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 79 articles )

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  1. Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun, Torsten Hoefler, Hugh Leather: ProGraML: Graph-based Deep Learning for Program Optimization and Analysis (2020) arXiv
  2. Garzella, Jack J.; Baranowski, Marek; He, Shaobo; Rakamarić, Zvonimir: Leveraging compiler intermediate representation for multi- and cross-language verification (2020)
  3. 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)
  4. Miles Lucas; Michael Bottom: ADI.jl: A Julia Package for High-Contrast Imaging (2020) not zbMATH
  5. Schreiber, Jacob; Bilmes, Jeffrey; Noble, William Stafford: apricot: submodular selection for data summarization in Python (2020)
  6. Seyoon Ko, Hua Zhou, Jin Zhou, Joong-Ho Won: DistStat.jl: Towards Unified Programming for High-Performance Statistical Computing Environments in Julia (2020) arXiv
  7. Andersson, Joel A. E.; Gillis, Joris; Horn, Greg; Rawlings, James B.; Diehl, Moritz: CasADi: a software framework for nonlinear optimization and optimal control (2019)
  8. Niemetz, Aina; Preiner, Mathias; Reynolds, Andrew; Zohar, Yoni; Barrett, Clark; Tinelli, Cesare: Towards bit-width-independent proofs in SMT solvers (2019)
  9. 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
  10. Sharma, Tushar; Reps, Thomas: A new abstraction framework for affine transformers (2019)
  11. Boutonnet, Rémy; Halbwachs, Nicolas: Improving the results of program analysis by abstract interpretation beyond the decreasing sequence (2018)
  12. Hensel, Jera; Giesl, Jürgen; Frohn, Florian; Ströder, Thomas: Termination and complexity analysis for programs with bitvector arithmetic by symbolic execution (2018)
  13. Kiefer, Moritz; Klebanov, Vladimir; Ulbrich, Mattias: Relational program reasoning using compiler IR (2018)
  14. Lauko, Henrich; Ročkai, Petr; Barnat, Jiří: Symbolic computation via program transformation (2018)
  15. Lopez-Garcia, P.; Darmawan, L.; Klemen, M.; Liqat, U.; Bueno, F.; Hermenegildo, M. V.: Interval-based resource usage verification by translation into Horn clauses and an application to energy consumption (2018)
  16. Popuri, Sai K.; Raim, Andrew M.; Neerchal, Nagaraj K.; Gobbert, Matthias K.: Parallelizing computation of expected values in recombinant binomial trees (2018)
  17. Wägemann, Peter; Dietrich, Christian; Distler, Tobias; Ulbrich, Peter; Schröder-Preikschat, Wolfgang: Whole-system worst-case energy-consumption analysis for energy-constrained real-time systems (2018)
  18. Yang, Kai; Duan, Zhenhua; Tian, Cong; Zhang, Nan: A compiler for MSVL and its applications (2018)
  19. Bezanson, Jeff; Edelman, Alan; Karpinski, Stefan; Shah, Viral B.: Julia: a fresh approach to numerical computing (2017)
  20. Dunning, Iain; Huchette, Joey; Lubin, Miles: JuMP: a modeling language for mathematical optimization (2017)

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