3 ms·
For most day-to-day (non-big) data the delay from copying the data to and from the GPU will outweigh the time saved. For large data, there’s usually an existing
by jahewson 4y ago
For most day-to-day (non-big) data the delay from copying the data to and from the GPU will outweigh the time saved. For large data, there’s usually an existing library for handing it - images, videos, ML weights, etc. It may take advantage of SIMD instructions, perhaps, or GPU, the end user doesn’t need to know.
Another reason is that specialised devices beat general devices. Once everyone settles on H.264, then adding specialised decoding silicon to the die is a big win, especially where battery life and efficiency matter. Same for ray-tracing, neural/tensor cores.
- torginus 4y agoYeah, but we got integrated GPUs which share memory memory with the CPU - consoles, mobiles, have done this forever, and it seems that sharing memory between the CPU and GPU is the way to go, considering stuff like the M1.
- rocqua 4y agoM1 can pull off shared memory because it has ridiculous RAM speed. iGPU has to share memory. Thing is CPU is very memory latency sensitive, whilst GPU is much more memory throughout dependent. Hence they get memory with different tradeoffs.