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Top 23 C++ Scientific Computing Projects
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InfluxDB
Power Real-Time Data Analytics at Scale. Get real-time insights from all types of time series data with InfluxDB. Ingest, query, and analyze billions of data points in real-time with unbounded cardinality.
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FluidX3D
The fastest and most memory efficient lattice Boltzmann CFD software, running on all GPUs via OpenCL.
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WorkOS
The modern identity platform for B2B SaaS. The APIs are flexible and easy-to-use, supporting authentication, user identity, and complex enterprise features like SSO and SCIM provisioning.
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casadi
CasADi is a symbolic framework for numeric optimization implementing automatic differentiation in forward and reverse modes on sparse matrix-valued computational graphs. It supports self-contained C-code generation and interfaces state-of-the-art codes such as SUNDIALS, IPOPT etc. It can be used from C++, Python or Matlab/Octave.
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ITK
Insight Toolkit (ITK) -- Official Repository. ITK builds on a proven, spatially-oriented architecture for processing, segmentation, and registration of scientific images in two, three, or more dimensions.
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Kratos
Kratos Multiphysics (A.K.A Kratos) is a framework for building parallel multi-disciplinary simulation software. Modularity, extensibility and HPC are the main objectives. Kratos has BSD license and is written in C++ with extensive Python interface. (by KratosMultiphysics)
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preCICE
A coupling library for partitioned multi-physics simulations, including, but not restricted to fluid-structure interaction and conjugate heat transfer simulations.
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ExprTK
C++ Mathematical Expression Parsing And Evaluation Library https://www.partow.net/programming/exprtk/index.html
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avogadrolibs
Avogadro libraries provide 3D rendering, visualization, analysis and data processing useful in computational chemistry, molecular modeling, bioinformatics, materials science, and related areas.
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SaaSHub
SaaSHub - Software Alternatives and Reviews. SaaSHub helps you find the best software and product alternatives
Project mention: How much C++ is used when it comes to performing quant research? | /r/quant | 2023-07-03Does C++ have the equivalent of Pandas or Apache Spark? Are there extensive libraries that exist/are being developed that allow you to perform operations with data? Or do people just use a combination of Python & its various libraries (NumPy etc)? If we leave aside the data bit, are there libraries that allow you to develop ML models in C++ (mlpack for instance ) faster & more efficiently compared to their Python counterparts (scikit-learn)? On a more general note, how does C++ fit into the routine of a Quant Researcher? And at what scale does an organization decide they need to start switching to other languages and spend more time developing the code ?
Loads of people have stated why easy GPU interfaces are difficult to create, but we solve many difficult things all the time.
Ultimately I think CPUs are just satisfactory for the vast vast majority of workloads. Servers rarely come with any GPUs to speak of. The ecosystem around GPUs is unattractive. CPUs have SIMD instructions that can help. There are so many reasons not to use GPUs. By the time anyone seriously considers using GPUs they're, in my imagination, typically seriously starved for performance, and looking to control as much of the execution details as possible. GPU programmers don't want an automagic solution.
So I think the demand for easy GPU interfaces is just very weak, and therefore no effort has taken off. The amount of work needed to make it as easy to use as CPUs is massive, and the only reason anyone would even attempt to take this on is to lock you in to expensive hardware (see CUDA).
For a practical suggestion, have you taken a look at https://arrayfire.com/ ? It can run on both CUDA and OpenCL, and it has C++, Rust and Python bindings.
cmake_minimum_required(VERSION 3.5) project(knn_cpp CXX) # Set up C++ version and properties include(CheckIncludeFileCXX) check_include_file_cxx(any HAS_ANY) check_include_file_cxx(string_view HAS_STRING_VIEW) check_include_file_cxx(coroutine HAS_COROUTINE) set(CMAKE_CXX_STANDARD 20) set(CMAKE_BUILD_TYPE Debug) set(CMAKE_CXX_STANDARD_REQUIRED ON) set(CMAKE_CXX_EXTENSIONS OFF) # Copy data file to build directory file(COPY ${CMAKE_CURRENT_SOURCE_DIR}/iris.data DESTINATION ${CMAKE_CURRENT_BINARY_DIR}) # Download library usinng FetchContent include(FetchContent) FetchContent_Declare(matplotplusplus GIT_REPOSITORY https://github.com/alandefreitas/matplotplusplus GIT_TAG origin/master) FetchContent_GetProperties(matplotplusplus) if(NOT matplotplusplus_POPULATED) FetchContent_Populate(matplotplusplus) add_subdirectory(${matplotplusplus_SOURCE_DIR} ${matplotplusplus_BINARY_DIR} EXCLUDE_FROM_ALL) endif() FetchContent_Declare( fmt GIT_REPOSITORY https://github.com/fmtlib/fmt.git GIT_TAG 7.1.3 # Adjust the version as needed ) FetchContent_MakeAvailable(fmt) # Add executable and link project libraries and folders add_executable(${PROJECT_NAME} main.cc) target_link_libraries(${PROJECT_NAME} PUBLIC matplot fmt::fmt) aux_source_directory(lib LIB_SRC) target_include_directories(${PROJECT_NAME} PRIVATE ${CMAKE_CURRENT_SOURCE_DIR}) target_sources(${PROJECT_NAME} PRIVATE ${LIB_SRC}) add_subdirectory(tests)
- single cell genomics: in collaboration with the Chan-Zuckerberg Initiative, we recently released TileDB-SOMA for single cell data, with APIs for both Python and R built around a common storage specification: https://tiledb.com/blog/tiledb-101-single-cell
With TileDB, all data — tables, genomics, images, videos, location, time-series — across multiple domains is captured as multi-dimensional arrays. TileDB Cloud implements a totally serverless infrastructure and delivers access control, easier data and code sharing and distributed computing at global scale, eliminating cluster management, minimizing TCO and promoting scientific collaboration and reproducibility.
Website: https://tiledb.com
GitHub: https://github.com/TileDB-Inc/TileDB
Interface for several solvers and integrators.
NOTE:
The open source projects on this list are ordered by number of github stars.
The number of mentions indicates repo mentiontions in the last 12 Months or
since we started tracking (Dec 2020).
C++ Scientific Computing related posts
- Why Wolfram uses square brackets for function calls
- What's the best way to get my language stress tested?
- Nelson Numerical Software
- Word Processor from scratch WYSIWYG with Web Assembly
- TIL the USA was supposed to adopt the metric system but the ship carrying the standardized meter and kilogram was hijacked by pirates in 1793 and the measurements never made it to the States
- A simple problem that isn't
- Software component names should be whimsical and cryptic
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A note from our sponsor - InfluxDB
www.influxdata.com | 25 Apr 2024
Index
What are some of the best open-source Scientific Computing projects in C++? This list will help you:
| Project | Stars | |
|---|---|---|
| 1 | mlpack | 4,797 |
| 2 | ArrayFire | 4,404 |
| 3 | matplotplusplus | 3,925 |
| 4 | NumCpp | 3,377 |
| 5 | FluidX3D | 3,193 |
| 6 | thread-pool | 1,924 |
| 7 | TileDB | 1,762 |
| 8 | casadi | 1,549 |
| 9 | mfem | 1,537 |
| 10 | ITK | 1,339 |
| 11 | Trilinos | 1,154 |
| 12 | Kratos | 955 |
| 13 | amgcl | 701 |
| 14 | VexCL | 695 |
| 15 | preCICE | 669 |
| 16 | ExprTK | 566 |
| 17 | avogadrolibs | 403 |
| 18 | blitz | 397 |
| 19 | feelpp | 293 |
| 20 | gdl | 261 |
| 21 | espresso | 215 |
| 22 | monolish | 189 |
| 23 | volesti | 139 |
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