3 ms·

That is interesting, even though I would need to look at non-consumer-GFX cards. What about actual benchmarks, though? Passmark, for example: https://www.video

That is interesting, even though I would need to look at non-consumer-GFX cards. What about actual benchmarks, though? Passmark, for example: https://www.videocardbenchmark.net/directCompute.html https://www.videocardbenchmark.net/directCompute.html has: GeForce RTX 4090 28,916 RTX 6000 Ada 23,296 GeForce RTX 4080 22,016 Radeon RX 7900 XTX 18,767 and Geekbench 6: https://browser.geekbench.com/opencl-benchmarks https://browser.geekbench.com/opencl-benchmarks has: GeForce RTX 4090 321928 L40 292357 <- L40S is out and may be better RTX 6000 Ada 274348 H100 267514 NVIDIA GeForce RTX 239863 Radeon RX 7900 XTX 199412

NavinF 3y ago
Neither of those benchmarks is relevant to ML which is what GP is talking about
- izacus 3y ago
  So what is relevant? As other poster said, nVidia tends to outpace AMD cards in pretty much every use case in real life.
  - Const-me 3y ago
    The only important numbers are processing power TFlops, and memory bandwidth GB/s. If your compute shader doesn’t approach the theoretical limit of either computations or memory, it doesn’t mean there’s anything wrong with the GPU. Here’s incomplete list of possible reasons. ● Insufficient parallelism of the problem. Some problems are inherently sequential. ● Poor HLSL programming skills. For example, a compute shaders with 32 threads/group wastes 50% of compute units of most AMD GPUs, the correct number for AMD is 64 threads/group, or a multiple of 64. BTW, nVidia and Intel are fine with 64 threads/group, they run 1 thread group as 2 wavefronts which does not waste any resources. ● The problem being too small to compensate for the overhead. For example, CPUs multiply two 4x4 matrices in a small fraction of a time it takes to dispatch a compute shader for that. You gonna need much larger matrices for GPGPU to win.

NavinF 3y ago

Neither of those benchmarks is relevant to ML which is what GP is talking about

izacus 3y ago
So what is relevant? As other poster said, nVidia tends to outpace AMD cards in pretty much every use case in real life.
- Const-me 3y ago
  The only important numbers are processing power TFlops, and memory bandwidth GB/s. If your compute shader doesn’t approach the theoretical limit of either computations or memory, it doesn’t mean there’s anything wrong with the GPU. Here’s incomplete list of possible reasons. ● Insufficient parallelism of the problem. Some problems are inherently sequential. ● Poor HLSL programming skills. For example, a compute shaders with 32 threads/group wastes 50% of compute units of most AMD GPUs, the correct number for AMD is 64 threads/group, or a multiple of 64. BTW, nVidia and Intel are fine with 64 threads/group, they run 1 thread group as 2 wavefronts which does not waste any resources. ● The problem being too small to compensate for the overhead. For example, CPUs multiply two 4x4 matrices in a small fraction of a time it takes to dispatch a compute shader for that. You gonna need much larger matrices for GPGPU to win.