GPT is a Python measurement toolkit built on Grid data parallelism (MPI, OpenMP, SIMD, and SIMT). It provides a physics library for lattice QCD and related theories, a QIS module including a digital quantum computing simulator, and a machine learning module.
The fastest way to try GPT is to install Docker, start a Jupyter notebook server with the latest GPT version by running
docker run --rm -p 8888:8888 gptdev/notebook
and then open the shown link http://127.0.0.1:8888/?token=<token> in a browser.
You should see the tutorials folder pre-installed.
Note that this session does not retain data after termination. Run
docker run --rm -p 8888:8888 -v $(pwd):/notebooks gptdev/notebook
to instead mount the current working directory on your machine.
Please consult the GPT Docker documentation for additional options.
A detailed description on how to install GPT locally can be found here.
You may also visit a static version of the tutorials here.
import gpt as g
# Double-precision 8^4 grid
grid = g.grid([8,8,8,8], g.double)
# Parallel random number generator
rng = g.random("seed text")
# Random gauge field
U = g.qcd.gauge.random(grid, rng)
# Mobius domain-wall fermion
fermion = g.qcd.fermion.mobius(U, mass=0.1, M5=1.8, b=1.0, c=0.0, Ls=24,
boundary_phases=[1,1,1,-1])
# Short-cuts
inv = g.algorithms.inverter
pc = g.qcd.fermion.preconditioner
# Even-odd-preconditioned CG solver
slv_5d = inv.preconditioned(pc.eo2_ne(), inv.cg(eps = 1e-4, maxiter = 1000))
# Abstract fermion propagator using this solver
fermion_propagator = fermion.propagator(slv_5d)
# Create point source
src = g.mspincolor(U[0].grid)
g.create.point(src, [0, 0, 0, 0])
# Solve propagator on 12 spin-color components
prop = g( fermion_propagator * src )
# Pion correlator
g.message(g.slice(g.trace(prop * g.adj(prop)), 3))