diff options
Diffstat (limited to 'ethosu/vela')
-rw-r--r-- | ethosu/vela/hillclimb_allocation.py | 27 |
1 files changed, 23 insertions, 4 deletions
diff --git a/ethosu/vela/hillclimb_allocation.py b/ethosu/vela/hillclimb_allocation.py index 2271fe9c..6d3003c5 100644 --- a/ethosu/vela/hillclimb_allocation.py +++ b/ethosu/vela/hillclimb_allocation.py @@ -94,6 +94,8 @@ class HillClimbAllocator: NOT_ALLOCATED = -1 # Used for live ranges allocated at address 0 NO_PREDECESSOR = -1 + # Special handling if best solution has not improved during this many iterations + MAX_ITERATIONS_STUCK = 50 def __init__(self, live_ranges: List[LiveRange]): # Contains the live ranges @@ -190,7 +192,7 @@ class HillClimbAllocator: turn_set.add(turn) turn_list.append(turn) - def attempt_bottleneck_fix(self, indices: List[int]): + def attempt_bottleneck_fix(self, indices: List[int], iterations_stuck): """ Finds the "bottleneck", the live range with highest end address, and reorders the indices such that a next allocation might lower the memory usage. @@ -255,24 +257,41 @@ class HillClimbAllocator: ix2 = turn_list[-1] # Swap indices indices[ix1], indices[ix2] = indices[ix2], indices[ix1] + if iterations_stuck > HillClimbAllocator.MAX_ITERATIONS_STUCK: + # The best allocation has not improved for a while, maybe improvement is not possible + # by single-swapping indices; add more neighbour live ranges and swap up to two more indices. + # Adding more neighbours can sometimes resolve the situation where the current bottleneck + # is resolved, but always results in a higher bottleneck at a nearby live range. + # Magic number is based on tuning + for turn in non_nb_turn_list: + for lr in self.lrs[indices[turn]].neighbours: + if lr.turn not in turn_set: + turn_set.add(lr.turn) + turn_list.append(lr.turn) + ix1 = turn_list[random.randint(0, len(turn_list) - 1)] + ix2 = turn_list[random.randint(0, len(turn_list) - 1)] + indices[ix1], indices[ix2] = indices[ix2], indices[ix1] def search(self, indices: List[int], iterations: int): """ Search for a solution, using the given indices as initial solution. """ best_indices = indices[:] - for _ in range(iterations): + last_improvement_iteration = 0 + for i in range(iterations): # Reorder the indices - self.attempt_bottleneck_fix(indices) + self.attempt_bottleneck_fix(indices, i - last_improvement_iteration) # Allocate the reordered indices and check if it gave an improvement new_size = self.allocate_indices(indices) if new_size <= self.best_size: # The new allocation produced a new best result remember it + if new_size < self.best_size: + last_improvement_iteration = i self.best_size = new_size best_indices = indices[:] self.allocated_addresses = [lr.address for lr in self.lrs] if self.best_size <= self.min_required_size: - # Target reached stop + # Target reached; stop return else: # The new allocation produced worse result undo the change |