英文:
Pymunk multi-core collision resolution?
问题
我有一个非常基本的空间设置，其中有大量（10000）个受重力影响的圆在下落。似乎只有一个核心在任何时候都处于活动状态，但据我所知，通过空间分割实现的碰撞解决是非常可并行化的，而 Chipmunk 引擎确实实现了这一点。
即使我在 Linux 上使用 threaded=True 和 space.use_spatial_hash() 创建空间，问题仍然存在。我真的需要并行线程提供的额外性能，我有哪些选择？我应该转而使用带有平面约束的 pybullet 来模拟2D吗？
英文:
I have a very basic space set up with a large number (10000) of circles falling under gravity. It seems that only one of my cores is active at all at any point in time, but as far as I know collision resolution is extremely parallelizable with space partitioning, which the Chipmunk engine does indeed implement.
It seems that even when I create the space with threaded=True on Linux, and space.use_spatial_hash(), the issue persists. I really need the extra performance parallel threads could provide, what are my options? Should I move to pybullet instead with planar constraints to emulate 2d?
答案1
得分: 1
你需要设置要使用的线程数，例如像这样：your_space.threads = 2
在文档中写得像这样（Space.init）：
如果你设置了threaded=True，step函数将在多线程模式下运行，这可能会提高速度。请注意，即使你设置了threaded=True，你仍然需要设置Space.threads=2才能真正使用多个线程。
你可以通过运行threaded_space示例来验证它是否有效，命令是 python -m pymunk.examples.threaded_space。如果我这样做，并在htop中观察CPU使用情况，我可以轻松地看到线程从1个变为2个。
我还应该补充说，不幸的是，在大多数情况下，多线程并不能显著提高性能。只有在同时发生大量碰撞时才能提供一些帮助，但即使在这种情况下，也没有太大改善（也许在ARM上会有更大的改善，但我无法尝试）。
英文:
You need to set the number of thread to use as well, for example like your_space.threads = 2
In the docs its written like this (Space.init):
> If you set threaded=True the step function will run in threaded mode which might give a speedup. Note that even when you set threaded=True you still have to set Space.threads=2 to actually use more than one thread.
You can verify that it works by running the threaded_space example python -m pymunk.examples.threaded_space. If I do that and watch CPU usage in htop I can easily see when it goes from 1 to 2 threads.
I should also add that unfortunately the multi-threading does not improve performance much in most cases. It can only help if there's a lot of collisions going on at the same time, but even then its not much (Maybe on ARM it gives bigger improvement, but that I cannot try).
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