4 ms·

Game engines spend a whole lot of work to not draw things. Even if you have an 8K monitor, that's still only around 33 million pixels. If the data set you want

by thadt 3y ago

Game engines spend a whole lot of work to not draw things. Even if you have an 8K monitor, that's still only around 33 million pixels. If the data set you want to visualize is 5 billion elements, and if you decided that you were going to use every single pixel on the screen, you'd still have to collapse those 5 billion elements in a visual space of less than %1 the size. In a video game it's easy to figure out what not to show - just hide everything that would normally be too small or far away to see. But in a scientific domain, what to show and what to hide might be a much more interesting question. On the other hand, if you're just looking for a plot of a few billion elements with the ability to zoom in and out, then a straightforward decimation of the data and drawing it to the screen can work. In the past I've written custom tools to do just that, but at this point in life I would probably throw it at something like SciChart and let it take care of that.

rcme 3y ago
Graphics is not as trivial of a problem as you're making it seem. Even if you're just rendering a single "thing", that thing can have an unbounded number of vertices. And it's definitely not a trivial problem deciding which vertices are important and which are not.
- thadt 3y ago
  Oh, I just meant that it was conceptually easy - things should look 'right' - whereas with scientific data figuring out how to map that data to a 2D screen often involves a lot of carefully thought-out discretion about what to show and what to hide. The actual implementations of 3D rendering are gargantuan feats of engineering, science, and art. Having spent time writing code to turn terrain height maps into quantized meshes for 3D rendering, I agree entirely that deciding which vertices are important is not a trivial problem. The OP linked a video about Nanite, which IMHO is a marvel of engineering.
  - rcme 3y ago
    My point is that both you and the person you were replying to have a kind of fundamental misconception about why data is slow and graphics are fast. The misconception is that data isn’t slow and graphics aren’t fast, at least relative to one another. For graphics, the main performance metric is (generally) triangle count. You can draw lots of low-poly objects on screen more efficiently than you can draw one very high-poly object. The same holds true for data: you can render low amounts data more efficiently than you can render large amounts of data. Nanite doesn’t magically render high-poly meshes in real time. Nanite needs to pre-process the mesh and produces lower poly meshes that maintain the geometric properties of the original mesh. This is the main innovation of nanite, because traditionally it’s been very hard to reduce triangle count while keeping the overall geometry roughly similar to the original. And in this way, data processing has traditionally been much more efficient than 3D graphics. There have long been various statistical aggregations you can do on data to keep the same rough statistical properties using less data. But you have to be willing to preprocess the data, and that is slow. I haven’t used nanite, but I imagine the import process is also slow relative to the rendering speed after processing.
    - johnnyanmac 3y ago
      >The misconception is that data isn’t slow and graphics aren’t fast, at least relative to one another. depends on the data/graphics. modern commercial GPUs are beefy (even when talking about integrated ones) and I suspect the Matlib kinds of tools aren't even tapping into a fraction of a percent of its power. But at the same time 5 billion draw calls raw will bring even a decent gaming GPU to its knees, at least for responsive, real time applications. The trick is to first understand your data (e.g. that 5 billion triangles are useless on a monitor that has 1-4 billion pixels). As you said, even Nanite isn't truly trying to process a trillion triangles raw. The steps from that understanding to a good enough approximation are indeed some dark magic.

by thadt 3y ago

rcme 3y ago

Graphics is not as trivial of a problem as you're making it seem. Even if you're just rendering a single "thing", that thing can have an unbounded number of vertices. And it's definitely not a trivial problem deciding which vertices are important and which are not.

thadt 3y ago
Oh, I just meant that it was conceptually easy - things should look 'right' - whereas with scientific data figuring out how to map that data to a 2D screen often involves a lot of carefully thought-out discretion about what to show and what to hide. The actual implementations of 3D rendering are gargantuan feats of engineering, science, and art. Having spent time writing code to turn terrain height maps into quantized meshes for 3D rendering, I agree entirely that deciding which vertices are important is not a trivial problem. The OP linked a video about Nanite, which IMHO is a marvel of engineering.
- rcme 3y ago
  My point is that both you and the person you were replying to have a kind of fundamental misconception about why data is slow and graphics are fast. The misconception is that data isn’t slow and graphics aren’t fast, at least relative to one another. For graphics, the main performance metric is (generally) triangle count. You can draw lots of low-poly objects on screen more efficiently than you can draw one very high-poly object. The same holds true for data: you can render low amounts data more efficiently than you can render large amounts of data. Nanite doesn’t magically render high-poly meshes in real time. Nanite needs to pre-process the mesh and produces lower poly meshes that maintain the geometric properties of the original mesh. This is the main innovation of nanite, because traditionally it’s been very hard to reduce triangle count while keeping the overall geometry roughly similar to the original. And in this way, data processing has traditionally been much more efficient than 3D graphics. There have long been various statistical aggregations you can do on data to keep the same rough statistical properties using less data. But you have to be willing to preprocess the data, and that is slow. I haven’t used nanite, but I imagine the import process is also slow relative to the rendering speed after processing.
  - johnnyanmac 3y ago
    >The misconception is that data isn’t slow and graphics aren’t fast, at least relative to one another. depends on the data/graphics. modern commercial GPUs are beefy (even when talking about integrated ones) and I suspect the Matlib kinds of tools aren't even tapping into a fraction of a percent of its power. But at the same time 5 billion draw calls raw will bring even a decent gaming GPU to its knees, at least for responsive, real time applications. The trick is to first understand your data (e.g. that 5 billion triangles are useless on a monitor that has 1-4 billion pixels). As you said, even Nanite isn't truly trying to process a trillion triangles raw. The steps from that understanding to a good enough approximation are indeed some dark magic.