The packing of data is good practice for many reasons, including disk space and efficient RAM or cache access. If we know the meaning of data we can often narrow down the range and precision, making informed decisions as to the amount of bytes we need. I was inspired once… Continue reading| The Code Corsair
GPUs make work parallelism very easy by design: each drawcall/dispatch shader instruction operates on batches of vertices, pixels, threads in general at the same time automatically. On the other ha…| Interplay of Light
I had a good question through Twitter DMs about what occupancy is and why is it important for shader performance, I am expanding my answer into a quick blog post. First some context, GPUs, while ru…| Interplay of Light
AMD RGP gives you unprecedented, in-depth access to a GPU. Easily analyze graphics, async compute usage, event timing, pipeline stalls, barriers, bottlenecks, and other performance inefficiencies.| gpuopen.com
The PIX downloads on this website are for use on Windows 10. If you are an Xbox developer, use the version of PIX that is included with the XDK instead.| PIX on Windows
Today's high computational throughput probably would not be attainable without the application of the SIMD paradigm in modern processors in increasingly clever ways. It's no coincidence that GPUs also gain most of their performance, die area, and efficiency benefits thanks to this instruction issue scheme. In this article we will explore a couple of examples of how GPUs may take advantage of SIMD and the implications of those on the programming model.| RasterGrid