Galry’s Story, or the Quest of Multi-Million Plots

About a month ago, I announced here the availability of a new experimental high performance visualization package in Python that I’m developing as part of my current research project. It has significantly evolved since then, but it is still experimental. Moreover, the interface is still not ready for a 0.1 release. I also need to do much more tests on various systems and graphics cards. In this post I’ll talk about how the idea of writing a new visualization package came up in the first place. I’ll also describe the new features that are coming to the library.

After my announcement, I was pleased to see that there were a lot of people interested in this project. There were more than 500 unique visits since then, which is not that impressive but still much more than what I’d have thought! That’s probably because I wasn’t the only one to note that it was simply not possible to plot huge datasets in Python. Matplotlib, probably the most popular plotting library in Python, crashes before displaying a multi-million dataset (at least that’s what I could experience on my machine), or when it works, the navigation is severly limited by an extremely low framerate.

All other plotting libraries I could find had the same issue. The Matlab plotting library appears to be a bit more efficient than matplotlib when it comes to multi-millions datasets, and it may be one of the reasons why many people still prefer to use Matlab rather than Python.

I think many people are doing just fine with matplotlib because they simply don’t work with very large datasets. But that may be going to change, with “big data” becoming a more and more popular buzz word. In bioinformatics, the mass of data becoming available is simply crazy. There’s the whole field of bioimaging of course, but even apparently harmless time-dependent signals can become quite large. Imagine, for example, a neurophysiological recording with an extracellular multi-electrode array with 250 channels, each channel sampling a signal at 16 bits and 20 kHz (this is close to a real example). That’s 10 MB of data per second (5 million points), more than 30 GB per hour (18 billion points) ! A modern hard drive can store that, but processing such a big file is simply not straightforward: it even doesn’t fit in system memory (at least on most today’s computers), and even less in graphics memory. Yet, is it too much to ask to just plot these data?

The typical way of processing this is to take chunks of data, either in space or in time. But when it comes to visualization, it’s hardly possible to plot even a single second across all channels, since that’s already 5 million points!

One could argue that a modern screen does not contain much more than 2 million pixels, and about 2000 only horizontally. But the whole point of interactive navigation (zooming and panning) is to be able to plot the whole signal at first, and zoom-in in real time on regions of interest.

I could not find any Python library that would allow me to do that. Outside Python, I am not aware of such a software either. That’s precisely why I decided to try a new approach, which is to use the graphics card for the whole rendering process in the most efficient possible way. I realized that the only way I could achieve the highest performance possible on a given piece of hardware was to go as low-level as I could with Python. Using a great and light Python wrapper around OpenGL (not unexpectingly called PyOpenGL) seemed like a natural choice. Initial proof-of-concept experiments with PyOpenGL suggested that it appeared to be like a viable method.

That’s how Galry was born earlier this year.

Here come shaders

The library has evolved a lot since then. I had to go through multiple improvements and refactoring sessions as I was using Galry for my research project. In addition, I also had to learn OpenGL in parallel. That was not an excellent idea, since I realized several times that I was doing it wrong. In particular, I was using at first a totally obsolete way of rendering points, which was to use the fixed function pipeline. When I discovered that the modern way of using OpenGL was to use customizable shaders , I had to go through a consequent rewriting of the whole rendering engine. I could have spared me this rewriting if I was aware of that point beforehand.

But it was in the end a very good decision, since programmable shaders are just infinitely more powerful than the fixed function pipeline, and make a whole new bunch of things possible with the package. Not only was I able to considerably improve the rendering part in my research project, but I realized that the same code could be used to do much more than just plotting. Here are a few examples of what I was able to do with the new “shader-aware” interface: GPU-based image filtering, GPU-based particle system, GPU-based fractal viewer, 3D rendering, dynamic graph rendering (CPU-based for now), etc. These are all actual working examples in the Galry repository. I suppose this package could also be used to write small video games!

The following video shows a demo of the graph example. This example incorporates many of the rendering techniques currently implemented in Galry: point sprites (a single texture attached to many points), lines, buffer references (the nodes and edges are rendered using the exact same memory buffer on the GPU, which contains the node positions), indexed rendering (the edges are rendered using indices targetting the corresponding nodes, always stored in the same buffer), real-time buffer updating (the positions are updated on the CPU and transferred on the GPU at every frame). GPU-based rendering may also be possible but it’s not straightforward, since the shaders need to access the other shaders’ information, and also modify dynamically the position. I might investigate this some time. Another solution is to use OpenCL, but it requires to have an OpenCL installation (it can work even if an OpenCL-capable GPU is not available, in which case the OpenCL kernels are executed on the CPU).

[youtube]http://www.youtube.com/watch?v=MLW3i-_yQ-k[/youtube]

About OpenGL

Another thing I discovered a bit late was that OpenGL is a fragmented library: there are multiple versions, a lot of different extensions, some being specific to a hardware vendor, and a lot of deprecated features. There’s also a specific version of OpenGL for mobile platforms (such as the IPhone and the IPad), called OpenGL ES, which is based on OpenGL but which is still different. In particular, a lot of deprecated features in OpenGL are simply unavailable in OpenGL ES. Also, the shading language (GLSL) is not the same between OpenGL and OpenGL ES. There’s a loose correspondence between the two but the version numbers do not match at all. And, by the way, the GLSL language version does not match the corresponding OpenGL version… except for later versions! Really confusing.

The OpenGL ES story is important for Galry, because apparently OpenGL ES is sometimes used in VirtualBox for hardware-acceleration, and it might also be useful in the future for a potential mobile version of Galry. In addition, OpenGL ES also forms the basis of WebGL, enabling access to OpenGL in the browser. I’ll talk about that below, but the point is that in order to have compatibility between multiple versions of OpenGL, I had to redesign again an important part of the rendering process (by using a small template system for dynamic shader code generation depending on the GLSL version).

Also, whereas the shading language is quite nice and easy to use, I find the host OpenGL API unintuitive and sometimes obscure. The Galry programming interface is right there to hide those details to the developer.

In brief, I find certain aspects of OpenGL a bit messy, but the advantages and the power of the library are definitely worth it.

About writing multi-platform software

Python is great for multi-platform software. Choosing Python for a new project means that one has the best chance of having a single code base for all operating systems out there. In theory, that’s the same story for OpenGL, since it’s a widely used open standard. In practice, it’s much more difficult due to the fragmentation of the OpenGL versions and drivers across different systems and graphics card manufacturers. Writing a multi-platform system means that all supported systems need to be tested, and that’s not particularly easy to do in practice: there are a large number of combinations of systems (Windows, different Linux distributions, Mac OSX, either 32 bits and 64 bits), of graphics card drivers, versions of Python/PyOpenGL/QT, etc.

High-level interface

In the current experimental version of Galry, the low-level API is the only interface I’ve been working on, since it’s really what I need for my project. However, I do plan to write a basic matplotlib-like high-level interface in the near future. At some point, I even considered integrating Galry’s code into a matplotlib GL backend, which is apparently something that several people have been trying to do for quite some time. However, as far as I understand, this is very much non-trivial due to the internal architecture of matplotlib. The backend handles the rendering process and is asked to redraw everything at each frame during interactive navigation. However, high performance is achieved in Galry by loading data at initialization time only, and updating the transformation at every frame so that the GPU can apply it on the data. The backend does not appear to have access to that transformation, so I can’t see how an efficient GL renderer could be written in the current architecture. But I’m pretty sure that somebody will manage to make that happen eventually.

In the meantime, I will probably write a high-level interface from scratch, without using matplotlib at all. The goal is to replace import matplotlib.pyplot as plt by something like import galry.plot as plt at the top of a script to use Galry instead of matplotlib. At first, I’ll probably only implement the most common functions such as plot, imshow, etc. That would already be very useful.

Galry in the browser

Fernando Perez, the author of IPython, suggested to integrate Galry in the IPython notebook. The notebook is a relatively new feature that allows to write (I)Python code in cells within an HTML page, and output the result below. That’s quite similar to what Mathematica or Maple offer. The whole interactive session can then be saved as a JSON file. It brings reproducibility and coherent structure in interactive computing. Headers, text, and even static images with matplotlib can be integrated in a notebook. Blog posts, courses, even technical books are being written with this.

I personally heard about the notebook some time ago, but I’d never tried it because I was a bit reluctant to use Python in a browser instead of a console. After Fernando’s suggestion, I tried to use the notebook and I quickly understood why so many people are very enthusiastic about it. It’s because it changes the very way we do exploratory research with numerical experiments. In a classical workflow, one would use a Python script to write some computational process, and use the interactive console to execute it, explore the model in the parameter space, etc. It works, but it can be terrible for reproducibility: there’s no way one can recover the exact set of parameters and code that corresponds to figure test34_old_newnewbis.png. Many people are dealing with this problem, me included. I’m quite ashamed by the file structure of most of my past research projects’ code, and I’ll try to use the notebook in the future to try being more organized than before.

The idea of integrating Galry in the notebook comes from the work that has been done during a Python conference earlier this month, with the integration of a 3D molecular visualization toolkit in the notebook using WebGL. WebGL is a standard specification derived from OpenGL that aims at bringing OpenGL rendering to the browser, through the HTML5 <canvas> element. It is still an ongoing project that may still take months or years to complete. Currently, it is only supported by the latest versions of modern browsers such as Chrome or Firefox (no IE of course). But it’s an exciting technology that has a huge potential.

So I thought it would be quite a good idea and I gave it a try: I managed to implement a proof-of-concept in only one day, by looking at the code that had been done during the conference.

[youtube]http://www.youtube.com/watch?v=taN4TobRS-E[/youtube]

Then, I was able to see what would need to be done in the code’s architecture to make that integration smooth and flexible. The solution I chose was to separate completely the scene definition (what to plot exactly, with all parameters, data, colors, shading code, etc.) with the actual GL rendering code. The scene can be serialized in JSON and transmitted to a Javascript/WebGL renderer. I had to rewrite a portion of the Python renderer in Javascript, which turned out to be less painful than what I thought.

Finally, the WebGL renderer can in fact be used as a completely standalone Javascript library, without any reference to Python. This may allow interesting scenarios, such as the conversion of a plotting script in Python using a matplotlib-like high-level interface, into standalone HTML/Javascript code that enables interactive visualization of that plot in the browser.

About performance

The major objective of Galry is, by far, performance. I found that PyOpenGL can be very fast at the important condition of using it correctly. In particular, data transfer from system memory to graphics memory should be made only when necessary. Also, the number of calls to OpenGL commands should be minimal in the rendering phase.

The first point means that data should be uploaded on the GPU at initialization time, and should stay on the GPU as long as possible. When zooming in, the GPU should handle the whole transformation on the same memory buffer. This ensures that the GPU is used optimally. In Matplotlib, as far as I know, everything is rendered again at each frame, which explains why the performance is not very good. And the CPU does the rendering in this case, not the GPU.

The second point is also crucial. When plotting a large number of individual points, or a single curve, it is possible to call a single OpenGL rendering command, so that the Python overhead is negligible compared to the actual GPU rendering phase. But when it comes to a plot containing a large number of individual curves, using a Python loop is highly inefficient, especially when every call renders a small curve. The best solution I could find is to use glMultiDrawArrays or glMultiDrawElements, which render several primitives with a single memory buffer and a single command. Even if this function is implemented internally as a loop by the driver, it will still be much faster than a Python loop, since there isn’t the cost of interpretation.

With this technique, I am able to plot 100 curves with 1 million points each at ~15 FPS with a recent graphics card. That’s 1.5 billion points per second! Such performance is directly related to the incredible power of modern GPUs, which is literally mind blowing.

Yet, I think there’s still some room for improvement by using dynamic undersampling techniques. But that is for the future…

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