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Comment by kartD | Hacker News Reader

Since this question has come up so many times in the thread, my take is that FP64 and FP16 won't be as good at the GP100. If TITAN X is based on the consumer parts, it misses out on the GP100's FP improvements.

From Anandtech's GTX1080 review page2: As a result while GP100 has some notable feature/design elements for HPC – things such faster FP64 & FP16 performance, ECC, and significantly greater amounts of shared memory and register file capacity per CUDA core – these elements aren’t present in GP104 (and presumably, future Pascal consumer-focused GPUs).

This requires confirmation though, it depends on whether it uses the consumer chip or the HPC chip.

Edit: AT has an article up http://www.anandtech.com/show/10510/nvidia-announces-nvidia-...

They think it's likely a consumer card, so lower FP16 and FP64 perf. Should be a gaming monster though

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Well the Titan X is a new chip: GP102. So they could have picked and chosen features from either GP100 (professional Tesla) or GP104 (consumer GeForce).

We know almost certainly it has lower FP64 perf than GP100 because it has 22% fewer transistors and the FP64 units take a lot of transistors count. However it is less clear about what the FP16 performance is. Nvidia could have decided to match GP100 on that regard.

Doubt it, why have three versions of a chip (one with fp16 improv, one with fp16/fp64 improv and one without them)? Especially when the Titan X has the same number of CUDA cores as the GP100.

"CUDA cores" is a misleading term.

When they quote "CUDA cores", they've been counting float32 fma functional units; e.g., Tesla K40 has 192 float32 fma units per SM x 15 SMs => 2880 "CUDA cores".

http://docs.nvidia.com/cuda/cuda-c-programming-guide/index.h...

fp16 and fp64 are likely different functional units with different issue rates, as is the case with old hardware; unless they've managed to share the same hardware (since for P100 the quoted fp64 rate is exactly half the fp32 rate, and the fp16 is exactly double the fp32 rate).

True, I forgot NVIDIA (and AMD) play around with the "core" definition

There's no real analogue to a CPU core (or thread) on a GPU, there are warp schedulers (Nvidia) and functional units with varying throughput rates. The closest analogue to a CPU thread is the GPU warp (Nvidia), which shares a program counter and executes as a 32-wide vector. AMD wavefronts (64-wide) are a little bit different, but not by much. The CUDA "thread" is really an illusion that makes a SIMD lane easier to program to (sometimes...)

I agree, I think an SMX is the closest to a CPU core - it contains dispatch, cache, schedulers etc. CUDA threads are of course have important differences since all threads in a warp move in lockstep. IMO, all of these contrived definitions are just to ease in programmers into the CUDA/OpenCL model or for bragging rights (like how a Jetson has 192 cores!).

The 22% transistor count could also be due to lack of HBM the memory bus and controllers for that take a big chunk of die space, but I'm also thinking that it would only do 1/2 of the fp64 that the tesla does. I'm disappointed at this Titan tbh 1200$ and no hbm2 memory? If AMD doesn't not royally screwup Vega might be time for a shift unless you explicitly need CUDA.

> it is less clear about what the FP16 performance is

Very curious about this as well. I got two GeForce 1080s as soon as they came out, and was very sad to discover that the stated advantage of Pascal architecture (speedup on FP16) is completely lacking on those.

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