Developer(s) Nvidia Corporation
Stable release 4.0 / May 17 2011; 6 months ago (May 17 2011)
Operating system Windows XP and later
Mac OS X
Platform See Supported GPUs
License Freeware
Website home new.html

CUDA or Compute Unified Device Architecture[1] is a parallel computing architecture developed by Nvidia. CUDA is the computing engine in Nvidia graphics processing units (GPUs) that is accessible to software developers through variants of industry standard programming languages. Programmers use 'C for CUDA' (C with Nvidia extensions and certain restrictions), compiled through a PathScale Open64 C compiler,[2] to code algorithms for execution on the GPU. CUDA architecture shares a range of computational interfaces with two competitors -the Khronos Group's OpenCL[3] and Microsoft's DirectCompute.[4] Third party wrappers are also available for Python, Perl, Fortran, Java, Ruby, Lua, MATLAB, and IDL, and native support exists in Mathematica.

CUDA gives developers access to the virtual instruction set and memory of the parallel computational elements in CUDA GPUs. Using CUDA, the latest Nvidia GPUs become accessible for computation like CPUs. Unlike CPUs however, GPUs have a parallel throughput architecture that emphasizes executing many concurrent threads slowly, rather than executing a single thread very quickly. This approach of solving general purpose problems on GPUs is known as GPGPU.

In the computer game industry, in addition to graphics rendering, GPUs are used in game physics calculations (physical effects like 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.[5][6][7][8] An example of this is the BOINC distributed computing client.[9]

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,[10] which supersedes the beta released February 14, 2008.[11] 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.

Example of CUDA processing flow
1. Copy data from main mem to GPU mem
2. CPU instructs the process to GPU
3. GPU execute parallel in each core
4. Copy the result from GPU mem to main mem



The GPU, as a specialized processor, addresses the demands of real-time high-resolution 3D graphics compute-intensive tasks. As of 2011 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:

For instance, the parallel nature of molecular dynamics simulations is suitable for CUDA implementation.[12]


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
  • Shared memory – CUDA exposes a fast shared memory region (up to 48KB per Multi-Processor) that can be shared amongst threads. This can be used as a user-managed cache, enabling higher bandwidth than is possible using texture lookups.[13]
  • Faster downloads and readbacks to and from the GPU
  • Full support for integer and bitwise operations, including integer texture lookups


  • Texture rendering is not supported (CUDA 3.2 and up addresses this by introducing "surface writes" to cuda Arrays, the underlying opaque data structure).
  • 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 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 space partitioning data structure during ray tracing).
  • Unlike OpenCL, CUDA-enabled GPUs are only available from Nvidia[14]
  • Valid C/C++ may sometimes be flagged and prevent compilation due to optimization techniques the compiler is required to employ to use limited resources.
  • CUDA (with compute capability 1.x) uses a recursion-free, function-pointer-free subset of the C language, plus some simple extensions. However, a single process must run spread across multiple disjoint memory spaces, unlike other C language runtime environments.
  • 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). [See CUDA C Programming Guide 3.1 - Appendix D.6]
  • Double precision (CUDA compute capability 1.3 and above[15]) deviate from the IEEE 754 standard: round-to-nearest-even is the only supported rounding mode for reciprocal, division, and square root. 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; and the precision of division/square root is slightly lower than single precision.

Supported GPUs

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

GPUs Cards
1.0 G80, G92, G92b, G94, G94b GeForce 8800GTX/Ultra, 9400GT, 9600GT, 9800GT, Tesla C/D/S870, FX4/5600, 360M, GT 420
1.1 G86, G84, G98, G96, G96b, G94, G94b, G92, G92b GeForce 8400GS/GT, 8600GT/GTS, 8800GT/GTS, 9600 GSO, 9800GTX/GX2, GTS 250, GT 120/30/40, FX 4/570, 3/580, 17/18/3700, 4700x2, 1xxM, 32/370M, 3/5/770M, 16/17/27/28/36/37/3800M, NVS420/50
1.2 GT218, GT216, GT215 GeForce 210, GT 220/40, FX380 LP, 1800M, 370/380M, NVS 2/3100M
1.3 GT200, GT200b GeForce GTX 260, GTX 275, GTX 280, GTX 285, GTX 295, Tesla C/M1060, S1070, Quadro CX, FX 3/4/5800
2.0 GF100, GF110 GeForce (GF100) GTX 465, GTX 470, GTX 480, Tesla C2050, C2070, S/M2050/70, Quadro Plex 7000, GeForce (GF110) GTX570, GTX580, GTX590
2.1 GF104, GF114, GF116, GF108, GF106 GeForce GT 430, GT 440, GTS 450, GTX 460, GTX 550 Ti, GTX 560, GTX 560 Ti, 500M, Quadro 600, 2000, 4000, 5000, 6000

A table of devices officially supporting CUDA (Note that many applications require at least 256 MB of dedicated VRAM, and some recommend at least 96 cuda cores).[14]

see full list here:

Nvidia GeForce
GeForce GTX 590
GeForce GTX 580
GeForce GTX 570
GeForce GTX 560 Ti
GeForce GTX 560
GeForce GTX 550 Ti
GeForce GTX 480
GeForce GTX 470
GeForce GTX 465
GeForce GTX 460
GeForce GTX 460 SE
GeForce GTS 450
GeForce GT 440
GeForce GT 430
GeForce GT 420
GeForce GTX 295
GeForce GTX 285
GeForce GTX 280
GeForce GTX 275
GeForce GTX 260
GeForce GTS 250
GeForce GTS 240
GeForce GT 240
GeForce GT 220
GeForce 210/G210
GeForce GT 140
GeForce 9800 GX2
GeForce 9800 GTX+
GeForce 9800 GTX
GeForce 9800 GT
GeForce 9600 GSO
GeForce 9600 GT
GeForce 9500 GT
GeForce 9400 GT
GeForce 9400 mGPU
GeForce 9300 mGPU
GeForce 9100 mGPU
GeForce 8800 Ultra
GeForce 8800 GTX
GeForce 8800 GTS
GeForce 8800 GT
GeForce 8800 GS
GeForce 8600 GTS
GeForce 8600 GT
GeForce 8600 mGT
GeForce 8500 GT
GeForce 8400 GS
GeForce 8300 mGPU
GeForce 8200 mGPU
GeForce 8100 mGPU
Nvidia GeForce Mobile
GeForce GTX 580M
GeForce GTX 570M
GeForce GTX 560M
GeForce GT 555M
GeForce GT 550M
GeForce GT 540M
GeForce GT 525M
GeForce GT 520M
GeForce GTX 480M
GeForce GTX 470M
GeForce GTX 460M
GeForce GT 445M
GeForce GT 435M
GeForce GT 425M
GeForce GT 420M
GeForce GT 415M
GeForce GTX 285M
GeForce GTX 280M
GeForce GTX 260M
GeForce GTS 360M
GeForce GTS 350M
GeForce GTS 260M
GeForce GTS 250M
GeForce GT 335M
GeForce GT 330M
GeForce GT 325M
GeForce GT 320M
GeForce 310M
GeForce GT 240M
GeForce GT 230M
GeForce GT 220M
GeForce G210M
GeForce GTS 160M
GeForce GTS 150M
GeForce GT 130M
GeForce GT 120M
GeForce G110M
GeForce G105M
GeForce G103M
GeForce G102M
GeForce G100
GeForce 9800M GTX
GeForce 9800M GTS
GeForce 9800M GT
GeForce 9800M GS
GeForce 9700M GTS
GeForce 9700M GT
GeForce 9650M GT
GeForce 9650M GS
GeForce 9600M GT
GeForce 9600M GS
GeForce 9500M GS
GeForce 9500M G
GeForce 9400M G
GeForce 9300M GS
GeForce 9300M G
GeForce 9200M GS
GeForce 9100M G
GeForce 8800M GTX
GeForce 8800M GTS
GeForce 8700M GT
GeForce 8600M GT
GeForce 8600M GS
GeForce 8400M GT
GeForce 8400M GS
GeForce 8400M G
GeForce 8200M G
Nvidia Quadro
Quadro 6000
Quadro 5000
Quadro 4000
Quadro 2000
Quadro 600
Quadro FX 5800
Quadro FX 5600
Quadro FX 4800
Quadro FX 4700 X2
Quadro FX 4600
Quadro FX 3800
Quadro FX 3700
Quadro FX 1800
Quadro FX 1700
Quadro FX 580
Quadro FX 570
Quadro FX 380
Quadro FX 370
Quadro NVS 450
Quadro NVS 420
Quadro NVS 295
Quadro NVS 290
Quadro Plex 1000 Model IV
Quadro Plex 1000 Model S4
Nvidia Quadro Mobile
Quadro 5010M
Quadro 5000M
Quadro 4000M
Quadro 3000M
Quadro 2000M
Quadro 1000M
Quadro FX 3800M
Quadro FX 3700M
Quadro FX 3600M
Quadro FX 2800M
Quadro FX 2700M
Quadro FX 1800M
Quadro FX 1700M
Quadro FX 1600M
Quadro FX 880M
Quadro FX 770M
Quadro FX 570M
Quadro FX 380M
Quadro FX 370M
Quadro FX 360M
Quadro NVS 320M
Quadro NVS 160M
Quadro NVS 150M
Quadro NVS 140M
Quadro NVS 135M
Quadro NVS 130M
Nvidia Tesla
Tesla C2050/2070
Tesla M2050/M2070
Tesla S2050
Tesla S1070
Tesla M1060
Tesla C1060
Tesla C870
Tesla D870
Tesla S870

Version features and specifications

Feature support (unlisted features are
supported for all compute capabilities)
Compute capability (version)
1.0 1.1 1.2 1.3 2.x
Integer atomic functions operating on
32-bit words in global memory
No Yes
atomicExch() operating on 32-bit
floating point values in global memory
Integer atomic functions operating on
32-bit words in shared memory
No Yes
atomicExch() operating on 32-bit
floating point values in shared memory
Integer atomic functions operating on
64-bit words in global memory
Warp vote functions
Double-precision floating-point operations No Yes
Atomic functions operating on 64-bit
integer values in shared memory
No Yes
Floating-point atomic addition operating on
32-bit words in global and shared memory
Surface functions
3D grid of thread block
Technical specifications Compute capability (version)
1.0 1.1 1.2 1.3 2.x
Maximum dimensionality of grid of thread blocks 2 3
Maximum x-, y-, or z-dimension of a grid of thread blocks 65535
Maximum dimensionality of thread block 3
Maximum x- or y-dimension of a block 512 1024
Maximum z-dimension of a block 64
Maximum number of threads per block 512 1024
Warp size 32
Maximum number of resident blocks per multiprocessor 8
Maximum number of resident warps per multiprocessor 24 32 48
Maximum number of resident threads per multiprocessor 768 1024 1536
Number of 32-bit registers per multiprocessor 8 K 16 K 32 K
Maximum amount of shared memory per multiprocessor 16 KB 48 KB
Number of shared memory banks 16 32
Amount of local memory per thread 16 KB 512 KB
Constant memory size 64 KB
Cache working set per multiprocessor for constant memory 8 KB
Cache working set per multiprocessor for texture memory Device dependent, between 6 KB and 8 KB
Maximum width for 1D texture
reference bound to a CUDA array
8192 32768
Maximum width for 1D texture
reference bound to linear memory
Maximum width and number of layers
for a 1D layered texture reference
8192 x 512 16384 x 2048
Maximum width and height for 2D
texture reference bound to
linear memory or a CUDA array
65536 x 32768 65536 x 65535
Maximum width, height, and number
of layers for a 2D layered texture reference
8192 x 8192 x 512 16384 x 16384 x 2048
Maximum width, height and depth
for a 3D texture reference bound to linear
memory or a CUDA array
2048 x 2048 x 2048
Maximum number of textures that
can be bound to a kernel
Maximum width for a 1D surface
reference bound to a CUDA array
Maximum width and height for a 2D
surface reference bound to a CUDA array
8192 x 8192
Maximum number of surfaces that
can be bound to a kernel
Maximum number of instructions per
2 million
Architecture specifications Compute capability (version)
1.0 1.1 1.2 1.3 2.0 2.1
Number of cores for integer and floating-point arithmetic functions operations 8[16] 32 48
Number of special function units for single-precision floating-point transcendental functions 2 4 8
Number of texture filtering units for every texture address unit or Render Output Unit (ROP)  ? 4 8
Number of warp schedulers 1 2 2
Number of instructions issued at once by scheduler 1 1 2[17]

For more information please visit this site: and also read Nvidia CUDA programming guide.[18]


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[0] = cudaAddressModeClamp;
  tex.addressMode[1] = 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
} //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[y*width+x] = 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[i] = a[i] * b[i];
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)
        drv.Out(dest), drv.In(a), drv.In(b),
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([[1,2,3],[4,5,6]],numpy.float32) )
B = CUBLASMatrix( numpy.mat([[2,3],[4,5],[6,7]],numpy.float32) )
C = A*B
print C.np_mat()

Language bindings

Current CUDA architectures

The current generation CUDA architecture (codename: "Fermi") which is standard on Nvidia's released (GeForce 400 Series [GF100] (GPU) 2010-03-27)[20] GPU is designed from the ground up to natively support more programming languages such as C++. It has eight times the peak double-precision floating-point performance compared to Nvidia's previous-generation Tesla GPU. It also introduced several new features[21] including:

  • up to 1024 CUDA cores and 3.0 billion transistors on the GTX 590
  • Nvidia Parallel DataCache technology
  • Nvidia GigaThread engine
  • ECC memory support
  • Native support for Visual Studio

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.
  • Real Time Cloth Simulation - Real Time Cloth Simulation
  • The Search for Extra-Terrestrial Intelligence (SETI@Home) program[22][23]

See also


  1. ^ NVIDIA CUDA Programming Guide Version 1.0
  2. ^ NVIDIA Clears Water Muddied by Larrabee Shane McGlaun (Blog) - August 5, 2008 - DailyTech
  3. ^ First OpenCL demo on a GPU on YouTube
  4. ^ DirectCompute Ocean Demo Running on NVIDIA CUDA-enabled GPU on YouTube
  5. ^ Giorgos Vasiliadis, Spiros Antonatos, Michalis Polychronakis, Evangelos P. Markatos and Sotiris Ioannidis (September 2008, Boston, MA, USA). "Gnort: High Performance Network Intrusion Detection Using Graphics Processors" (PDF). Proceedings of the 11th International Symposium on Recent Advances in Intrusion Detection (RAID). 
  6. ^ 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. 
  7. ^ Manavski, Svetlin A.; Giorgio Valle (2008). "CUDA compatible GPU cards as efficient hardware accelerators for Smith-Waterman sequence alignment". BMC Bioinformatics 9(Suppl 2):S10: S10. doi:10.1186/1471-2105-9-S2-S10. PMC 2323659. PMID 18387198. 
  8. ^ Pyrit - Google Code
  9. ^ Use your NVIDIA GPU for scientific computing, BOINC official site (December 18, 2008)
  10. ^ NVIDIA CUDA Software Development Kit (CUDA SDK) - Release Notes Version 2.0 for MAC OSX
  11. ^ CUDA 1.1 - Now on Mac OS X- (Posted on Feb 14, 2008)
  12. ^ Stephen Zunes (1999). Nonviolent social movements: a geographical perspective. Wiley-Blackwell. p. 47. ISBN 9781577180760. Retrieved 10 May 2011. 
  13. ^ Silberstein, Mark (2007). "Efficient computation of Sum-products on GPUs" (PDF). 
  14. ^ a b "CUDA-Enabled Products". CUDA Zone. Nvidia Corporation. Retrieved 2008-11-03. 
  15. ^ CUDA and double precision floating point numbers
  16. ^ Cores perform only single-precision floating-point arithmetics. There is 1 double-precision floating-point unit.
  17. ^ 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.
  18. ^ Appendix F. Features and Technical SpecificationsPDF (3.2 MiB), Page 158 of 173 (Version 4.0 2011-05-06)
  19. ^ "MATLAB Adds GPGPU Support". 2010-09-20. 
  20. ^ broadcast about Nvidia GeForce GTX 470 and 480
  21. ^ The Current Generation CUDA Architecture, Code Named Fermi
  22. ^
  23. ^

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