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GPT Logo

GPT - Grid Python Toolkit

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.

System Requirements

Before installing GPT, ensure your system meets the following requirements:

  • Operating System: Linux (Ubuntu 18.04+, Debian 10+, CentOS 7+) or macOS (10.14+)
  • CPU: x86_64 architecture with at least AVX support (including all recent Intel and AMD CPUs), ARM architecture with NEON/SVE support (such as Apple silicon M1 and newer, A64FX)
  • GPU: GPT can make use of accelerators using CUDA/HIP/SYCL (optional)
  • Memory: Minimum 8GB RAM, 16GB or more recommended for larger simulations
  • Storage: At least 10GB of free disk space
  • Python: Version 3.6 or newer

Prerequisites

GPT requires the following components:

  1. Grid: Based on the feature/gpt branch of https://github.com/lehner/Grid ; please also consult Grid's README for supported architectures
  2. Python 3.6 or newer
  3. Optionally an MPI implementation (e.g., OpenMPI, MPICH); Grid needs to be compiled with "--enable-comms=none" in the absence of MPI.
  4. BLAS and LAPACK libraries

Installation

Quick Start with Docker

The fastest way to try GPT is using Docker:

  1. Install Docker on your system (https://docs.docker.com/get-docker/)

  2. Run the following command to start a Jupyter notebook server:

    docker run --rm -p 8888:8888 gptdev/notebook

  3. Open the displayed link (http://127.0.0.1:8888/?token=) in your browser

Note: This session doesn't retain data after termination. To mount your current directory, use:

docker run --rm -p 8888:8888 -v $(pwd):/notebooks gptdev/notebook

Please consult the GPT Docker documentation for additional options.

Local Installation

For a detailed local installation, follow these steps:

  1. Clone the GPT repository:

    git clone https://github.com/lehner/gpt

  2. Install Grid dependencies. On Ubuntu/Debian, you can use:

    sudo apt-get update
sudo apt-get install -y build-essential cmake libmpich-dev libopenmpi-dev liblapack-dev libatlas-base-dev

  3. Build and install Grid (here an example for a AVX2 compatible CPU):

    git clone https://github.com/lehner/Grid.git
cd Grid
git checkout feature/gpt
./bootstrap.sh
mkdir build
cd build
../configure --enable-simd=AVX2 CXXFLAGS=-fPIC
make -j
sudo make install

  4. Install GPT:

    cd ../gpt/lib/cgpt
./make

    This produces a source.sh file in the gpt/lib/cgpt/build directory. This file needs to be loaded (e.g., via source gpt-path/gpt/lib/cgpt/build/source.sh) to add gpt to the PYTHONPATH environment variable.

    You can add a Grid build directory as an argument to the "./make" command to have serveral different builds at the same time. This may be useful to test both CPU and GPU based builds or with and without MPI communication.

Bootstrap Script

GPT includes bootstrap scripts for common architectures. From the GPT directory, run:

scripts/bootstrap/debian10.clang.avx2.no-mpi

Replace with the appropriate script for your system.

Connecting GPT to Grid

GPT is built on top of Grid and utilizes its data parallelism features. Here's how they connect:

  1. Grid provides the underlying lattice structure and parallelization.
  2. GPT uses Grid's data types and parallel primitives to implement high-level physics algorithms.
  3. The feature/gpt branch of Grid contains specific optimizations and features for GPT.

Running GPT Efficiently

To run GPT efficiently:

  1. Use MPI for distributed computing across multiple nodes.
  2. Enable OpenMP for shared-memory parallelism on multi-core systems.
  3. Utilize SIMD instructions (such as AVX2 or AVX-512) for vectorization.
  4. For GPU acceleration, use CUDA/HIP/SYCL-enabled builds of Grid and GPT.

Tutorials

You may also visit a static version of the tutorials here.

Usage

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))

Citation

You can cite the use of GPT using the BibTeX entry

@software{GPT,
  author       = {Lehner, Christoph and
                 Bruno, Mattia and
                 Richtmann, Daniel and
                 Schlemmer, Maximilian and
                 Lehner, Raphael and
                 Knüttel, Daniel and
                 Wurm, Thomas and
                 Jin, Luchang and
                 Bürger, Simon and
                 Hackl, Andreas and
                 Klein, Alexander},
  title        = {{Grid Python Toolkit (GPT), 2024-10}},
  month        = 10,
  year         = 2024,
  publisher    = {Zenodo},
  version      = {2024-10},
  doi          = {10.5281/zenodo.14017415},
  url          = {https://doi.org/10.5281/zenodo.14017415}
}