There is evidence that FP64 performance on "gaming" cards is crippled due to having very few or no FP64 capable units.
#FP64 PRECISION NVIDIA CODE#
Out of preference a vector processor just wants a stream of "run this simple code against this huge array" and a lot of repeated runs on one piece of data quickly eats up bandwidth and processor cores. The whole point in vector processors is that they work on streams of instructions and data and even in a GPU with massive bandwidth memory access is expensive, especially as your data has a dependency on previous parts of the calculation. And with support for bfloat16, INT8, and INT4, these third-generation Tensor Cores create incredibly versatile accelerators for both AI training. There is a lot of additional math involved because you can't do a simple "add these two registers together" but instead have to do the math the long way around.įrom Stack Overflow Multiplying 64-bit number by a 32-bit number in 8086 asmįor the final code (with merging) you'd end up with 8 MUL instructions, 3 ADD instructions and about 7 ADC instructions. The NVIDIA Ampere architecture Tensor Cores build upon prior innovations by bringing new precisionsTF32 and FP64to accelerate and simplify AI adoption and extend the power of Tensor Cores to HPC. Doing 64-bit floating point math in 32-bit registers is workable, but it is far from a simple halving due to being double width.
There would be additional load/stores and bytes needed to handle overflow which might use more registers. However, CUDA seems like the more useful. Normally theres a fixed ratio between the peak single and double precision. Here, as far as I understand, double precision has to be activated manually. Double precision (FP64) compute performance has always been lower than single precision (FP32) in GPUs for that reason. However, in order to support non NVIDIA hardware, the user should be able to use OpenCL, too. All of the GPUs you mention have some FP64 double-precision capability, so the differences dont come down to capability so much as performance. AMD appears to offer more cores for that sort of computation over NVIDIA. In CUDA, this is no problem, it is automatically supported. The numbers of 1/8 and 1/24 that you refer to are not affecting precision but they are affecting throughput. Click to expand Click to expand Click to expand menu. On the other hand multiplying 64-bit values would require either 4 registers (two 64-bit values split into 32-bit parts each) or memory load/stores between doing the lower 32-bit and then the higher 32-bit of the 64-bit value. FP64 refers to float and double in C or C++. NVIDIA Home Menu icon Menu icon Close icon Close icon Close icon Accordion is closed, click to open. Probably because the default register size within the units is 32-bits.Ī 32-bit register can hold two 16-bit values that can be multiplied across resulting in a doubling of performance.
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