Threading

By default, all code is executed on only a single processor core. However, modern processors can contain many cores that can run code simultaneously, and all this extra processing power provided by these other cores would go unused. To take advantage of these extra cores and improve a program’s performance, a program’s flow needs to be restructured into multiple separate threads of execution.

One reason to use multiple threads is to improve an application’s performance by using the extra processing power provided by the other cores. Another is to perform long-running computations or I/O operations in the background while the foreground thread continues to do the normal frame-to-frame rendering functions without slowing down.

Panda3D provides various ways to use threading, both implicitly (by telling it to run some things in separate threads) as well as explicitly, by offering ways to create your own threads for running your own code. Panda3D is compiled by default to use “true” threading, which makes it safe to use Python threading interfaces (or any other threading library) in conjunction with or in lieu of Panda’s own built-in threading interfaces described below.

If you want to test whether threading is enabled in your build of panda, use the following program:

from panda3d.core import Thread
print(Thread.isThreadingSupported())

If threading is enabled, it’s also possible to turn it off, for example if you want to test if a certain problem you are experiencing is related to threading. Put this in your Config.prc:

support-threads #f

Multi-threaded Render Pipeline

Panda’s own rendering pipeline is divided up into multiple stages: App, Cull, and Draw. These stages can be executed concurrently on separate threads, which makes it possible for the next frame to start its computations before the previous frame has finished being drawn to the screen. In the best case, this means theoretically improving performance by 3x!

See the page Multithreaded Render Pipeline for more information.

Asynchronous Operations

Panda3D provides several useful high-level functions for loading models and doing other expensive operations in a thread, so the user of your application will not notice chugs in the frame rate. These functions are managed by Panda3D automatically, so do not require knowledge of threading. It is recommended that you first seek out these features before implementing threading yourself.

See Asynchronous Loading for more information about these features.

Task Chains

Task chains provide a high-level interface for automatically running tasks on separate threads. See the Task Chains page for more information.

Threading

If you want to use threading with Panda3D, it’s not recommended to use Python’s built-in threading modules, since you will most likely run into issues (for Panda3D is written in C++ and thus does not use the Python threading modules). However, Panda3D offers a threading implementation that is safe to use, by reimplementing Python’s “thread” and “threading” modules, these work the same as the Python built-in threading modules but are actually safe to use with Panda3D.

You can get access to Panda3D’s implementation of Python’s thread module by importing the thread module from direct.stdpy:

# WRONG:
import thread
# RIGHT:
from direct.stdpy import thread

For the Python module threading, Panda3D offers two equivalents, threading and threading2, which you can find both in direct.stdpy also. The threading module implements the threading module with a thin layer over Panda’s threading constructs. As such, the semantics are close to, but not precisely, the semantics documented for Python’s standard threading module. If you really do require strict adherence to Python’s semantics, see the threading2 module instead.

In fact, the threading2 module is a bald-face copy of Python’s threading module from Python 2.5, with a few lines at the top to import Panda’s thread reimplementation instead of the system thread module, and so it is therefore layered on top of Panda’s thread implementation.

However, if you don’t need such strict adherence to Python’s original semantics, the “threading” module is probably a better choice. It is likely to be slightly faster than the threading2 module (and even slightly faster than Python’s own threading module). It is also better integrated with Panda’s threads, so that Panda’s thread debug mechanisms will be easier to use and understand.

# WRONG:
import threading
# RIGHT:
from direct.stdpy import threading
# ALSO RIGHT:
from direct.stdpy import threading2 as threading

It is permissible to mix-and-match both threading and threading2 within the same application.

File I/O

Panda3D also offers a thread-safe replacement for the Python file module. You can find it in direct.stdpy.file. The interface is exactly the same as Python’s, so it’s safe to put this import above all the files where you want to use the open() function:

from direct.stdpy.file import *

This module reimplements Python’s file I/O mechanisms using Panda constructs. This enables Python to interface more easily with Panda’s virtual file system, and it also better-supports Panda’s SIMPLE_THREADS model, by avoiding blocking all threads while waiting for I/O to complete.

Compiling Panda3D with threading support

There are two different interfaces for threading which you can enable using the definitions HAVE_THREADS and SIMPLE_THREADS. The former is a full and heavy implementation of threading and compiling with that option will slow down the Panda3D build, unless you fully make use of the benefits that threading gives. The latter, however, is a more simple threading interface that doesn’t give you the runtime overhead HAVE_THREADS gives you.

Note that you will have to define both HAVE_THREADS and SIMPLE_THREADS at the same time to enable the simple interface, or you will not have threading.

The public builds enable true threading by default, so you will not need to build Panda3D yourself if you want to take advantage of true threading.

If you wish to disable threading, you can pass the option --override HAVE_THREADS=UNDEF to makepanda.py. If you wish to use the simple threading model, you may pass --override SIMPLE_THREADS=1 instead.

Debugging Threading Bugs

Due to the nature of threading-related bugs, it can be very difficult to track down the source of a problem if a piece of code is crashing due to a threading problem. It may be useful to recompile Panda3D with the DEBUG_THREADS=1 option enabled. This will enable various debug checking tools that will alert you of incorrect use of threading, rather than crashing. However, there is a significant performance cost associated with this option.