/// <summary>
/// Pops the lowest FCost node index from the open set.
/// </summary>
/// <param name="openSet"></param>
/// <param name="nodesInfo"></param>
/// <returns></returns>
private int PopLowestFCostNodeIndexFromOpenSet(NativeList <int> openSet, NativeArray <NodePathFindInfo> nodesInfo)
{
int foundAtIndex = -1;
int lowestIndex = -1;
int lowestFCostHCost = int.MaxValue;
int lowestFCostVal = int.MaxValue;
for (int i = 0; i < openSet.Length; ++i)
{
int currNodeIndex = openSet[i];
NodePathFindInfo info = nodesInfo[currNodeIndex];
if (info.FCost < lowestFCostVal || info.FCost == lowestFCostVal && info.hCost < lowestFCostHCost)
{
foundAtIndex = i;
lowestIndex = currNodeIndex;
lowestFCostHCost = info.hCost;
lowestFCostVal = info.FCost;
}
}
// I have tested RemoveAt vs RemoveAtSwapBack, and oddly enough there seems to be a
// extremely slightly difference in favour of RemoveAt, but perhaps this is due to
// the non-totally-deterministic stress test... I'm still surprised there is not a
// notable difference in favour of RemoveAtSwapBack though.
openSet.RemoveAtSwapBack(foundAtIndex);
return(lowestIndex);
}
public void Execute(int index)
{
int globalIndex = jobIndexStride + index;
int startNodeIndex = startIndices[globalIndex];
int endNodeIndex = endIndices[globalIndex];
// Note: Temp allocator can fall back to a very much slower version if the block of
// memory that it uses is exhausted. By the looks of the tests that I have done, it
// seems that this memory is released after the job is finished. I had this
// originally in an IJobParallelFor and there were threads that introduced
// significant bottlenecks due to this issue (the inner loop batch count didn't
// matter), but after switching to a "batch of IJobs" this issue is gone, as the
// maximum jobs that can run at the same time is just the number of logical threads
// of the system, which shouldn't be more than ~32 in a high end system (although
// some threadrippers can go up to 128, which eventually may be an issue again). I
// have tested this in a complete flat map with no obstacles, and the time to
// complete each job is reasonably similar.
// Update: I have been exhaustively testing the new approach with thousands of IJobs
// and simple paths. This approach is certainly much better than the one mentioned
// above, but it has a considerably big issue: when there's tons of simple paths,
// the dependencies become too complex and the main thread struggles more and more
// as the number of paths increases, since the workers push hard to finish all the
// tasks, but the main thread has to check every single jobhandle when calling
// CompleteAll or combining dependencies (do note that this is speculation based on
// my observations in the profiler though).
// Update2: I have ended using IJobFor with very low iterations (8 seem to work fine
// enough). I am now in a middle ground between 1. and 2. mentioned above. I think
// it is a decent spot but with lots of paths and very low work, I am seeing that
// some paths take extremely long time to be computed. At this point I think the
// only way to get it better is going into micro-optimization and some kind of
// hierarchiccal pathfinding, There are obviously other ways of computing
// pathfinding for large crowds such as flow fields, but I'm currently only
// interested in A*. If memory is really the problem, hierarchiccal pathfinding
// should greatly increase performance as constraining the searchs to i.e a 16x16
// grid (so we could use bytes for gCost and hCost and sub-indices). The open and
// closed set would also be extremely constrained and it may also be possible to use
// fixedlist for some of the info, which may give huge speedups.
// I have tried swapping this by just a plain int3 and surprisingly, it is
// substantially slower.
NativeArray <NodePathFindInfo> nodesInfo = new NativeArray <NodePathFindInfo>(numNodes, Allocator.Temp);
NativeBitArray closedSet = new NativeBitArray(numNodes, Allocator.Temp);
NativeBitArray openSetContains = new NativeBitArray(numNodes, Allocator.Temp);
// Warning: 272 is a magical number due to the map layout and possible paths, which
// seems to not throw errors about being too low for the test scene. This list
// should be somewhat constrained to a low range in order to keep the algorithm
// performant, otherwise it would be worth to look at implementing a native binary
// heap to speed up the extaction of the lowest fcost node.
NativeList <int> openSet = new NativeList <int>(272, Allocator.Temp);
// set the info for the first node
nodesInfo[startNodeIndex] = new NodePathFindInfo(0, GetHeuristic(startNodeIndex, endNodeIndex), -1);
openSet.AddNoResize(startNodeIndex);
while (openSet.Length > 0)
{
int currNodeIndex = PopLowestFCostNodeIndexFromOpenSet(openSet, nodesInfo);
// we've reached the goal
if (currNodeIndex == endNodeIndex)
{
SaveNextNodeIndexToMoveTo(globalIndex, nodesInfo);
return;
}
// add it to the closed set by setting a flag at its index
closedSet.SetBits(currNodeIndex, true, 1);
NodePathFindInfo currNodeInfo = nodesInfo[currNodeIndex];
// go over the neighbors
int start = currNodeIndex * numNeighbors;
int end = start + numNeighbors;
for (int i = start; i < end; ++i)
{
int neighborIndex = nodesNeighbors[i].neighborIndex;
// if it does not have neighbor, was already expanded or can't be walked by
if (!nodesNeighbors[i].isValid || closedSet.IsSet(neighborIndex) || (byte)nodesTypes[neighborIndex] > 0)
{
continue;
}
NodePathFindInfo neighborNodeInfo = nodesInfo[neighborIndex];
int newGCost = currNodeInfo.gCost + GetHeuristic(currNodeIndex, neighborIndex);
// not in open set
if (!openSetContains.IsSet(neighborIndex))
{
// update parent, costs, and add to the open set
neighborNodeInfo.gCost = newGCost;
neighborNodeInfo.hCost = GetHeuristic(neighborIndex, endNodeIndex);
neighborNodeInfo.parentNodeIndex = currNodeIndex;
nodesInfo[neighborIndex] = neighborNodeInfo;
openSet.AddNoResize(neighborIndex);
openSetContains.SetBits(neighborIndex, 1);
}
else if (newGCost < neighborNodeInfo.gCost)
{
// update parent, and gCost (hCost is already calculated)
neighborNodeInfo.gCost = newGCost;
neighborNodeInfo.parentNodeIndex = currNodeIndex;
nodesInfo[neighborIndex] = neighborNodeInfo;
}
}
}
// Note: TODO? I think the only way to get here is if the way to a valid end node is
// completely blocked, in which case I should decide what to do
return;
}