gEvas: the GTK+2 to Evas Bridge
The Evas library provides a canvas for quickly rendering raster graphics with alpha blending support. Evas is part of the Enlightenment Foundation Libraries (EFL), a suite of libraries originally built to support Enlightenment DR 17. Other libraries in the EFL that complement Evas are the Edje and Embryo combination and the Emotion library. Edje allows you to wrap up fonts, graphics and functionality into portable theme-like files. Embryo is a simple yet Turing complete scripting language that enables simple scripts to be embedded into Edje files. Emotion allows you to have many video streams as first-class canvas objects. This means you can alpha blend video, move video objects around, resize them and layer them in the canvas while playing.
gEvas is a wrapper and glue library built to allow Evas to be used from GTK+2.x applications easily. In order to motivate you to try gEvas, I present here the points that originally motivated me to use Evas and subsequently create gEvas. My major motivations to use Evas were its simple API and great render speed for an alpha-blended canvas.
Making claims of high render speed unfortunately requires a brief trip into the benchmarking world. The evas_bench application from the Evas distribution involves many pixmap canvas items, scaling and blending of pixmap items and text elements. The image scaling is nowhere near as extreme as the above resize benchmarks. I have ported evas_bench to using GNOME Canvas.
Figure 1 shows a screenshot of the GNOME Canvas port of evas_bench. I have also created some simpler tests for the canvas scaling algorithm both with and without forcing alpha blends on each frame. For the non-alpha-blended version, a leaf image is resized from larger than full-canvas width to 0 by 0 and back again in a loop; see Figure 2. For the alpha-blended version, a red rectangle image of the same size as the leaf image was used ranging from 0 alpha in the top left to full alpha in the bottom.
As stated above, I created gEvas, and some readers may notice that I am a part of the Enlightenment developers team. Although this is true, I have gone to lengths to ensure that the benchmarks are not biased. The benchmark source code is available (see the on-line Resources). Those reading the source, please excuse the excessive use of less-than-optimal coding conventions used in the quick code hack.
For those unfamiliar with GNOME Canvas, it has two rendering back ends. From the GNOME Canvas developer documentation (see Resources): “...It [GNOME Canvas] offers a choice of two rendering back ends, one based on Xlib for extremely fast display, and another based on Libart, a sophisticated, anti-aliased, alpha-compositing engine.” Evas attempts to provide the best of both worlds. I benchmark Evas against both GNOME Canvas rendering engines.
There are also reasons why Evas might not be an appropriate choice. GNOME Canvas supports a Bezier path canvas item that, at present, Evas does not support. In addition, Evas and gEvas are less likely than GNOME Canvas to be preinstalled.
Evas may get beziers in the future. The summary of this point from Raster (aka Carsten Haitzler) was: “If you are after a vector editor suite or something, GNOME Canvas is better. If you want real time fast, alpha-blended object display on all targets, Evas is a good choice.”
Evas itself supports multiple back-end render targets, including the framebuffer, XLib and OpenGL. At present, gEvas uses only the XLib Evas back end. However, as GTK+2 can run on the framebuffer, you should be able to use gEvas on the framebuffer too, but this is untested.
Both GNOME Canvas and Evas share a similar data model. The Qt QCanvas data model is different enough to make clean benchmark comparisons difficult. The first major difference that makes comparisons difficult is how QCanvas handles images. To put an image on the canvas, you create a QCanvasSprite with a single frame. To scale that image, you then use QImage::scale() or QImage::smoothScale(), which gives you back an image that you can use to update your sprite. This puts image scaling and the cache handling for scaled images into the client application. Both Evas and GNOME Canvas allow canvas objects to be resized directly and thus take responsibility for handling the caching of scaled images for you.
The second difference is that Qt gives you control over the update tile size. The Qt documentation recommends: “A good rule of thumb is that the size should be a bit smaller than the average canvas item size. If you have moving objects, the chunk size should be a bit smaller than the average size of the moving items.”
Due to the data model differences, I haven't created a Qt port of evas_bench as yet. I did create a canvas scaling and blending client, although there are some canvas design issues affecting a clean comparison.
With Qt moving the cache policy fully in the client, I have chosen to cache all scaled images in the first iteration of scaling and reset the benchmark start timer for further iterations that will use only the cache.
So keep in mind that the Qt resize benchmark is performed with all resized images pre-cached and many user-specified tile sizes are used for benchmarking. This effectively should give QCanvas a huge speed advantage over Evas and GNOME Canvas.
Practical Task Scheduling Deployment
July 20, 2016 12:00 pm CDT
One of the best things about the UNIX environment (aside from being stable and efficient) is the vast array of software tools available to help you do your job. Traditionally, a UNIX tool does only one thing, but does that one thing very well. For example, grep is very easy to use and can search vast amounts of data quickly. The find tool can find a particular file or files based on all kinds of criteria. It's pretty easy to string these tools together to build even more powerful tools, such as a tool that finds all of the .log files in the /home directory and searches each one for a particular entry. This erector-set mentality allows UNIX system administrators to seem to always have the right tool for the job.
Cron traditionally has been considered another such a tool for job scheduling, but is it enough? This webinar considers that very question. The first part builds on a previous Geek Guide, Beyond Cron, and briefly describes how to know when it might be time to consider upgrading your job scheduling infrastructure. The second part presents an actual planning and implementation framework.
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With all the industry talk about the benefits of Linux on Power and all the performance advantages offered by its open architecture, you may be considering a move in that direction. If you are thinking about analytics, big data and cloud computing, you would be right to evaluate Power. The idea of using commodity x86 hardware and replacing it every three years is an outdated cost model. It doesn’t consider the total cost of ownership, and it doesn’t consider the advantage of real processing power, high-availability and multithreading like a demon.
This ebook takes a look at some of the practical applications of the Linux on Power platform and ways you might bring all the performance power of this open architecture to bear for your organization. There are no smoke and mirrors here—just hard, cold, empirical evidence provided by independent sources. I also consider some innovative ways Linux on Power will be used in the future.Get the Guide