Ampere), the compute chip itself is a totally separate silicon GA100 vs GeForce and Quadro chips based on GA102, 104, 106, etc. Seriously, just go and read some of the tons of information thats available out there on the Kepler and Maxwell architecture (and the little info thats available for Pascal). Of course H100 has many other compute specific capabilities that doesn't exist in Ada.Įven in generation where Nvidia uses the same name for their compute chip (e.g. Hence the 1/32 FP64 capabilities in Ada based cards. A single GP100 with 3580 FP32/1792 FP64 CUDA Cores has incredible horsepower to render photorealistic design concepts interactively, create extremely detailed. They are different architecture where Hopper does not have any RT cores and Ada does not have FP64 cores beyond simple compatibility. A100, H100).įor the current generation, the compute chip is based on Hopper architecture (GH100) and everything else (gaming, pro viz) are based on Ada architecture (AD102, 103, 104, etc). I suspect this is due to Nvidia is targeting Pro Viz market for people who want to use RTX to speed up their workload and RT cores do not exist in the compute chips (e.g. Sometimes Quadro does end up using the compute chip (Quadro GP100 and GV100) but Nvidia hasn't put their compute chip in Quadro since Volta. GP100 is only used for their compute related product while GP102, 104, and others are used for their GeForce product. Over time, Nvidia has carved out their compute chip into its own silicon. This leads to product like the OG TITAN where it has 1/2 FP64 capabilities. In fact, I'm pretty it was Cray who said that a supercomputer is a way to turn a CPU-bound problem into an IO-bound problem.Product segmentation via different silicon.īack in the days before GPGPU workload was "less" specialized, Nvidia used to use the same chip for their entire portfolio. With GPU and "classic" HPC (don't know about the new systems with the "compute fabric interconnects"), memory usually becomes the bottleneck (except for embarrasingly paralell problems, of course). For example, today most CPUs end up being bound by cache sizes and performance tuning focuses on being nice on the cache rather than being optimal in your instructions (see for example Abrash's Pixomatic articles, which are about high performance assembly programming in "modern environments"). And here are my two questions: Why did Nvidia put both FP32 and FP64 units in the chip Why not just put FP64 units that are capable of performing 2xFP32 operations per instruction (like the SIMD instruction sets in CPUs). People/manufacturers tend to look at clock rates, fill rates (for GPUs), FLOPs, "crunching power" in general, forgetting completely the memory part. Each Streaming Multiprocessor in the Pascal consists of 64xFP32 and 32xFP64 cores. I sort of "under-sold" it on the "better design" part of my last bullet. I agree on the memory model being the most interesting thing about this card. So, to summarize, if in 2000 the fastest supercomputer on the planet ran at about 4.9 TFLOPs, does that mean, apples-apples on the LINPACK (and only the LINPACK), that Pascal today would outperform that Supercomputer? This excludes the use of a fast matrix multiply algorithm like "Strassen's Method" or algorithms which compute a solution in a precision lower than full precision (64 bit floating point arithmetic) and refine the solution using an iterative approach. In particular, the operation count for the algorithm must be 2/3 n^3 + O(n^2) double precision floating point operations. In an attempt to obtain uniformity across all computers in performance reporting, the algorithm used in solving the system of equations in the benchmark procedure must conform to LU factorization with partial pivoting. That is, on the top 500 list, the first 4.9 Teraflop computer was in 2000, but does that mean that Pascal could provide performance similar to the Supercomputer on the LINPACK benchmark? What I'm trying to figure out, is whether a teraflop directly comparable.
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