CUDA

Graphics processing unit

The graphics processing unit (GPU) is a specialized processor. It addresses the demands of real-time high-resolution, compute-intensive tasks such as 3D graphics. By 2012, GPUs had evolved into highly parallel multi-core systems allowing efficient manipulation of large blocks of data. This design is more effective than general-purpose central processing unit (CPUs) for algorithms in situations where processing large blocks of data is done in parallel, such as:

cryptographic hash functions

machine learning

molecular dynamics simulations

physics engines

History

CUDA traces to the early 2000s, when Ian Buck, a computer science PhD student at Stanford University, began experimenting with using GPUs for purposes beyond rendering graphics. Buck had become interested in GPUs during his undergraduate studies at Princeton University, initially through video gaming. After graduation, he interned at Nvidia, gaining deeper exposure to GPU architecture. At Stanford, he built an 8K gaming rig using 32 GeForce graphics cards, originally to push the limits of graphics performance in games like Quake and Doom. However, his interests shifted toward exploring the potential of GPUs for general-purpose parallel computing.

To that end, Buck developed Brook, a programming language designed to enable general-purpose computing on GPUs. His work attracted support from Nvidia and the Defense Advanced Research Projects Agency (DARPA). In 2004, Nvidia hired Buck and paired him with John Nickolls, then director of architecture for GPU computing. Together, they began transforming Brook into CUDA. CUDA was officially released in 2007.

CUDA became central to the company's strategy of positioning GPUs as versatile hardware for scientific applications. By 2015, CUDA's development increasingly focused on accelerating machine learning and artificial neural network workloads.

Ontology

The following table offers an approximate summary of the CUDA ontology.

Programming abilities

The CUDA platform is accessible to software developers through CUDA-accelerated libraries, compiler directives such as OpenACC, and extensions to industry-standard programming languages including C, C++, Fortran and Python. C/C++ programmers can use 'CUDA C/C++', compiled to PTX with nvcc (Nvidia's LLVM-based C/C++ compiler) or by clang itself. Fortran programmers can use 'CUDA Fortran', compiled with the PGI CUDA Fortran compiler from The Portland Group. Python programmers can use the cuPyNumeric library to accelerate applications on Nvidia GPUs.

In addition to libraries, compiler directives, CUDA C/C++ and CUDA Fortran, the CUDA platform supports other computational interfaces, including the Khronos Group's OpenCL, Microsoft's DirectCompute, OpenGL Compute Shader and C++ AMP. Third party wrappers are also available for Python, Perl, Fortran, Java, Ruby, Lua, Common Lisp, Haskell, R, MATLAB, IDL, Julia, and native support in Mathematica.

In the computer game industry, GPUs are used for graphics rendering, and for game physics calculations (physical effects such as debris, smoke, fire, fluids); examples include PhysX and Bullet. CUDA has also been used to accelerate non-graphical applications in computational biology, cryptography and other fields by an order of magnitude or more.

CUDA provides both a low level API (CUDA Driver API, non single-source) and a higher level API (CUDA Runtime API, single-source). The initial CUDA SDK was made public on 15 February 2007, for Microsoft Windows and Linux. Mac OS X support was later added in version 2.0, which supersedes the beta released February 14, 2008. CUDA works with all Nvidia GPUs from the G8x series onwards, including GeForce, Quadro and the Tesla line. CUDA is compatible with most standard operating systems.

CUDA 8.0 comes with the following libraries (for compilation & runtime, in alphabetical order):

cuBLAS – CUDA Basic Linear Algebra Subroutines library

CUDART – CUDA Runtime library

cuFFT – CUDA Fast Fourier Transform library

Advantages

CUDA has several advantages over traditional general-purpose computation on GPUs (GPGPU) using graphics APIs:

Scattered reads – code can read from arbitrary addresses in memory

Unified virtual memory (CUDA 4.0 and above)

Unified memory (CUDA 6.0 and above)

Shared memory – CUDA exposes a fast shared memory region that can be shared among threads. This can be used as a user-managed cache, enabling higher bandwidth than is possible using texture lookups.

Faster downloads and readbacks to and from the GPU

Full support for integer and bitwise operations, including integer texture lookups

Limitations

Whether for the host computer or the GPU device, all CUDA source code is now processed according to C++ syntax rules. This was not always the case. Earlier versions of CUDA were based on C syntax rules. As with the more general case of compiling C code with a C++ compiler, it is therefore possible that old C-style CUDA source code will either fail to compile or will not behave as originally intended.

Interoperability with rendering languages such as OpenGL is one-way, with OpenGL having access to registered CUDA memory but CUDA not having access to OpenGL memory.

Copying between host and device memory may incur a performance hit due to system bus bandwidth and latency (this can be partly alleviated with asynchronous memory transfers, handled by the GPU's DMA engine).

Threads should be running in groups of at least 32 for best performance, with total number of threads numbering in the thousands. Branches in the program code do not affect performance significantly, provided that each of 32 threads takes the same execution path; the SIMD execution model becomes a significant limitation for any inherently divergent task (e.g. traversing a space partitioning data structure during ray tracing).

No emulation or fallback functionality is available for modern revisions.

Valid C++ may sometimes be flagged and prevent compilation due to the way the compiler approaches optimization for target GPU device limitations.

C++ run-time type information (RTTI) and C++-style exception handling are only supported in host code, not in device code.

In single-precision on first generation CUDA compute capability 1.x devices, denormal numbers are unsupported and are instead flushed to zero, and the precision of both the division and square root operations are slightly lower than IEEE 754-compliant single precision math. Devices that support compute capability 2.0 and above support denormal numbers, and the division and square root operations are IEEE 754 compliant by default. However, users can obtain the prior faster gaming-grade math of compute capability 1.x devices if desired by setting compiler flags to disable accurate divisions and accurate square roots, and enable flushing denormal numbers to zero.

Example

This example code in C++ loads a texture from an image into an array on the GPU:

Below is an example given in Python that computes the product of two arrays on the GPU. The unofficial Python language bindings can be obtained from PyCUDA.

Additional Python bindings to simplify matrix multiplication operations can be found in the program pycublas.

while CuPy directly replaces NumPy:

GPUs supported

Note on notation: compute capability X.Y is also written as SMXY or sm_XY (e.g. 10.3 as SM103 or sm_103) in professional Nvidia software and the code Nvidia has contributed to LLVM.

Below is a table of supported CUDA compute capabilities based on the CUDA SDK version and microarchitecture, listed by code name:

Note: CUDA SDK 10.2 is the last official release for macOS, as support will not be available for macOS in newer releases.

CUDA compute capability by version with associated GPU semiconductors and GPU card models (separated by their various application areas):

* – OEM-only products

Version features and specifications

Note: A GPU with a higher compute capacity is able to execute PTX code meant for a GPU of a lower range of compute capacities. However, it is possible to compile CUDA code into a form that only works on one family (same "X") of GPUs; if existing code is compiled this way, recompilation will be needed for it to work on a newer GPU.

Data types

Note: Any missing lines or empty entries do reflect some lack of information on that exact item.

Tensor cores

Note: Any missing lines or empty entries do reflect some lack of information on that exact item.

Multiprocessor architecture

For more information read the Nvidia CUDA C++ Programming Guide.

Usages of CUDA architecture

Accelerated rendering of 3D graphics

Accelerated interconversion of video file formats

Accelerated encryption, decryption and compression

Bioinformatics, e.g. NGS DNA sequencing BarraCUDA

Distributed calculations, such as predicting the native conformation of proteins

Medical analysis simulations, for example virtual reality based on CT and MRI scan images

Physical simulations, particularly in fluid dynamics

Neural network training in machine learning problems

Large Language Model inference

Face recognition

Volunteer computing projects, such as SETI@home and other projects using BOINC software

Molecular dynamics

Mining cryptocurrencies

Structure from motion (SfM) software

Comparison with competitors

CUDA competes with other GPU computing stacks: Intel OneAPI and AMD ROCm.

Whereas Nvidia's CUDA is closed-source, Intel's OneAPI and AMD's ROCm are open source.

Intel OneAPI

oneAPI is an initiative based in open standards, created to support software development for multiple hardware architectures. The oneAPI libraries must implement open specifications that are discussed publicly by the Special Interest Groups, offering the possibility for any developer or organization to implement their own versions of oneAPI libraries.

Originally made by Intel, other hardware adopters include Fujitsu and Huawei.

Unified Acceleration Foundation (UXL) is a new technology consortium working on the continuation of the OneAPI initiative, with the goal to create a new open standard accelerator software ecosystem, related open standards and specification projects through Working Groups and Special Interest Groups (SIGs). The goal is to offer open alternatives to Nvidia's CUDA. The main companies behind it are Intel, Google, ARM, Qualcomm, Samsung, Imagination, and VMware.

AMD ROCm

ROCm is an open source software stack for graphics processing unit (GPU) programming from Advanced Micro Devices (AMD).