The whole report can be found here
Fractal growth is a computationally intensive simulation which has typically ignored traditionoal first principles in physics in order to increase speed. Lightning is a well understood phenomena that can be simulated accurately with the Dielectric Breakdown Model, but it is computationally expensive to do so. We seek to optimize this simulation with HPC through the parallelization of the simualtion at each time step using a multi-core and -node architecture on Google Cloud Engine. Using this approach, we obtained a non-trivial speedup and amanged to simulate lightning growth on a 1400x1400 grid.
The generated videos can be found in output. We recommend comparing the initial video before our optimization using a 100x100 grid, to the new one in a 1400x1400 grid.
PyCuda implementation is on the pycuda branch.
git checkout pycuda
The implementation is here, on master. To run, you need to do the following commands on Ubuntu 18.04
cd src
./setup.sh
./gb.sh
python3 lightning.pyArchitecture: 96-core machine on GCP $ lscpu Architecture: x86_64 CPU op-mode(s): 32-bit, 64-bit Byte Order: Little Endian CPU(s): 96 On-line CPU(s) list: 0-95 Thread(s) per core: 2 Core(s) per socket: 24 Socket(s): 2 NUMA node(s): 2 Vendor ID: GenuineIntel CPU family: 6 Model: 85 Model name: Intel(R) Xeon(R) CPU @ 2.00GHz Stepping: 3 CPU MHz: 2000.180 BogoMIPS: 4000.36 Hypervisor vendor: KVM Virtualization type: full L1d cache: 32K L1i cache: 32K L2 cache: 256K L3 cache: 56320K NUMA node0 CPU(s): 0-23,48-71 NUMA node1 CPU(s): 24-47,72-95
The implementation can be found on the grpc-buffer branch.
git checkout grpc-buffer
The implementation can be found on the MPI-OMP branch.
git checkout MPI-OMP