The LLVM Project is a collection of modular and reusable compiler and toolchain technologies. Despite its name, LLVM has little to do with traditional virtual machines, though it does provide helpful libraries that can be used to build them.LLVM began as a research project at the University of Illinois, with the goal of providing a modern, SSA-based compilation strategy capable of supporting both static and dynamic compilation of arbitrary programming languages. Since then, LLVM has grown to be an umbrella project consisting of a number of different subprojects, many of which are being used in production by a wide variety of commercial and open source projects as well as being widely used in academic research.

References in zbMATH (referenced in 88 articles )

Showing results 1 to 20 of 88.
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  1. Alex Tritt, Joshua Morris, Joel Hochstetter, R. P. Anderson, James Saunderson, L. D. Turner: Spinsim: a GPU optimized python package for simulating spin-half and spin-one quantum systems (2022) arXiv
  2. Ciaran Welsh, Jin Xu, Lucian Smith, Matthias König, Kiri Choi, Herbert M. Sauro: libRoadRunner 2.0: A High-Performance SBML Simulation and Analysis Library (2022) arXiv
  3. Jaiswal, Shashank: Non-linear Boltzmann equation on hybrid-unstructured non-conforming multi-domains (2022)
  4. Ene, Cristian; Mounier, Laurent; Potet, Marie-Laure: Output-sensitive information flow analysis (2021)
  5. Li, Liyi; Gunter, Elsa L.: A complete semantics of (\mathbbK) and its translation to Isabelle (2021)
  6. Mika, Michal L.; Hughes, Thomas J. R.; Schillinger, Dominik; Wriggers, Peter; Hiemstra, René R.: A matrix-free isogeometric Galerkin method for Karhunen-Loève approximation of random fields using tensor product splines, tensor contraction and interpolation based quadrature (2021)
  7. Niemetz, Aina; Preiner, Mathias; Reynolds, Andrew; Zohar, Yoni; Barrett, Clark; Tinelli, Cesare: Towards satisfiability modulo parametric bit-vectors (2021)
  8. Tsoupidi, Rodothea Myrsini; Lozano, Roberto Castañeda; Baudry, Benoit: Constraint-based diversification of JOP gadgets (2021)
  9. Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun, Torsten Hoefler, Hugh Leather: ProGraML: Graph-based Deep Learning for Program Optimization and Analysis (2020) arXiv
  10. Gao, Kaifeng; Mei, Gang; Piccialli, Francesco; Cuomo, Salvatore; Tu, Jingzhi; Huo, Zenan: Julia language in machine learning: algorithms, applications, and open issues (2020)
  11. Garzella, Jack J.; Baranowski, Marek; He, Shaobo; Rakamarić, Zvonimir: Leveraging compiler intermediate representation for multi- and cross-language verification (2020)
  12. Ish-Shalom, Oren; Itzhaky, Shachar; Manevich, Roman; Rinetzky, Noam: Harnessing static analysis to help learn pseudo-inverses of string manipulating procedures for automatic test generation (2020)
  13. Miles Lucas; Michael Bottom: ADI.jl: A Julia Package for High-Contrast Imaging (2020) not zbMATH
  14. Schreiber, Jacob; Bilmes, Jeffrey; Noble, William Stafford: apricot: submodular selection for data summarization in Python (2020)
  15. Seyoon Ko, Hua Zhou, Jin Zhou, Joong-Ho Won: DistStat.jl: Towards Unified Programming for High-Performance Statistical Computing Environments in Julia (2020) arXiv
  16. Andersson, Joel A. E.; Gillis, Joris; Horn, Greg; Rawlings, James B.; Diehl, Moritz: CasADi: a software framework for nonlinear optimization and optimal control (2019)
  17. Niemetz, Aina; Preiner, Mathias; Reynolds, Andrew; Zohar, Yoni; Barrett, Clark; Tinelli, Cesare: Towards bit-width-independent proofs in SMT solvers (2019)
  18. Ricardo Nobre, João Bispo, Tiago Carvalho, João M.P. Cardoso: Nonio - modular automatic compiler phase selection and ordering specialization framework for modern compilers (2019) not zbMATH
  19. Sharma, Tushar; Reps, Thomas: A new abstraction framework for affine transformers (2019)
  20. Boutonnet, Rémy; Halbwachs, Nicolas: Improving the results of program analysis by abstract interpretation beyond the decreasing sequence (2018)

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Further publications can be found at: http://llvm.org/pubs/