It's no doubt that HTML5 is going to be next big platform for software development. Some people say it could even kill traditional operating systems and all applications in future will be written with HTML5 and JavaScript. Others say HTML5 apps will have their market share, but never replace native applications completely. One of the main reasons is poor JavaScript performance, they say. But wait, browser vendors say they did lots of optimizations and JavaScript is fast as it was never before! Isn't it true?
Well, simple answer is yes... and no. Modern JavaScript engines such as Google's V8 have impressive performance in case you compare them with their predecessors five-ten years ago. Although, their results are not so impressive if you compare them with statically typed languages such as Java or C#. And of course it will be absolutely unfair competition if we compare JavaScript with native code written with C++.
But how one can determine if their application could be written in JavaScript or should they choose native tools?
Recently we had a chance to make such kind of decision. We were working on proposal for tablet application that should include Paint-like control where user can draw images using standard drawing tools like Pencil and Fill. Target platforms were Android, Windows 8 and iOS, so cross-platform development tools had to be taken into consideration. From the very beginning there was a concern that HTML5 canvas could be too slow for such task. We implemented simple demo application to test canvas performance and prove if it is applicable in that case. Leaping ahead, let us point out that we have mixed fillings about gathered results. On the one hand canvas was fast enough on simple functions like pencil drawing due to native implementation of basic drawing methods. On the other hand, when we implemented classic Flood Fill algorithm using Pixel Manipulation API we found that it is too slow for that class of algorithms. During that research we applied set of performance optimizations to our Flood Fill implementation. We measured their effect on several browsers and want to share them with you.
Initial implementation
Our very first Flood Fill implementation was very simple:
We tested it with 3 desktop browsers running on Core i5 (3.2 GHz) and 3rd generation iPad with iOS 6. We got following results with that implementation:
Surprisingly, IE 10 is even
slower than Safari on iPad. Chrome proved that it is still fastest browser in
the world.
Optimize pixel manipulation
Let's take a look at getPixelColor function:
Code looks little bit ugly, so let's cache result of ((y * (img.width * 4)) + (x * 4)) expression (pixel offset) in variable. Also it makes sense to cache img.data reference into another variable. WE also applied similar optimizations to setPixelColor function:
At least code looks more readable. And what about performance?
Impressive, we got 40-50% performance gain on desktop browsers and about 30% on Safari for iOS. IE 10 now has comparable performance to mobile Safari. It seems that Safari's JavaScript compiler already applied some of optimization we did, so effect was less dramatic for it.
Optimize color comparison
Let's take a look at getPixelColor function again. We mostly use it in if statement to determine if pixel already was filled with new color: getPixelColor(img, cur.x + dx[i], cur.y + dy[i]) != hitColor. As far as you probably know, HTML5 canvas API provide access to individual color components of each pixel. We use this components to get whole color in RGB format, but here we actually don't need to do it. Let's implement special function to compare pixel color with given color:
Here we use standard behavior of || operator: it doesn't execute right part of the expression if left part returns true. This optimization allows us to minimize array reads and arithmetic operations count. Let's take look at its effect:
Almost no effect: 5-6% faster on Chrome and IE and 2-3% slower on FF and Safari. So, problem must be somewhere else. We left this fix in our code because the code is little bit faster in average with it than without.
Temp object for inner loop
As you probably noticed, our code in main flood fill loop looks little bit ugly because of duplicated arithmetic operations:
Let's rewrite it using temp object for new point we work with:
And test effect:
Results are discouraging. It seems that side-effect of such fix is higher garbage collector load and as a result overall slowness of the application. We tried to replace it with two variables for coordinates, defined in outer scope but it didn't help at all. Logical decision is to revert that code, what we actually did.
Visited pixels cache
Let's think again about pixel visiting in Flood Fill algorithm. It is obvious that we should visit each pixel only once. We guarantee such behavior by comparing colors of neighbor pixels with hit pixel color, which must be slow operation. In fact, we can mark pixels as visited and compare colors only if pixel is not visited. Let's do it:
So, what are results? Well, here they go:
Again, absolutely unexpected results: IE 10 is 10% faster with that fix, but other browsers are dramatically slower! Safari is even slower than initial implementation. It is hard to tell what is the main reason of such behavior, but we can suppose that it could be garbage collector. It also makes sense to apply it in case you don't target mobile Safari and want to have maximum performance in worst case (Sorry IE, it is you. As usual).
Conclusions
We tried to make some more optimizations but it didn't help. Worst thing about JavaScript optimizations is that it is hard to predict their effect, mainly because of implementation differences. Remember, there are two basic rules when you optimize JavaScript code:
- benchmark results after each optimization step
- test in each browser you want your application work with
HTML5 is cool, but still much slower than native platforms. You should think twice before choosing it as a platform for any compute-intensive application. In other words, there will be no pure HTML5 Photoshop for a long time. Probably you can move some calculations to server-side, but sometimes it is not an option.
You can check our demo code at GitHub:
https://github.com/eleks/canvasPaint
You can play with app, deployed on S3:
https://s3.amazonaws.com/rnd-demo/canvasPaint/index.html
Stay tuned!
UPD:
Part 2: going deeper!
