more on-gpu code, bump python requirement

setup.py

@@ -14,7 +14,7 @@ setup(
     url='https://github.com/s-holst/kyupy',
     author='Stefan Holst',
     author_email='mail@s-holst.de',
-    python_requires='>=3.6',
+    python_requires='>=3.8',
     install_requires=[
         'numpy>=1.17.0',
         'lark-parser>=0.8.0'
@@ -33,9 +33,8 @@ setup(
         'Operating System :: OS Independent',
         'Programming Language :: Python :: 3',
         'Programming Language :: Python :: 3 :: Only',
-        'Programming Language :: Python :: 3.6',
-        'Programming Language :: Python :: 3.7',
         'Programming Language :: Python :: 3.8',
         'Programming Language :: Python :: 3.9',
+        'Programming Language :: Python :: 3.10',
     ],
 )

src/kyupy/sim.py

@@ -158,6 +158,7 @@ class SimOps:
     def __init__(self, circuit, c_caps=1, c_reuse=False, strip_forks=False):
         self.circuit = circuit
         self.s_nodes = list(circuit.io_nodes) + [n for n in circuit.nodes if 'dff' in n.kind.lower()]
+        self.s_len = len(self.s_nodes)
         keep_signals = not c_reuse
         
         if isinstance(c_caps, int):

src/kyupy/wave_sim.py

@@ -353,17 +353,9 @@ class WaveSimCuda(WaveSim):
         
         self._block_dim = (32, 16)
 
-    # TODO implement on GPU
     def s_to_c(self):
-        s = np.array(self.s)
-        c = np.array(self.c)
-        sins = np.moveaxis(s[self.pippi_s_locs], -1, 0)
-        cond = (sins[2] != 0) + 2*(sins[0] != 0)  # choices order: 0 R F 1
-        c[self.pippi_c_locs] = np.choose(cond, [TMAX, sins[1], TMIN, TMIN])
-        c[self.pippi_c_locs+1] = np.choose(cond, [TMAX, TMAX, sins[1], TMAX])
-        c[self.pippi_c_locs+2] = TMAX
-        self.s[:,:,:] = s
-        self.c[:,:] = c
+        grid_dim = self._grid_dim(self.sims, self.s_len)
+        wave_assign_gpu[grid_dim, self._block_dim](self.c, self.s, self.vat, self.ppi_offset)
 
     def _grid_dim(self, x, y):
         gx = math.ceil(x / self._block_dim[0])
@@ -378,19 +370,38 @@ class WaveSimCuda(WaveSim):
                 sims, self.timing, self.params, sd, seed)
         cuda.synchronize()
     
-    # TODO implement on GPU
     def c_to_s(self, time=TMAX, sd=0.0, seed=1):
-        s = np.array(self.s)
-        c = np.array(self.c)
-        vat = np.array(self.vat)
-        for s_loc, (c_loc, c_len, _) in zip(self.poppo_s_locs, vat[self.ppo_offset+self.poppo_s_locs]):
-            for vector in range(self.sims):
-                s[s_loc, vector, 3:] = wave_capture_cpu(c, c_loc, c_len, vector, time=time, sd=sd, seed=seed)
-        self.s[:,:,:] = s
-        self.c[:,:] = c
+        grid_dim = self._grid_dim(self.sims, self.s_len)
+        wave_capture_gpu[grid_dim, self._block_dim](self.c, self.s, self.vat, self.ppo_offset,
+            time, sd * math.sqrt(2), seed)
+    
+    def s_ppo_to_ppi(self, time=0.0):
+        grid_dim = self._grid_dim(self.sims, self.s_len)
+        ppo_to_ppi_gpu[grid_dim, self._block_dim](self.s, self.vat, time, self.ppi_offset, self.ppo_offset)
 
-    # TODO implement on GPU
-    #def s_ppo_to_ppi(self, time=0.0):
+
+@cuda.jit()
+def wave_assign_gpu(c, s, vat, ppi_offset):
+    x, y = cuda.grid(2)
+    if y >= len(s): return
+    c_loc, c_len, _ = vat[ppi_offset + y]
+    if c_loc < 0: return
+    if x >= c.shape[-1]: return
+    value = int(s[y, x, 2] >= 0.5) | (2*int(s[y, x, 0] >= 0.5))
+    ttime = s[y, x, 1]
+    if value == 0:
+        c[c_loc, x] = TMAX
+        c[c_loc+1, x] = TMAX
+    elif value == 1:
+        c[c_loc, x] = ttime
+        c[c_loc+1, x] = TMAX
+    elif value == 2:
+        c[c_loc, x] = TMIN
+        c[c_loc+1, x] = ttime
+    else:
+        c[c_loc, x] = TMIN
+        c[c_loc+1, x] = TMAX
+    c[c_loc+2, x] = TMAX
 
 
 @cuda.jit(device=True)
@@ -527,3 +538,75 @@ def wave_eval_gpu(ops, op_start, op_stop, cbuf, vat, st_start, st_stop, line_tim
 
     # generate overflow flag or propagate from input
     cbuf[z_mem + z_cur, st_idx] = TMAX_OVL if overflows > 0 else max(a, b, c, d)
+
+
+@cuda.jit()
+def wave_capture_gpu(c, s, vat, ppo_offset, time, s_sqrt2, seed):
+    x, y = cuda.grid(2)
+    if ppo_offset + y >= len(vat): return
+    line, tdim, _ = vat[ppo_offset + y]
+    if line < 0: return
+    if x >= c.shape[-1]: return
+    vector = x
+    m = 0.5
+    acc = 0.0
+    eat = TMAX
+    lst = TMIN
+    tog = 0
+    ovl = 0
+    val = int(0)
+    final = int(0)
+    for tidx in range(tdim):
+        t = c[line + tidx, vector]
+        if t >= TMAX:
+            if t == TMAX_OVL:
+                ovl = 1
+            break
+        m = -m
+        final ^= 1
+        if t < time:
+            val ^= 1
+        if t <= TMIN: continue
+        if s_sqrt2 > 0:
+            acc += m * (1 + math.erf((t - time) / s_sqrt2))
+        eat = min(eat, t)
+        lst = max(lst, t)
+        tog += 1
+    if s_sqrt2 > 0:
+        if m < 0:
+            acc += 1
+        if acc >= 0.99:
+            val = 1
+        elif acc > 0.01:
+            seed = (seed << 4) + (vector << 20) + (y << 1)
+            seed = int(0xDEECE66D) * seed + 0xB
+            seed = int(0xDEECE66D) * seed + 0xB
+            rnd = float((seed >> 8) & 0xffffff) / float(1 << 24)
+            val = rnd < acc
+        else:
+            val = 0
+    else:
+        acc = val
+
+    s[y, vector, 3] = (c[line, vector] <= TMIN)
+    s[y, vector, 4] = eat
+    s[y, vector, 5] = lst
+    s[y, vector, 6] = final
+    s[y, vector, 7] = acc
+    s[y, vector, 8] = val
+    s[y, vector, 9] = 0  # TODO
+    s[y, vector, 10] = ovl
+
+
+@cuda.jit()
+def ppo_to_ppi_gpu(s, vat, time, ppi_offset, ppo_offset):
+    x, y = cuda.grid(2)
+    if y >= s.shape[0]: return
+    if x >= s.shape[1]: return
+    
+    if vat[ppi_offset + y, 0] < 0: return
+    if vat[ppo_offset + y, 0] < 0: return
+    
+    s[y, x, 0] = s[y, x, 2]
+    s[y, x, 1] = time
+    s[y, x, 2] = s[y, x, 8]