CUDA

CUDA is a technology that enable you to use the computing resource in GPU(Graphics processing Unit) to compute. This technology is given by NVIDIA.
The following is from wiki

CUDA (originally Compute Unified Device Architecture although this is no longer used) is a parallel computing architecture developed by NVIDIA. Simply put, CUDA is the compute engine in NVIDIA graphics processing units or GPUs, that is accessible to software developers through industry standard programming languages. Programmers use C for CUDA, compiled through a PathScale Open64 C compiler,^[1] to code algorithms for execution on the GPU. CUDA is architected to support all computational interfaces, including C and new open standards like OpenCL and DX11 Compute. Third party wrappers are also available for Python, Fortran and Java.

The latest drivers all contain the necessary CUDA components. CUDA works with all NVIDIA GPUs from the G8X series onwards, including GeForce, Quadro and the Tesla line. NVIDIA states that programs developed for the GeForce 8 series will also work without modification on all future Nvidia video cards, due to binary compatibility. CUDA gives developers access to the native instruction set and memory of the parallel computational elements in CUDA GPUs. Using CUDA, the latest NVIDIA GPUs effectively become open architectures like CPUs (Central Processing Units). Unlike CPUs however, GPUs have a parallel "many-core" architecture, each core capable of running thousands of threads simultaneously - if an application is suited to this kind of an architecture, the GPU can offer large performance benefits.

In the computer gaming industry, in addition to graphics rendering, graphics cards are used in game physics calculations (physical effects like debris, smoke, fire, fluids), an example being PhysX and Bullet (software). CUDA has also been used to accelerate non-graphical applications in computational biology, cryptography and other fields by an order of magnitude or more.^[2][3][4]

CUDA provides both a low level API and a higher level API. The initial CUDA SDK was made public 15th February 2007. NVIDIA has released versions of the CUDA API for Microsoft Windows, Linux and Mac OS X was added as a fully supported platform in version 2.0^[5], which supersedes the beta released February 14, 2008.^[6]

Advantages

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

• It uses the standard C language, with some simple extensions.
• Scattered reads – code can read to arbitrary addresses in memory.
• Shared memory – CUDA exposes a fast shared memory region (16KB in size) that can be shared amongst threads. This can be used as a user-managed cache, enabling higher bandwidth than is possible using texture lookups.^[7]
• Faster downloads and readbacks to and from the GPU
• Full support for integer and bitwise operations, including integer texture lookups.

Limitations

• It uses a recursion-free subset of C language, plus some simple extensions. However, a single process must run spread across multiple disjoint memory spaces, unlike other C language runtime environments.
• Texture rendering is not supported.
• Recursive functions are not supported and must be converted to loops.
• For double precision there are no deviations from the IEEE 754 standard. In single precision, Denormals and signalling NaNs are not supported; only two IEEE rounding modes are supported (chop and round-to-nearest even), and those are specified on a per-instruction basis rather than in a control word (whether this is a limitation is arguable); and the precision of division/square root is slightly lower than single precision.
• The bus bandwidth and latency between the CPU and the GPU may be a bottleneck.
• Threads should be run 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 impact 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 ray tracing acceleration data structure).
• CUDA-enabled GPUs are only available from NVIDIA (GeForce 8 series and above, Quadro and Tesla)^[8]