Publications

Order by: publication date number of citations

• Codebase release 1.3 for SymTensor

  Yang Gao, Phillip Helms, Garnet Kin-Lic Chan, Edgar Solomonik

  SciPost Phys. Codebases 10-r1.3 (2023) · published 24 February 2023 | Toggle abstract · src

  Tensor contractions are ubiquitous in computational chemistry and physics, where tensors generally represent states or operators and contractions express the algebra of these quantities. In this context, the states and operators often preserve physical conservation laws, which are manifested as group symmetries in the tensors. These group symmetries imply that each tensor has block sparsity and can be stored in a reduced form. For nontrivial contractions, the memory footprint and cost are lowered, respectively, by a linear and a quadratic factor in the number of symmetry sectors. State-of-the-art tensor contraction software libraries exploit this opportunity by iterating over blocks or using general block-sparse tensor representations. Both approaches entail overhead in performance and code complexity. With intuition aided by tensor diagrams, we present a technique, irreducible representation alignment, which enables efficient handling of Abelian group symmetries via only dense tensors, by using contraction-specific reduced forms. This technique yields a general algorithm for arbitrary group symmetric contractions, which we implement in Python and apply to a variety of representative contractions from quantum chemistry and tensor network methods. As a consequence of relying on only dense tensor contractions, we can easily make use of efficient batched matrix multiplication via Intel's MKL and distributed tensor contraction via the Cyclops library, achieving good efficiency and parallel scalability on up to 4096 Knights Landing cores of a supercomputer.

  1 citation

• Automatic transformation of irreducible representations for efficient contraction of tensors with cyclic group symmetry

  Yang Gao, Phillip Helms, Garnet Kin-Lic Chan, Edgar Solomonik

  SciPost Phys. Codebases 10 (2023) · published 24 February 2023 | Toggle abstract · pdf

  Tensor contractions are ubiquitous in computational chemistry and physics, where tensors generally represent states or operators and contractions express the algebra of these quantities. In this context, the states and operators often preserve physical conservation laws, which are manifested as group symmetries in the tensors. These group symmetries imply that each tensor has block sparsity and can be stored in a reduced form. For nontrivial contractions, the memory footprint and cost are lowered, respectively, by a linear and a quadratic factor in the number of symmetry sectors. State-of-the-art tensor contraction software libraries exploit this opportunity by iterating over blocks or using general block-sparse tensor representations. Both approaches entail overhead in performance and code complexity. With intuition aided by tensor diagrams, we present a technique, irreducible representation alignment, which enables efficient handling of Abelian group symmetries via only dense tensors, by using contraction-specific reduced forms. This technique yields a general algorithm for arbitrary group symmetric contractions, which we implement in Python and apply to a variety of representative contractions from quantum chemistry and tensor network methods. As a consequence of relying on only dense tensor contractions, we can easily make use of efficient batched matrix multiplication via Intel's MKL and distributed tensor contraction via the Cyclops library, achieving good efficiency and parallel scalability on up to 4096 Knights Landing cores of a supercomputer.

  1 citation

• An algorithm to parallelise parton showers on a GPU

  Michael H. Seymour, Siddharth Sule

  SciPost Phys. Codebases 33 (2024) · published 12 August 2024 | Toggle abstract · pdf

  The Single Instruction, Multiple Thread (SIMT) paradigm of GPU programming does not support the branching nature of a parton shower algorithm by definition. However, modern GPUs are designed to schedule threads with diverging processes independently, allowing them to handle such branches. With regular thread synchronisation and careful treatment of the individual steps, one can simulate a parton shower on a GPU. We present a Sudakov veto algorithm designed to simulate parton branching on multiple events in parallel. We also release a CUDA C++ program that generates matrix elements, showers partons and computes jet rates and event shapes for LEP at 91.2 GeV on a GPU. To benchmark its performance, we also provide a near-identical C++ program designed to simulate events serially on a CPU. While the consequences of branching are not absent, we demonstrate that a GPU can provide the throughput of a many-core CPU. As an example, we show that the time taken to shower $10^6$ events on one NVIDIA TESLA V100 GPU is equivalent to that of 295 Intel Xeon E5-2620 CPU cores.

  1 citation

• Codebase release 1.0 for TeNPy

  Johannes Hauschild, Jakob Unfried, Sajant Anand, Bartholomew Andrews, Marcus Bintz, Umberto Borla, Stefan Divic, Markus Drescher, Jan Geiger, Martin Hefel, Kévin Hémery, Wilhelm Kadow, Jack Kemp, Nico Kirchner, Vincent S. Liu, Gunnar Möller, Daniel Parker, Michael Rader, Anton Romen, Samuel Scalet, Leon Schoonderwoerd, Maximilian Schulz, Tomohiro Soejima, Philipp Thoma, Yantao Wu, Philip Zechmann, Ludwig Zweng, Roger S. K. Mong, Michael P. Zaletel, Frank Pollmann

  SciPost Phys. Codebases 41-r1.0 (2024) · published 26 November 2024 | Toggle abstract · src

  TeNPy (short for 'Tensor Network Python') is a python library for the simulation of strongly correlated quantum systems with tensor networks. The philosophy of this library is to achieve a balance of readability and usability for new-comers, while at the same time providing powerful algorithms for experts. The focus is on MPS algorithms for 1D and 2D lattices, such as DMRG ground state search, as well as dynamics using TEBD, TDVP, or MPO evolution. This article is a companion to the recent version 1.0 release of TeNPy and gives a brief overview of the package.

  1 citation

• Codebase release r1.0 for disorder

  Alexander Karlberg

  SciPost Phys. Codebases 32-r1.0 (2024) · published 9 July 2024 | Toggle abstract · src

  We present a Fortran 77/95 code capable of computing QCD corrections in deep inelastic scattering (DIS). The code uses the Projection-to-Born method to augment an existing $\mathcal{O}(\alpha_{\mathrm{s}}^2)$ dijet DIS code, thereby obtaining predictions for photon-mediated neutral-current single-jet DIS production in the laboratory frame. The code is lightweight and fast, and yet includes the most common functionalities found in typical perturbative QCD programs, like automatic renormalisation and factorisation scale uncertainties, options to run and combine multiple seeds, and interfaces to fastjet and LHAPDF. Due to the underlying disent and HOPPET codes, the program also provides stable results in the infrared, relevant for extracting logarithmic coefficients for analytic resummations, and access to the massless DIS structure functions and (reduced) cross sections up to $\mathcal{O}(\alpha_{\mathrm{s}}^3)$.

  1 citation

• disorder: Deep inelastic scattering at high orders

  Alexander Karlberg

  SciPost Phys. Codebases 32 (2024) · published 9 July 2024 | Toggle abstract · pdf

  We present a Fortran 77/95 code capable of computing QCD corrections in deep inelastic scattering (DIS). The code uses the Projection-to-Born method to augment an existing $\mathcal{O}(\alpha_{\mathrm{s}}^2)$ dijet DIS code, thereby obtaining predictions for photon-mediated neutral-current single-jet DIS production in the laboratory frame. The code is lightweight and fast, and yet includes the most common functionalities found in typical perturbative QCD programs, like automatic renormalisation and factorisation scale uncertainties, options to run and combine multiple seeds, and interfaces to fastjet and LHAPDF. Due to the underlying disent and HOPPET codes, the program also provides stable results in the infrared, relevant for extracting logarithmic coefficients for analytic resummations, and access to the massless DIS structure functions and (reduced) cross sections up to $\mathcal{O}(\alpha_{\mathrm{s}}^3)$.

  1 citation

• Codebase release r0.1 for PanScales

  Melissa van Beekveld, Mrinal Dasgupta, Basem Kamal El-Menoufi, Silvia Ferrario Ravasio, Keith Hamilton, Jack Helliwell, Alexander Karlberg, Rok Medves, Pier Francesco Monni, Gavin P. Salam, Ludovic Scyboz, Alba Soto-Ontoso, Gregory Soyez, Rob Verheyen

  SciPost Phys. Codebases 31-r0.1 (2024) · published 25 June 2024 | Toggle abstract · src

  In this article, we document version 0.1 of the PANSCALES code for parton shower simulations. With the help of a few examples, we discuss basic usage of the code, including tests of logarithmic accuracy of parton showers. We expose some of the numerical techniques underlying the logarithmic tests and include a description of how users can implement their own showers within the framework. Some of the simpler logarithmic tests can be performed in a few minutes on a modern laptop. As an early step towards phenomenology, we also outline some aspects of a preliminary interface to PYTHIA 8.3, for access to its hard matrix elements and its hadronisation modules.

  1 citation

• Codebase release r1.1 for SmoQyDEAC.jl

  James Neuhaus, Nathan S. Nichols, Debshikha Banerjee, Benjamin Cohen-Stead, Thomas A. Maier, Adrian Del Maestro, Steven Johnston

  SciPost Phys. Codebases 39-r1.1 (2024) · published 12 November 2024 | Toggle abstract · src

  We introduce the SmoQyDEAC.jl package, a Julia implementation of the Differential Evolution Analytic Continuation (DEAC) algorithm [N. S. Nichols et al., Phys. Rev. E 106, 025312 (2022)] for analytically continuing noisy imaginary time correlation functions to the real frequency axis. Our implementation supports fermionic and bosonic correlation functions on either the imaginary time or Matsubara frequency axes, and treatment of the covariance error in the input data. This paper presents an overview of the DEAC algorithm and the features implemented in the SmoQyDEAC.jl package. It also provides detailed benchmarks of the package's output against the popular maximum entropy and stochastic analytic continuation methods. The code for this package can be downloaded from our GitHub repository at https://github.com/SmoQySuite/SmoQyDEAC.jl or installed using the Julia package manager. The online documentation, including examples, can be accessed at https://smoqysuite.github.io/SmoQyDEAC.jl/stable/.

  1 citation

• SmoQyDEAC.jl: A differential evolution package for the analytic continuation of imaginary time correlation functions

  James Neuhaus, Nathan S. Nichols, Debshikha Banerjee, Benjamin Cohen-Stead, Thomas A. Maier, Adrian Del Maestro, Steven Johnston

  SciPost Phys. Codebases 39 (2024) · published 12 November 2024 | Toggle abstract · pdf

  We introduce the SmoQyDEAC.jl package, a Julia implementation of the Differential Evolution Analytic Continuation (DEAC) algorithm [N. S. Nichols et al., Phys. Rev. E 106, 025312 (2022)] for analytically continuing noisy imaginary time correlation functions to the real frequency axis. Our implementation supports fermionic and bosonic correlation functions on either the imaginary time or Matsubara frequency axes, and treatment of the covariance error in the input data. This paper presents an overview of the DEAC algorithm and the features implemented in the SmoQyDEAC.jl package. It also provides detailed benchmarks of the package's output against the popular maximum entropy and stochastic analytic continuation methods. The code for this package can be downloaded from our GitHub repository at https://github.com/SmoQySuite/SmoQyDEAC.jl or installed using the Julia package manager. The online documentation, including examples, can be accessed at https://smoqysuite.github.io/SmoQyDEAC.jl/stable/.

  1 citation

• Codebase release 4.0 for RIVET

  Christian Bierlich, Andy Buckley, Jonathan Butterworth, Christian Gutschow, Leif Lonnblad, Tomasz Procter, Peter Richardson, Yoran Yeh

  SciPost Phys. Codebases 36-r4.0 (2024) · published 4 November 2024 | Toggle abstract · src

  The Rivet toolkit is the primary mechanism for phenomenological preservation of collider-physics measurements, containing both a computational core and API for analysis implementation, and a large collection of more than a thousand preserved analyses. In this note we summarise the main changes in the new Rivet 4 major release series. These include a major generalisation and more semantically coherent model for histograms and related data objects, a thorough clean-up of inelegant and legacy observable-computation tools, and new systems for extended analysis-data, incorporation of preserved machine-learning models, and serialization for high-performance computing applications. Where these changes introduce backward-incompatible interface changes, existing analyses have been updated and indications are given on how to update new analysis routines and workflows.

  1 citation