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| SymEngine | |
|---|---|
| Name | SymEngine |
| Developer | SymEngine development team |
| Released | 2014 |
| Programming language | C++ |
| License | MIT License |
| Repository | Git |
SymEngine is a fast symbolic manipulation library implemented primarily in C++ with multi-language bindings. It provides core algebraic capabilities used by researchers and engineers, integrating with multiple projects and ecosystems to deliver symbolic simplification, differentiation, and expression manipulation. The library aims to offer performance, portability, and embeddability for applications spanning computer algebra, numerical computation, and domain-specific tooling.
SymEngine emerged from efforts to provide a high-performance symbolic core for projects such as Python-based environments and scientific software, with roots in collaborations involving contributors from the University of Cambridge, Imperial College London, and companies active in scientific computing. Early development was influenced by the needs of projects like SymPy, SageMath, and initiatives at institutions including Massachusetts Institute of Technology and University of Oxford. Key milestones include initial C++ implementation, adoption of bindings for languages favored in data science and scientific computing communities, and integration with build systems used at research centers such as Los Alamos National Laboratory and Lawrence Berkeley National Laboratory. The project attracted contributors who had previously worked on libraries and systems like Boost, LLVM, and GCC, and engaged with standards and tooling from organizations such as ISO and IEEE on numerical and programming language interoperability.
The architecture centers on a minimal, portable core written in C++ with emphasis on performance and ABI stability, adopting practices used in projects like LLVM and Boost. The core exposes a small, well-typed API designed to interoperate with language runtimes developed at places like Google, Facebook, and Microsoft Research. Internally, the design draws upon algebraic data structures similar to those in Maxima and Maple while avoiding heavy dependencies found in systems such as Mathematica. Memory management techniques and reference counting strategies parallel approaches from Qt and glib, and the build and continuous integration setup leverages tools and services used by projects like GitHub and Travis CI. The codebase uses concepts and patterns promoted by communities around C++ Standards Committee proposals, and testing practices echo those in large open-source projects like Linux kernel and Apache HTTP Server.
SymEngine implements expression trees, arbitrary-precision arithmetic, symbolic differentiation, polynomial algebra, and function manipulation comparable to functionality seen in Maxima, Maple, and Mathematica. It supports exact arithmetic through libraries developed by contributors from GNU MPFR and GMP ecosystems, and provides simplification routines related to algorithms discussed in literature from institutions such as CNRS and INRIA. The feature set includes algebraic factorization, expansion, series expansion techniques inspired by work at Princeton University and Stanford University, and special-function handling with influence from implementations at NIST and Wolfram Research. Additional capabilities cover rational function operations, substitution systems similar to those in Axiom and Reduce, and expression hashing and canonicalization ideas that appear in projects at IBM Research and Bell Labs.
Bindings exist for several programming languages and platforms to facilitate integration with environments like Python, Julia, Ruby, and R. The Python interface complements ecosystems around projects hosted at organizations such as Anaconda, Inc. and NumFOCUS, and plays a role in notebooks developed by users of Jupyter and Google Colaboratory. Julia bindings align with workflows promoted by teams at MIT and ETH Zurich, while interfaces for languages such as Haskell and OCaml draw on functional programming communities at places like University of Cambridge Computer Laboratory and The Alan Turing Institute. The library also exposes C-compatible APIs used in applications from research labs including CERN and European Space Agency for embedding symbolic computation into simulation and data-analysis pipelines.
Performance comparisons have been published by contributors and independent groups benchmarking against systems like SymPy, Maxima, and proprietary systems from Wolfram Research. Benchmarks focus on expression simplification, symbolic differentiation, polynomial arithmetic, and large-scale combinatorial expression handling, with results discussed in forums frequented by engineers from NVIDIA, Intel and AMD regarding numeric and symbolic performance. Profiling and optimization leverage toolchains and methodologies from projects at Google and Facebook for CPU-bound workloads, and some performance work references compiler optimizations researched at LLVM and GCC communities. Microbenchmarks and regression suites are maintained similarly to continuous benchmarking efforts undertaken by large scientific software projects at Argonne National Laboratory and Oak Ridge National Laboratory.
Development is coordinated through distributed version control and collaborative platforms used by communities such as GitHub and GitLab, with governance influenced by practices common in projects like Python Software Foundation-hosted initiatives. Contributors include researchers and engineers from universities including University of Cambridge, Imperial College London, ETH Zurich, and companies that invest in scientific tooling such as Anaconda, Inc. and Google Research. Community communication occurs via mailing lists, chat platforms inspired by those used in projects like Apache Software Foundation and Linux Foundation communities, and at conferences attended by members of ACM and IEEE societies. The governance model and contribution workflow reflect approaches seen in mature open-source ecosystems such as NumPy and SciPy.
The library is employed in computer algebra research at institutions like University of Oxford and Harvard University, in symbolic-numeric workflows in computational physics groups at CERN and Fermilab, and in engineering toolchains at companies such as Siemens and Schlumberger. It is used to accelerate symbolic preprocessing in projects related to machine learning prototypes developed by teams at DeepMind and OpenAI, and in automated reasoning efforts in groups at Stanford University and Princeton University. Additional use cases include curriculum resources in computational mathematics taught at Massachusetts Institute of Technology and California Institute of Technology, symbolic backends for electronic design automation explored by researchers at ARM and Cadence Design Systems, and integration with numerical solvers in workflows at Ansys and COMSOL.
Category:Computer algebra systems