@numba.njit
def level_eval_cpu(ops, op_start, op_stop, c, c_locs, c_caps, ebuf, abuf, sim_start, sim_stop, delays, simctl_int, seed):
def level_eval_cpu(ops, op_start, op_stop, c, c_locs, c_caps, ebuf, abuf, sim_start, sim_stop, delays, simctl_int, seed, delta):
for op_idx in range(op_start, op_stop):
op = ops[op_idx]
for sim in range(sim_start, sim_stop):
nrise, nfall = wave_eval_cpu(op, c, c_locs, c_caps, ebuf, sim, delays, simctl_int[:, sim], seed)
nrise, nfall = wave_eval_cpu(op, c, c_locs, c_caps, ebuf, sim, delays, simctl_int[:, sim], seed, delta)
a_loc = op[6]
a_wr = op[7]
a_wf = op[8]
@@ -363,7 +382,7 @@ class WaveSimCuda(WaveSim):
self.lsts = cuda.to_device(self.lsts)
self.overflows = cuda.to_device(self.overflows)
self.retval_int = cuda.to_device(np.array([0], dtype=np.int32))
self.aux = cuda.to_device(np.zeros(8*1024, dtype=np.int32))
self._block_dim = (32, 16)
@@ -381,7 +400,7 @@ class WaveSimCuda(WaveSim):
state['error_counts'] = np.array(self.error_counts)
state['lsts'] = np.array(self.lsts)
state['overflows'] = np.array(self.overflows)
state['retval_int'] = np.array(self.retval_int)
state['aux'] = np.array(self.aux)
return state
def __setstate__(self, state):
@@ -398,7 +417,7 @@ class WaveSimCuda(WaveSim):
self.error_counts = cuda.to_device(self.error_counts)
self.lsts = cuda.to_device(self.lsts)
self.overflows = cuda.to_device(self.overflows)
self.retval_int = cuda.to_device(self.retval_int)
self.aux = cuda.to_device(self.aux)
def s_to_c(self):
grid_dim = self._grid_dim(self.sims, self.s_len)
@@ -406,23 +425,23 @@ class WaveSimCuda(WaveSim):
def _grid_dim(self, x, y): return cdiv(x, self._block_dim[0]), cdiv(y, self._block_dim[1])
def c_prop(self, sims=None, seed=1, op_from=0, op_to=None):
def c_prop(self, sims=None, seed=1, op_from=0, op_to=None, delta=0):
sims = min(sims or self.sims, self.sims)
for op_start, op_stop in zip(self.level_starts, self.level_stops):
if op_from > op_start: continue
if op_to is not None and op_to <= op_start: break
grid_dim = self._grid_dim(sims, op_stop - op_start)
wave_eval_gpu[grid_dim, self._block_dim](self.ops, op_start, op_stop, self.c, self.c_locs, self.c_caps, self.e, self.abuf, int(0),
sims, self.delays, self.simctl_int, seed)
sims, self.delays, self.simctl_int, seed, delta)
cuda.synchronize()
def c_prop_level(self, level, sims=None, seed=1):
def c_prop_level(self, level, sims=None, seed=1, delta=0):
sims = min(sims or self.sims, self.sims)
op_start = self.level_starts[level]
op_stop = self.level_stops[level]
grid_dim = self._grid_dim(sims, op_stop - op_start)
wave_eval_gpu[grid_dim, self._block_dim](self.ops, op_start, op_stop, self.c, self.c_locs, self.c_caps, self.e, self.abuf, int(0),
sims, self.delays, self.simctl_int, seed)
sims, self.delays, self.simctl_int, seed, delta)
def c_to_s(self, time=TMAX, sd=0.0, seed=1):
grid_dim = self._grid_dim(self.sims, self.s_len)
@@ -533,7 +552,7 @@ _wave_eval_gpu = cuda.jit(_wave_eval, device=True)
@cuda.jit()
def wave_eval_gpu(ops, op_start, op_stop, cbuf, c_locs, c_caps, ebuf, abuf, sim_start, sim_stop, delays, simctl_int, seed):
def wave_eval_gpu(ops, op_start, op_stop, cbuf, c_locs, c_caps, ebuf, abuf, sim_start, sim_stop, delays, simctl_int, seed, delta):
x, y = cuda.grid(2)
sim = sim_start + x
op_idx = op_start + y
@@ -545,7 +564,7 @@ def wave_eval_gpu(ops, op_start, op_stop, cbuf, c_locs, c_caps, ebuf, abuf, sim_
a_wr = op[7]
a_wf = op[8]
nrise, nfall = _wave_eval_gpu(op, cbuf, c_locs, c_caps, ebuf, sim, delays, simctl_int[:, sim], seed)
nrise, nfall = _wave_eval_gpu(op, cbuf, c_locs, c_caps, ebuf, sim, delays, simctl_int[:, sim], seed, delta)
# accumulate WSA into abuf
if a_loc >= 0: