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CUDA, which stands for Compute Unified Device Architecture, is a parallel computing platform and application programming interface (API) model created by NVIDIA. It allows software developers to use a CUDA-enabled graphics processing unit (GPU) for general purpose processing – an approach known as GPGPU. The CUDA platform is a software layer that gives direct access to the GPU's virtual instruction set and parallel computational elements.

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++ and Fortran. C/C++ programmers use 'CUDA C/C++', compiled with "nvcc", NVIDIA's LLVM-based C/C++ compiler. Fortran programmers can use 'CUDA Fortran', compiled with the PGI CUDA Fortran compiler from The Portland Group.

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 Shaders and C++ AMP. Third party wrappers are also available for Python, Perl, Fortran, Java, Ruby, Lua, Haskell, R, MATLAB, IDL, and native support in Mathematica.

In the computer game industry, GPUs are used not only for graphics rendering but also in 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 and a higher level API. 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. Nvidia states that programs developed for the G8x series will also work without modification on all future Nvidia video cards, due to binary compatibility.

Background

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

• push-relabel maximum flow algorithm
• fast sort algorithms of large lists
• two-dimensional fast wavelet transform
• molecular dynamics simulations

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 amongst 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

• CUDA does not support the full C standard, as it runs host code through a C++ compiler, which makes some valid C (but invalid C++) code fail to compile.
• 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).
• Unlike OpenCL, CUDA-enabled GPUs are only available from Nvidia
• No emulator or fallback functionality is available for modern revisions
• Valid C/C++ may sometimes be flagged and prevent compilation due to optimization techniques the compiler is required to employ to use limited resources.
• A single process must run spread across multiple disjoint memory spaces, unlike other C language runtime environments.
• C++ Run-Time Type Information (RTTI) is not supported in CUDA code, due to lack of support in the underlying hardware.
• Exception handling is not supported in CUDA code due to performance overhead that would be incurred with many thousands of parallel threads running.
• CUDA (with compute capability 2.x) allows a subset of C++ class functionality, for example member functions may not be virtual (this restriction will be removed in some future release).
• In single precision on first generation CUDA compute capability 1.x devices, denormal numbers are not supported and are instead flushed to zero, and the precisions of 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 previous faster gaming-grade math of compute capability 1.x devices if desired by setting compiler flags to disable accurate divisions, disable accurate square roots, and enable flushing denormal numbers to zero.

Supported GPUs

Compute capability table (version of CUDA supported) by GPU and card. Also available directly from Nvidia:

'*' - OEM-only products

A table of devices officially supporting CUDA:

Version features and specifications

For more information please visit this site: http://www.geeks3d.com/20100606/gpu-computing-nvidia-cuda-compute-capability-comparative-table/ and also read Nvidia CUDA programming guide.

Example

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

texture<float, 2, cudaReadModeElementType> tex;
void foo()
{
  cudaArray* cu_array;
  // Allocate array
  cudaChannelFormatDesc description = cudaCreateChannelDesc<float>();
  cudaMallocArray(&cu_array, &description, width, height);
  // Copy image data to array
  cudaMemcpyToArray(cu_array, image, width*height*sizeof(float), cudaMemcpyHostToDevice);
  // Set texture parameters (default)
  tex.addressMode = cudaAddressModeClamp;
  tex.addressMode = cudaAddressModeClamp;
  tex.filterMode = cudaFilterModePoint;
  tex.normalized = false; // do not normalize coordinates
  // Bind the array to the texture
  cudaBindTextureToArray(tex, cu_array);
  // Run kernel
  dim3 blockDim(16, 16, 1);
  dim3 gridDim((width + blockDim.x - 1)/ blockDim.x, (height + blockDim.y - 1) / blockDim.y, 1);
  kernel<<< gridDim, blockDim, 0 >>>(d_data, height, width);
  // Unbind the array from the texture
  cudaUnbindTexture(tex);
} //end foo()
__global__ void kernel(float* odata, int height, int width)
{
   unsigned int x = blockIdx.x*blockDim.x + threadIdx.x;
   unsigned int y = blockIdx.y*blockDim.y + threadIdx.y;
   if (x < width && y < height) {
      float c = tex2D(tex, x, y);
      odata = c;
   }
}

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.

import pycuda.compiler as comp
import pycuda.driver as drv
import numpy
import pycuda.autoinit
mod = comp.SourceModule("""
__global__ void multiply_them(float *dest, float *a, float *b)
{
  const int i = threadIdx.x;
  dest = a * b;
}
""")
multiply_them = mod.get_function("multiply_them")
a = numpy.random.randn(400).astype(numpy.float32)
b = numpy.random.randn(400).astype(numpy.float32)
dest = numpy.zeros_like(a)
multiply_them(
        drv.Out(dest), drv.In(a), drv.In(b),
        block=(400,1,1))
print dest-a*b

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

import numpy
from pycublas import CUBLASMatrix
A = CUBLASMatrix( numpy.mat(],],numpy.float32) )
B = CUBLASMatrix( numpy.mat(],,],numpy.float32) )
C = A*B
print C.np_mat()

Language bindings

• Common Lisp - cl-cuda
• Fortran – FORTRAN CUDA, PGI CUDA Fortran Compiler
• F# - Alea.CUDA
• Haskell – Data.Array.Accelerate
• IDL – GPULib
• Java – jCUDA, JCuda, JCublas, JCufft, CUDA4J
• Lua – KappaCUDA
• Mathematica – CUDALink
• MATLAB – Parallel Computing Toolbox, MATLAB Distributed Computing Server, and 3rd party packages like Jacket.
• .NET – CUDA.NET, Managed CUDA, CUDAfy.NET .NET kernel and host code, CURAND, CUBLAS, CUFFT
• Perl – KappaCUDA, CUDA::Minimal
• Python – Numba, NumbaPro, PyCUDA, KappaCUDA, Theano
• Ruby – KappaCUDA
• R – gputools

Current and future usages of CUDA architecture

• Accelerated rendering of 3D graphics
• Accelerated interconversion of video file formats
• Accelerated encryption, decryption and compression
• 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, in particular in fluid dynamics.
• Neural network training in machine learning problems
• Distributed computing
• Molecular dynamics
• Mining cryptocurrencies

See also

• Allinea DDT - A debugger for CUDA, OpenACC, and parallel applications
• OpenCL - A standard for programming a variety of platforms, including GPUs
• BrookGPU – the Stanford University graphics group's compiler
• Array programming
• Parallel computing
• Stream processing
• rCUDA – An API for computing on remote computers
• Molecular modeling on GPU

References

1. Shimpi, Anand Lal (November 8, 2006). "NVIDIA's GeForce 8800 (G80): GPUs Re-architected for DirectX 10". AnandTech. Retrieved May 16, 2015. {{cite web}}: Unknown parameter |coauthors= ignored (|author= suggested) (help)
2. NVIDIA CUDA Home Page
3. Abi-Chahla, Fedy (June 18, 2008). "Nvidia's CUDA: The End of the CPU?". Tom's Hardware. Retrieved May 17, 2015.
4. CUDA LLVM Compiler
5. First OpenCL demo on a GPU on YouTube
6. DirectCompute Ocean Demo Running on Nvidia CUDA-enabled GPU on YouTube
7. Giorgos Vasiliadis, Spiros Antonatos, Michalis Polychronakis, Evangelos P. Markatos and Sotiris Ioannidis (September 2008). "Gnort: High Performance Network Intrusion Detection Using Graphics Processors" (PDF). Proceedings of the 11th International Symposium on Recent Advances in Intrusion Detection (RAID).{{cite journal}}: CS1 maint: multiple names: authors list (link)
8. Schatz, M.C., Trapnell, C., Delcher, A.L., Varshney, A. (2007). "High-throughput sequence alignment using Graphics Processing Units". BMC Bioinformatics. 8:474: 474. doi:10.1186/1471-2105-8-474. PMC 2222658. PMID 18070356.{{cite journal}}: CS1 maint: multiple names: authors list (link) CS1 maint: unflagged free DOI (link)
9. Manavski, Svetlin A.; Giorgio Valle (2008). "CUDA compatible GPU cards as efficient hardware accelerators for Smith-Waterman sequence alignment". BMC Bioinformatics. 9: S10. doi:10.1186/1471-2105-9-S2-S10. PMC 2323659. PMID 18387198.{{cite journal}}: CS1 maint: unflagged free DOI (link)
10. Pyrit – Google Code https://code.google.com/p/pyrit/
11. Use your Nvidia GPU for scientific computing, BOINC official site (December 18, 2008)
12. Nvidia CUDA Software Development Kit (CUDA SDK) – Release Notes Version 2.0 for MAC OS X
13. CUDA 1.1 – Now on Mac OS X- (Posted on Feb 14, 2008)
14. Silberstein, Mark; Schuster, Assaf; Geiger, Dan; Patney, Anjul; Owens, John D. (2008). Efficient computation of sum-products on GPUs through software-managed cache. Proceedings of the 22nd annual international conference on Supercomputing - ICS '08. pp. 309–318. doi:10.1145/1375527.1375572. ISBN 978-1-60558-158-3.
15. NVCC forces c++ compilation of .cu files
16. C++ keywords on CUDA C code
17. ^ "CUDA-Enabled Products". CUDA Zone. Nvidia Corporation. Retrieved 2008-11-03.
18. Whitehead, Nathan; Fit-Florea, Alex. "Precision & Performance: Floating Point and IEEE 754 Compliance for NVIDIA GPUs" (PDF). Nvidia. Retrieved November 18, 2014.
19. Cores perform only single-precision floating-point arithmetics. There is 1 double-precision floating-point unit.
20. No more than one scheduler can issue 2 instructions at once. The first scheduler is in charge of the warps with an odd ID and the second scheduler is in charge of the warps with an even ID.
21. Template:PDFlink, Page 148 of 175 (Version 5.0 October 2012)
22. PyCUDA
23. pycublas
24. "MATLAB Adds GPGPU Support". 2010-09-20.

External links

• Official website
• CUDA Community on Google+
• A little tool to adjust the VRAM size

• Computer physics engines
• GPGPU
• GPGPU libraries
• Graphics hardware
• Nvidia software
• Parallel computing
• Video cards
• Video game hardware