restart "LxxManager", a service.

in powershell

$manifest = (Get-AppxPackage Microsoft.WindowsStore).InstallLocation + '\AppxManifest.xml' ; Add-AppxPackage -DisableDevelopmentMode -Register $manifest

url: https://github.com/tensorflow/tensorflow/issues/30227

https://github.com/tensorflow/tensorflow/issues/30227#issuecomment-506783788

Searching deeply, I found that the first timestep is also used to determine the cell output shape and its dtype.

(py3-tf2-gpu) sephiroce@bike:/usr/local/cuda/samples/5_Simulations/nbody$ ./nbody -benchmark -numbodies=2560000 -device=0

Run "nbody -benchmark [-numbodies=]" to measure performance.

-fullscreen (run n-body simulation in fullscreen mode)

-fp64 (use double precision floating point values for simulation)

-hostmem (stores simulation data in host memory)

-benchmark (run benchmark to measure performance)

-numbodies= (number of bodies (>= 1) to run in simulation)

-device= (where d=0,1,2.... for the CUDA device to use)

-numdevices= (where i=(number of CUDA devices > 0) to use for simulation)

-compare (compares simulation results running once on the default GPU and once on the CPU)

-cpu (run n-body simulation on the CPU)

-tipsy= (load a tipsy model file for simulation)

NOTE: The CUDA Samples are not meant for performance measurements. Results may vary when GPU Boost is enabled.

> Windowed mode

> Simulation data stored in video memory

> Single precision floating point simulation

> 1 Devices used for simulation

gpuDeviceInit() CUDA Device [0]: "Ampere

> Compute 8.6 CUDA device: [GeForce RTX 3090]

number of bodies = 2560000

2560000 bodies, total time for 10 iterations: 69005.547 ms

= 949.721 billion interactions per second

= 18994.415 single-precision GFLOP/s at 20 flops per interaction