Posts Tagged ‘GPU’

Pushing computation to the front: thumbnail generation

Frontend possibilities

As the APIs brought forward by HTML5 about a decade ago have matured and the devices running web browsers have continued to improve in computational power, looking at what’s possible on the frontend and the ability to bring backend computations to the frontend has been increasingly interesting to me. Such architectures would see each user’s browsers as a worker for certain tasks and could simply backend systems, as those tasks are pushed forward to the client. Using Canvas for image processing tasks is one area that interesting and that I’ve had success with.

For Mural, I did the following Medium-esque image preload effect, the basis of which is generating a tiny (16×16) thumbnail which is loaded with the page. That thumbnail is blurred via CSS filter, and transitions to the full-resolution image once it’s loaded. The thumbnail itself is generated entirely on the frontend when a card is created and saved alongside the card data.

In this post, I’ll run though generating and handling that 16×16 thumbnail. This is fairly straightforward use of the Canvas API, but it does highlight how frontend clients can be utilized for operations typically relegated to server-side systems.

The image processing code presented is encapsulated in the canvas-image-transformer library.

<img> → <canvas>

A precursor for any sort of image processing is getting the image data into a <canvas>. The <img> element and corresponding HTMLImageElement interface don’t provide any sort of pixel-level read/write functionality, whereas the <canvas> element and corresponding HTMLCanvasElement interface does. This transformation is pretty straightforward:

The code is as follows (an interesting thing to note here is that this can all be done without injecting anything into the DOM or rendering anything onto the screen):

const img = new Image(); img.onload = function() { const canvas = document.createElement('canvas'); canvas.width = img.width; canvas.height = img.height; const canvasCtx = canvas.getContext('2d'); canvasCtx.drawImage(img, 0, 0, img.width, img.width); // the image has now been rendered onto canvas } img.src = "https://some-image-url";

Resizing an image

Resizing is trivial, as it can be handled directly via arguments to CanvasRenderingContext2D.drawImage(). Adding in a bit of math to do proportional scaling (i.e. preserve aspect ratio), we can wrap the transformation logic into the following method:

/** * * @param {HTMLImageElement} img * @param {Number} newWidth * @param {Number} newHeight * @param {Boolean} proportionalScale * @returns {Canvas} */ imageToCanvas: function(img, newWidth, newHeight, proportionalScale) { if(proportionalScale) { if(img.width > img.height) { newHeight = newHeight * (img.height / img.width); } else if(img.height > img.width) { newWidth = newWidth * (img.width / img.height); } else {} } var canvas = document.createElement('canvas'); canvas.width = newWidth; canvas.height = newHeight; var canvasCtx = canvas.getContext('2d'); canvasCtx.drawImage(img, 0, 0, newWidth, newHeight); return canvas; }

Getting the transformed image from the canvas

My goto method for getting the data off a canvas and into a more interoperable form is to use the HTMLCanvasElement.toDataURL() method, which allows easily getting the image as a PNG or JPEG. I do have mixed feeling about data-URIs; they’re great for the web, because so much of the web is textually based, but they’re also horribly bloated and inefficient. In any case, I think interoperability and ease-of-use usually wins out (esp. here where we’re dealing with a 16×16 thumbnail and the data-uri is relatively lightweight) and getting a data-uri is generally the best solution.

Using CanvasRenderingContext2D.getImageData() to get the raw pixel from a canvas is also an option but, for a lot of use-cases, you’d likely need to compress and/or package the data in some way to make use of it.

Save the transformed image

With a data-uri, saving the image is pretty straightforward. Send it to the server via some HTTP method (POST, PUT, etc.) and save it. For a 16×16 PNG the data-uri textual representation is small enough that we can put it directly in a relational database and not worry about a conversion to binary.

Alternatives & limitations

The status quo alternative is having this sort of image manipulation logic encapsulated within some backend component (method, microservice, etc.) and, to be fair, such systems work well. There’s also some very concrete benefits:

  • You are aware of and have control over the environment in which the image processing is done, so you’re isolated from browser quirks or issues stemming from a user’s computing environment.
  • You have an easier path for any sort of backfill (e.g. how do you generate thumbnails for images previously uploaded?) or migration needs (e.g. how can you move to a different sized thumbnail?); you can’t just run though rows in a database and make a call to get what you need.

However, something worth looking at is that backend systems and server-side environments are typically not optimized for any sort of graphics workload, as processing is centered around CPU cores. In contrast, the majority of frontend environments have access to a GPU, even fairly cheap phone have some sort of GPU that is better suited for “embarassing parallel”-esque graphics operations, the performance benefits of which you get for free with the Canvas API in all modern browsers.

In Chrome, see the output of chrome://gpu:

chrome settings, canvas hardware acceleration

Scale, complexity and cost also come into play. Thinking of frontend clients as computational nodes can change the architecture of systems. The need for server-side resources (hardware, VMs, containers, etc.) is eliminated. Scaling concerns are also, to a large extent, eliminated or radically changed as operations are pushed forward to the client.

Future work

What’s presented here is just scratching the surface of what’s possible with Canvas. WebGL also presents as a ton of possibilities and abstraction layers like gpu.js are really interesting. Overall, it’s exciting to see the web frontend evolve beyond a mechanism for user input and into a layer in which substantive computation can be done.

Batching, a basis for optimization

It’s interesting that in 3 distinct domains I’ve run across the same underlying basis for optimization:

  • Graphics: Modern GPUs depend heavily on batching primitives, typically triangles. Instead of rendering triangles individually, you get much better performance by batching primitives together in a list, sending it to the GPU via a single call, then letting the GPU pipelines to do their thing. Even before modern GPUs existed, graphics cards supported techniques like BitBlt which, essentially, performed operations on batched blocks of pixels, to take advantage of the embarrassingly parallel nature of computer graphics.
  • Relational Databases: Issuing lots of small queries can kill performance. A better strategy is, usually, to issue fewer queries, joining and returning as much data as possible with each query. Even if these queries becomes complex and costly, the cost of a complex query will usually still be less than the aggregate cost of numerous simpler queries.
  • Networking: The speed of light sucks… server and packet switching latencies make things worse. I usually assume ~50ms baseline latency to send a request packet + get a reply packet back from an internet server (I use the term “packet” loosely, referring to programmer-defined, application-level “packets” or messages, or whatever you like to call them, not necessarily TCP/IP packets). Note that this baseline is regardless of the amount of information in a packet and is bound by the travel time between server and client. So, to optimize communication and bandwidth, a good strategy is to transfer as much as possible per-packet instead of depending upon numerous requests/responses to/from a server, which would mean lots of packets and lots of wasted time.


I recently read about the Windows Advanced Rasterization Platform (WARP), which is a software rasterizer that will ship as part of Windows 7. WARP is targeted at:

Casual Games: Games have simple rendering requirements but also want the ability to use impressive visual effects that can be hardware accelerated. The majority of the best selling game titles for Windows are either simulations or casual games, neither of which requires high performance graphics, but both styles of games greatly benefit from modern shader based graphics and the ability to scale on hardware if present.

Existing Non-Gaming Applications: There is a large gamut of graphical applications that want to minimize the number of code paths in their rendering layer. WARP10 enables these applications to implement a single Direct3D 10, 10.1, or 11 code-path that can target a very large number of machine configurations.

Advanced Rendering Games: Game developers that want to isolate graphics card or driver specific rendering errors. We believe that all games, even extremely graphically demanding games would benefit from being able to render their content using WARP to validate that any visual artifacts they might experience are due to rendering errors or problems with hardware or drivers.

Using WARP as a tool for isolating rendering errors is understandable, but as a fallback for DirectX 10 casual games or non-gaming applications attempting to run on a PC w/o a DX10 GPU, a few things pop into my mind.

  • As a fallback mechanism, it goes back too far. We’re talking about going from DX10 -> software rasterization. There’s still lots of graphics hardware out there that targeted previous versions of DirectX, at the very least DX7, DX8, and DX9. Why not allow for seamless fallback to these earlier classes of graphics hardware, instead of a making a gigantic leap backwards to software rasterization? From a developer’s perspective, there would be a real benefit here in writing a DX10 codepath and having it run on older hardware.
  • DX10 adoption is slow to non-existent due to the slow adoption rate of Windows Vista. Unless Microsoft is able to generate massive demand for Windows 7, WARP will have little impact due to the little impact of DX10.
  • A project like WARP seems to be based around the mentality that a GPU is something special for a PC instead of a requirement. Versus software rasterization, GPU rasterization is orders of magnitude faster and the price of a decent card is under $50. Why is setting a GPU requirement such an endeavor, for Microsoft of all companies?!
  • On performance, WARP beats Intel integrated graphics. This really isn’t a surprise or any sort of accomplishment. Intel is really just selling overpriced garbage here.
  • Perhaps Microsoft working on a project like WARP instead of setting stricter graphics hardware requirements for Windows 7 is due to another shady deal with Intel. Remember the one with Vista.