Question Job is slower than Main Thread

Hi everyone!

I've recently started learning and experimenting with the Unity job system and i was trying to implement the A* Pathfinding using it.

I was thinking that using multithreading would greatly improve the performance by splitting paths calculations amoung different threads but when i tested it i noticed that i wasn't getting any performance boost.


That's the code i'm using to test it(it's called in the update function on a MonoBehavior):

Code (CSharp):

1. private void Pathfind(bool multithread)
2.         {
3.             PathfindingNode start = new PathfindingNode((Vector2)t_start.transform.position, grid);
4.             PathfindingNode end = new PathfindingNode((Vector2)t_end.transform.position, grid);
5.             NativeArray<JobHandle> handlers = new NativeArray<JobHandle>(loops, Allocator.TempJob);
6.             AStar[] jobs = new AStar[loops];
7.             float time = Time.realtimeSinceStartup;
8.             if (multithread)
9.             {
10.                 for (int i = 0; i < loops; i++)
11.                 {
12.                     AStar aStar = new AStar(start, end, navPoints, grid);
13.                     jobs[i] = aStar;
14.                     handlers[i] = aStar.Schedule();
15.                 }
16.                 JobHandle.CompleteAll(handlers);
17.             }
18.             else
19.             {
20.                 AStar aStar = new AStar(start, end, navPoints, grid);
21.                 for (int i = 0; i < loops; i++)
22.                     aStar.Run();
23.             }
24.             Debug.Log(Time.realtimeSinceStartup - time);
25.             handlers.Dispose();
26.  
27.         }

and here's the job(note that it's not been optimized, it's a raw implementation for now):

Code (CSharp):

1. public struct AStar : IJob
2.     {
3.         [ReadOnly] public NativeArray<float2> navPoints;
4.         //public NativeList<float2> path;
5.        
6.         private PathfindingNode start;
7.         private PathfindingNode end;
8.         private CustomGrid grid;
9.  
10.         public AStar(PathfindingNode start, PathfindingNode end, NativeArray<float2> navPoints, CustomGrid grid)
11.         {
12.             this.start = start;
13.             this.end = end;
14.             this.navPoints = navPoints;
15.             this.grid = grid;
16.             //path = new NativeList<float2>(Allocator.TempJob);
17.         }
18.        
19.         public void Execute()
20.         {
21.             FindPath(start, end);
22.         }
23.  
24.         ///<summary>
25.         /// Find the path between start point and end point.
26.         ///</summary>
27.         public void FindPath(PathfindingNode start, PathfindingNode end)
28.         {
29.             // Compute distance from start node and target node.
30.             PathfindingNode current = start;
31.             current.g = 0;
32.             current.h = start.GetDistance(end);
33.             // Initialize search list.
34.             NativeList<PathfindingNode> toSearch = new NativeList<PathfindingNode>(Allocator.Temp) {current};
35.             NativeArray<PathfindingNode> processed = new NativeArray<PathfindingNode>(grid.width * grid.height, Allocator.Temp);
36.  
37.             while (toSearch.Length > 0)
38.             {
39.                 current = toSearch[0];
40.                 // Choose best node.
41.                 foreach (PathfindingNode node in toSearch)
42.                     if (node.f < current.f || (node.f == current.f && node.h < current.h))
43.                         current = node;
44.                
45.                 // If we've reached the end goal trace back and build path,
46.                 // otherwise keep adding processed path
47.                 if (current.Equals(end))
48.                 {
49.                     // Trace back the path
50.                     CalculatePath(current, processed);
51.                     break;
52.                 }
53.  
54.                 for(int i = 0; i< toSearch.Length; i++)
55.                     if(toSearch[i].index == current.index)
56.                     {
57.                         toSearch.RemoveAtSwapBack(i);
58.                         break;
59.                     }
60.                 processed[current.index] = current;
61.                
62.                 NativeArray<PathfindingNode> neighbours = current.GetNeighbors();
63.                 for (int i = 0; i < neighbours.Length; i++)
64.                 {
65.                     PathfindingNode neighbour = neighbours[i];
66.                     bool inSearch = toSearch.AsArray().Contains(neighbour);
67.                     // Check if this neighbour node has already been processed, if yes discard it,
68.                     // otherwise keep computing it.
69.                     if (!processed.Contains(neighbour) && navPoints.Contains(neighbour.nodeCell.center))
70.                     {
71.                         if (!inSearch)
72.                         {
73.                             neighbour.connection = current.index;
74.                             neighbour.g = current.g + current.GetDistance(neighbour);
75.                             neighbour.CalculateH(end);
76.                             toSearch.Add(neighbour);
77.                         }
78.                     }
79.                 }
80.             }
81.         }
82.  
83.         // Trace back calculated path and build pathfinding result.
84.         public void CalculatePath(PathfindingNode start, NativeArray<PathfindingNode> pathList)
85.         {
86.             int iter = 0;
87.             PathfindingNode currentPathNode = start;
88.             NativeList<float2> path = new NativeList<float2>(Allocator.Temp);
89.             while (currentPathNode.connection != -1 && iter < pathList.Length)
90.             {
91.                 PathfindingNode cameFrom = pathList[currentPathNode.connection];
92.                 path.Add(cameFrom.nodeCell.center);
93.                 currentPathNode = cameFrom;
94.                 iter++;
95.             }
96.             pathList.Dispose();
97.             path.Dispose();
98.         }
99.     }

I'm still new to the job system and i honestly don't know what i might be doing wrong/breaking, so i would really appreciate if you let me know.

Thanks.

 

Pretty common actually. It's still the same CPU running the code, and since everything (generally) has to be marshaled back through a main thread hook (if it interacts with the Unity API at all), multithreading only gives benefits in the narrowest of specific cases, and even those cases often require intention re-engineering to truly realize full benefit.

 

Yes it would be great, thanks!

 

First off, it isn't obvious to me skimming through the code how running in parallel is actually parallelizing the calculations. It looks like each job is calculating the same path over and over again. For A*, stick to a single-threaded job per agent and run agents in parallel.

Second, Burst does so much more than vectorization. Vectorization can only give you at max a 4 or 8 times speedup, usually less. Yet Burst will frequently make code an order of magnitude faster or more. Make sure you add that BurstCompile attribute.

Also, there's nothing wrong with using Allocator.Temp inside a job, especially if you are going to be using that memory for a lot of work (Allocating temporary lists and hashmaps for an entire A* solve is an excellent use case).

If you want really good performance, focus on a single agent and a single threaded job and try to optimize that as much as possible. If you really want to parallelize for a single agent, you will likely be better served by a different technique such as a flow graph instead.

 

Thank you so much to everyone which tried to help me.

By following your advices i learned a lot and was able to massively improve performance formy A* Pathfinding algorithm.
One important note is that even though i knew following a data-oriented approach was more efficient the results quite shocked me, i guess sometimes you just have to try yourself.

Also Burst is EXTREMELY good, shocked about that.

 

would you share your updated code as learning for us how you made it faster?