Network layout algorithms using IPC¶
Hi all. In the past weeks, I’ve been focusing on developing Helios; the network visualization library for FURY. I improved the visual aspects of the network rendering as well as implemented the most relevant network layout methods.
In this post I will discuss the most challenging task that I faced to implement those new network layout methods and how I solved it.
The problem: network layout algorithm implementations with a blocking behavior¶
Case 1: Suppose that you need to monitor a hashtag and build a social graph. You want to interact with the graph and at the same time get insights about the structure of the user interactions. To get those insights you can perform a node embedding using any kind of network layout algorithm, such as force-directed or minimum distortion embeddings.
Case 2: Suppose that you are modelling a network dynamic such as an epidemic spreading or a Kuramoto model. In some of those network dynamics a node can change the state and the edges related to the node must be deleted. For example, in an epidemic model a node can represent a person who died due to a disease. Consequently, the layout of the network must be recomputed to give better insights.
In described cases if we want a better (UX) and at the same time a more practical and insightful application of Helios layouts algorithms shouldn’t block any kind of computation in the main thread.
In Helios we already have a lib written in C (with a python wrapper) which performs the force-directed layout algorithm using separated threads avoiding the GIL problem and consequently avoiding the blocking. But and the other open-source network layout libs available on the internet? Unfortunately, most of those libs have not been implemented like Helios force-directed methods and consequently, if we want to update the network layout the python interpreter will block the computation and user interaction in your network visualization. How to solve this problem?
Why is using the python threading is not a good solution?¶
One solution to remove the blocking behavior of the network layout libs like PyMDE is to use the threading module from python. However, remember the GIL problem: only one thread can execute python code at once. Therefore, this solution will be unfeasible for networks with more than some hundreds of nodes or even less! Ok, then how to solve it well?
IPC using python¶
As I said in my previous posts I’ve created a streaming system for data visualization for FURY using webrtc. The streaming system is already working and an important piece in this system was implemented using the python SharedMemory from multiprocessing. We can get the same ideas from the streaming system to remove the blocking behavior of the network layout libs.
My solution to have PyMDE and CuGraph-ForceAtlas without blocking was to break the network layout method into two different types of processes: A and B. The list below describes the most important behaviors and responsibilities for each process
Where the visualization (NetworkDraw) will happen
Create the shared memory resources: edges, weights, positions, info..
Check if the process B has updated the shared memory resource which stores the positions using the timestamp stored in the info_buffer
Update the positions inside of NetworkDraw instance
Read the network information stored in the shared memory resources: edges , weights, positions
Execute the network layout algorithm
Update the positions values inside of the shared memory resource
Update the timestamp inside of the shared memory resource
I used the timestamp information to avoid unnecessary updates in the FURY/VTK window instance, which can consume a lot of computational resources.
How have I implemented the code for A and B?¶
Because we need to deal with a lot of different data and share them between different processes I’ve created a set of tools to deal with that, take a look for example in the ShmManagerMultiArrays Object , which makes the memory management less painful.
I’m breaking the layout method into two different processes. Thus I’ve created two abstract objects to deal with any kind of network layout algorithm which must be performed using inter-process-communication (IPC). Those objects are: NetworkLayoutIPCServerCalc ; used by processes of type B and NetworkLayoutIPCRender ; which should be used by processes of type A.
I’ll not bore you with the details of the implementation. But let’s take a look into some important points. As I’ve said saving the timestamp after each step of the network layout algorithm. Take a look into the method _check_and_sync from NetworkLayoutIPCRender here. Notice that the update happens only if the stored timestamp has been changed. Also, look at this line helios/layouts/mde.py#L180, the IPC-PyMDE implementation This line writes a value 1 into the second element of the info_buffer. This value is used to inform the process A that everything worked well. I used that info for example in the tests for the network layout method, see the link helios/tests/test_mde_layouts.py#L43
Until now Helios has three network layout methods implemented: Force Directed , Minimum Distortion Embeddings and Force Atlas 2. Here docs/examples/viz_helios_mde.ipynb you can get a jupyter notebook that I’ve a created showing how to use MDE with IPC in Helios.
In the animation below we can see the result of the Helios-MDE application into a network with a set of anchored nodes.
I’ll probably focus on the Helios network visualization system. Improving the documentation and testing the ForceAtlas2 in a computer with cuda installed. See the list of opened issues
Summary of most important pull-requests:¶
IPC tools for network layout methods (helios issue #7) fury-gl/helios/pull/6
Improved the visual aspects and configurations of the network rendering(helios issue #12) https://github.com/devmessias/helios/tree/fury_network_actors_improvements
Tests, examples and documentation for Helios (helios issues #3 and #4) fury-gl/helios/pull/5
Reduced the flickering effect on the FURY/Helios streaming system fury-gl/helios/pull/10 fury-gl/fury/pull/437/commits/a94e22dbc2854ec87b8c934f6cabdf48931dc279