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OpenMM has a C++ API as well as a Python wrapper.Developers are able to customize force fields as well as integrators for low-level simulation control. Users who only require high-level control of their simulations can use built-in force fields (consisting of many commonly used force fields) and built in integrators like Langevin, Verlet, Nosé–Hoover, and Brownian.
CuPy is an open source library for GPU-accelerated computing with Python programming language, providing support for multi-dimensional arrays, sparse matrices, and a variety of numerical algorithms implemented on top of them. [3]
CUDA code runs on both the central processing unit (CPU) and graphics processing unit (GPU). NVCC separates these two parts and sends host code (the part of code which will be run on the CPU) to a C compiler like GNU Compiler Collection (GCC) or Intel C++ Compiler (ICC) or Microsoft Visual C++ Compiler, and sends the device code (the part which will run on the GPU) to the GPU.
macOS i386, x86-64 and AArch64, iOS, Linux, FreeBSD, NetBSD, Windows: No ? Apache License v2.0 with LLVM Exceptions 16.0.5, 2 June 2023 mdb: 1999 Solaris standard debugger (adb) successor Any compiled to machine code: Solaris: Yes (Memory debugger) ? CDDL: 2016 TotalView: 1998 Source code and memory serial and parallel debugger C++, C, CUDA ...
Numba can compile Python functions to GPU code. Initially two backends are available: Nvidia CUDA, see numba.pydata.org /numba-doc /dev /cuda; AMD ROCm HSA, see numba.pydata.org /numba-doc /dev /roc; Since release 0.56.4, [2] AMD ROCm HSA has been officially moved to unmaintained status and a separate repository stub has been created for it.
CUDA 9.0–9.2 comes with these other components: CUTLASS 1.0 – custom linear algebra algorithms, NVIDIA Video Decoder was deprecated in CUDA 9.2; it is now available in NVIDIA Video Codec SDK; CUDA 10 comes with these other components: nvJPEG – Hybrid (CPU and GPU) JPEG processing; CUDA 11.0–11.8 comes with these other components: [19 ...
C, Java, C#, Fortran, Python 1970 many components Not free Proprietary: General purpose numerical analysis library. Math.NET Numerics: C. Rüegg, M. Cuda, et al. C#, F#, C, PowerShell 2009 4.7.0, November 2018 Free MIT/X11: General purpose numerical analysis and statistics library for the .NET framework and Mono, with optional support for ...
These programs are written in CUDA C or directly in PTX code and are linked together when used by the OptiX engine. In order to use OptiX a CUDA-capable GPU must be available on the system and the CUDA toolkit must be installed. Using the OptiX engine in a ray tracing application usually involves the following steps: