cuda
CUDA
A parallel computing platform and programming model
| |
| Developer(s) | Nvidia Corporation |
|---|---|
| Initial release | June 23, 2007 |
| Stable release |
9.0 / September 25, 2017
|
| Operating system | Windows XP and later, macOS, Linux |
| Platform | Supported GPUs |
| Type | GPGPU |
| License | Freeware |
| Website | developer |
CUDA is a parallel computing platform and application programming interface (API) model created by Nvidia.[1] It allows software developers and software engineers to use a CUDA-enabled graphics processing unit (GPU) for general purpose processing – an approach termed GPGPU (General-Purpose computing on Graphics Processing Units). The CUDA platform is a software layer that gives direct access to the GPU's virtual instruction set and parallel computational elements, for the execution of compute kernels.[2]
The CUDA platform is designed to work with programming languages such as C, C++, and Fortran. This accessibility makes it easier for specialists in parallel programming to use GPU resources, in contrast to prior APIs like Direct3D and OpenGL, which required advanced skills in graphics programming. Also, CUDA supports programming frameworks such as OpenACC and OpenCL.[2] When it was first introduced by Nvidia, the name CUDA was an acronym for Compute Unified Device Architecture,[3] but Nvidia subsequently dropped the use of the acronym.
Contents
[hide]Background[edit]
The graphics processing unit (GPU), as a specialized computer processor, addresses the demands of real-time high-resolution 3D graphics compute-intensive tasks. By 2012, GPUs had evolved into highly parallel multi-core systems allowing very 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:
- push-relabel maximum flow algorithm
- fast sort algorithms of large lists
- two-dimensional fast wavelet transform
- molecular dynamics simulations
Programming abilities[edit]
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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.[4] 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,[5] Microsoft's DirectCompute, OpenGL Compute Shaders and C++ AMP.[6]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 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.[7][8][9][10][11]
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,[12] which supersedes the beta released February 14, 2008.[13] 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.[citation needed]
CUDA 8.0 comes with the following libraries (for compilation & runtime, in alphabetical order):
- CUBLAS - CUDA Basic Linear Algebra Subroutines library, see main and docs
- CUDART - CUDA RunTime library, see docs
- CUFFT - CUDA Fast Fourier Transform library, see main and docs
- CURAND - CUDA Random Number Generation library, see main and docs
- CUSOLVER - CUDA based collection of dense and sparse direct solvers, see main and docs
- CUSPARSE - CUDA Sparse Matrix library, see main and docs
- NPP - NVIDIA Performance Primitives library, see main and docs
- NVGRAPH - NVIDIA Graph Analytics library, see main and docs
- NVML - NVIDIA Management Library, see main and docs
- NVRTC - NVRTC RunTime Compilation library for CUDA C++, see docs
CUDA 8.0 comes with these other software components:
- nView - NVIDIA nView Desktop Management Software, see main and docs (pdf)
- NVWMI - NVIDIA Enterprise Management Toolkit, see main and docs (chm)
- PhysX - GameWorks PhysX is a scalable multi-platform game physics solution, see main and docs
Advantages[edit]
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.[14]
- Faster downloads and readbacks to and from the GPU
- Full support for integer and bitwise operations, including integer texture lookups
Limitations[edit]
- Whether for the host computer or the GPU device, all CUDA source code is now processed according to C++ syntax rules.[15] This was not always the case. Earlier versions of CUDA were based on C syntax rules.[16] 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).
- Unlike OpenCL, CUDA-enabled GPUs are only available from Nvidia.[17]
- No emulator 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.[citation needed]
- 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 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 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.[18]
GPUs supported[edit]
Supported CUDA Level of GPU and Card. See direct also Nvidia:
- CUDA SDK 6.5: Last Version with support for Compute Capability 1.x (Tesla)
- CUDA SDK 7.5 support for Compute Capability 2.0 – 5.x (Fermi, Kepler, Maxwell)
- CUDA SDK 8.0 support for Compute Capability 2.0 – 6.x (Fermi, Kepler, Maxwell, Pascal), last version with support for Compute Capability 2.x (Fermi)
- CUDA SDK 9.0 support for Compute Capability 3.0 – 7.x (Kepler, Maxwell, Pascal, Volta)
| Compute capability (version) | Micro- architecture | GPUs | GeForce | Quadro, NVS | Tesla | Tegra, Jetson, DRIVE |
|---|---|---|---|---|---|---|
| 1.0 | Tesla | G80 | GeForce 8800 Ultra, GeForce 8800 GTX, GeForce 8800 GTS(G80) | Quadro FX 5600, Quadro FX 4600, Quadro Plex 2100 S4 | Tesla C870, Tesla D870, Tesla S870 | |
| 1.1 | G92, G94, G96, G98, G84, G86 | GeForce GTS 250, GeForce 9800 GX2, GeForce 9800 GTX, GeForce 9800 GT, GeForce 8800 GTS(G92), GeForce 8800 GT, GeForce 9600 GT, GeForce 9500 GT, GeForce 9400 GT, GeForce 8600 GTS, GeForce 8600 GT, GeForce 8500 GT, GeForce G110M, GeForce 9300M GS, GeForce 9200M GS, GeForce 9100M G, GeForce 8400M GT, GeForce G105M | Quadro FX 4700 X2, Quadro FX 3700, Quadro FX 1800, Quadro FX 1700, Quadro FX 580, Quadro FX 570, Quadro FX 470, Quadro FX 380, Quadro FX 370, Quadro FX 370 Low Profile, Quadro NVS 450, Quadro NVS 420, Quadro NVS 290, Quadro NVS 295, Quadro Plex 2100 D4, Quadro FX 3800M, Quadro FX 3700M, Quadro FX 3600M, Quadro FX 2800M, Quadro FX 2700M, Quadro FX 1700M, Quadro FX 1600M, Quadro FX 770M, Quadro FX 570M, Quadro FX 370M, Quadro FX 360M, Quadro NVS 320M, Quadro NVS 160M, Quadro NVS 150M, Quadro NVS 140M, Quadro NVS 135M, Quadro NVS 130M, Quadro NVS 450, Quadro NVS 420, Quadro NVS 295 | |||
| 1.2 | GT218, GT216, GT215 | GeForce GT 340*, GeForce GT 330*, GeForce GT 320*, GeForce 315*, GeForce 310*, GeForce GT 240, GeForce GT 220, GeForce 210, GeForce GTS 360M, GeForce GTS 350M, GeForce GT 335M, GeForce GT 330M, GeForce GT 325M, GeForce GT 240M, GeForce G210M, GeForce 310M, GeForce 305M | Quadro FX 380 Low Profile, Nvidia NVS 300, Quadro FX 1800M, Quadro FX 880M, Quadro FX 380M, Nvidia NVS 300, NVS 5100M, NVS 3100M, NVS 2100M, ION | |||
| 1.3 | GT200, GT200b | GeForce GTX 295, GTX 285, GTX 280, GeForce GTX 275, GeForce GTX 260 | Quadro FX 5800, Quadro FX 4800, Quadro FX 4800 for Mac, Quadro FX 3800, Quadro CX, Quadro Plex 2200 D2 | Tesla C1060, Tesla S1070, Tesla M1060 | ||
| 2.0 | Fermi | GF100, GF110 | GeForce GTX 590, GeForce GTX 580, GeForce GTX 570, GeForce GTX 480, GeForce GTX 470, GeForce GTX 465, GeForce GTX 480M | Quadro 6000, Quadro 5000, Quadro 4000, Quadro 4000 for Mac, Quadro Plex 7000, Quadro 5010M, Quadro 5000M | Tesla C2075, Tesla C2050/C2070, Tesla M2050/M2070/M2075/M2090 | |
| 2.1 | GF104, GF106 GF108, GF114, GF116, GF117, GF119 | GeForce GTX 560 Ti, GeForce GTX 550 Ti, GeForce GTX 460, GeForce GTS 450, GeForce GTS 450*, GeForce GT 640 (GDDR3), GeForce GT 630, GeForce GT 620, GeForce GT 610, GeForce GT 520, GeForce GT 440, GeForce GT 440*, GeForce GT 430, GeForce GT 430*, GeForce GT 420*, GeForce GTX 675M, GeForce GTX 670M, GeForce GT 635M, GeForce GT 630M, GeForce GT 625M, GeForce GT 720M, GeForce GT 620M, GeForce 710M, GeForce 610M, GeForce 820M, GeForce GTX 580M, GeForce GTX 570M, GeForce GTX 560M, GeForce GT 555M, GeForce GT 550M, GeForce GT 540M, GeForce GT 525M, GeForce GT 520MX, GeForce GT 520M, GeForce GTX 485M, GeForce GTX 470M, GeForce GTX 460M, GeForce GT 445M, GeForce GT 435M, GeForce GT 420M, GeForce GT 415M, GeForce 710M, GeForce 410M | Quadro 2000, Quadro 2000D, Quadro 600, Quadro 410, Quadro 4000M, Quadro 3000M, Quadro 2000M, Quadro 1000M, NVS 310, NVS 315, NVS 5400M, NVS 5200M, NVS 4200M | |||
| 3.0 | Kepler | GK104, GK106, GK107 | GeForce GTX 770, GeForce GTX 760, GeForce GT 740, GeForce GTX 690, GeForce GTX 680, GeForce GTX 670, GeForce GTX 660 Ti, GeForce GTX 660, GeForce GTX 650 Ti BOOST, GeForce GTX 650 Ti, GeForce GTX 650, GeForce GTX 880M, GeForce GTX 780M, GeForce GTX 770M, GeForce GTX 765M, GeForce GTX 760M, GeForce GTX 680MX, GeForce GTX 680M, GeForce GTX 675MX, GeForce GTX 670MX, GeForce GTX 660M, GeForce GT 750M, GeForce GT 650M, GeForce GT 745M, GeForce GT 645M, GeForce GT 740M, GeForce GT 730M, GeForce GT 640M, GeForce GT 640M LE, GeForce GT 735M, GeForce GT 730M | Quadro K5000, Quadro K4200, Quadro K4000, Quadro K2200, Quadro K2000, Quadro K2000D, Quadro K600, Quadro K420, Quadro K500M, Quadro K510M, Quadro K610M, Quadro K1000M, Quadro K2000M, Quadro K1100M, Quadro K2100M, Quadro K3000M, Quadro K3100M, Quadro K4000M, Quadro K5000M, Quadro K4100M, Quadro K5100M, NVS 510 | Tesla K10, GRID K340, GRID K520 | |
| 3.2 | GK20A | Tegra K1, Jetson TK1 | ||||
| 3.5 | GK110, GK208 | GeForce GTX Titan Z, GeForce GTX Titan Black, GeForce GTX Titan, GeForce GTX 780 Ti, GeForce GTX 780, GeForce GT 640 (GDDR5), GeForce GT 630 v2, GeForce GT 730, GeForce GT 720, GeForce GT 710, GeForce GT 740M (64-bit, DDR3), GeForce GT 920M | Quadro K6000, Quadro K5200 | Tesla K40, Tesla K20x, Tesla K20 | ||
| 3.7 | GK210 | Tesla K80 | ||||
| 5.0 | Maxwell | GM107, GM108 | GeForce GTX 750 Ti, GeForce GTX 750, GeForce GTX 960M, GeForce GTX 950M, GeForce 940M, GeForce 930M, GeForce GTX 860M, GeForce GTX 850M, GeForce 845M, GeForce 840M, GeForce 830M | Quadro K1200, Quadro K620, Quadro M2000M, Quadro M1000M, Quadro M600M, Quadro K620M, NVS 810 | Tesla M10 | |
| 5.2 | GM200, GM204, GM206 | GeForce GTX Titan X, GeForce GTX 980 Ti, GeForce GTX 980, GeForce GTX 970, GeForce GTX 960, GeForce GTX 950, GeForce GTX 750 SE, GeForce GTX 980M, GeForce GTX 970M, GeForce GTX 965M | Quadro M6000 24GB, Quadro M6000, Quadro M5000, Quadro M4000, Quadro M2000, Quadro M5500, Quadro M5000M, Quadro M4000M, Quadro M3000M | Tesla M4, Tesla M40, Tesla M6, Tesla M60 | ||
| 5.3 | GM20B | Tegra X1, Jetson TX1, DRIVE CX, DRIVE PX | ||||
| 6.0 | Pascal | GP100 | Quadro GP100 | Tesla P100 | ||
| 6.1 | GP102, GP104, GP106, GP107, GP108 | Nvidia TITAN Xp, Titan X, GeForce GTX 1080 Ti, GTX 1080, GTX 1070, GTX 1060, GTX 1050 Ti, GTX 1050, GT 1030, MX150 | Quadro P6000, Quadro P5000, Quadro P4000, Quadro P2000, Quadro P1000, Quadro P600, Quadro P400, Quadro P5000(Mobile), Quadro P4000(Mobile), Quadro P3000(Mobile) | Tesla P40, Tesla P6, Tesla P4 | ||
| 6.2 | GP10B[19] | Drive PX2 with Tegra X2 (T186)[20]Jetson TX2 | ||||
| 7.0 | Volta | GV100 | Tesla V100 | |||
| 7.1 |
'*' – OEM-only products
Version features and specifications[edit]
| Feature support (unlisted features are supported for all compute abilities) | Compute capability (version) | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.0 | 1.1 | 1.2 | 1.3 | 2.x | 3.0 | 3.2 | 3.5, 3.7, 5.0, 5.2 | 5.3 | 6.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 | |||||||||||||
| _ballot() | |||||||||||||
| _threadfence_system() | |||||||||||||
| _syncthreads_count(), _syncthreads_and(), _syncthreads_or() | |||||||||||||
| Surface functions | |||||||||||||
| 3D grid of thread block | |||||||||||||
| Warp shuffle functions | No | Yes | |||||||||||
| Funnel shift | No | Yes | |||||||||||
| Dynamic parallelism | No | Yes | |||||||||||
| Half-precision floating-point operations: addition, subtraction, multiplication, comparison, warp shuffle functions, conversion | No | Yes | |||||||||||
| Atomic addition operating on 64-bit floating point values in global memory and shared memory | No | Yes | |||||||||||
| Data Type | Operation | Supported since | Supported since for Global Memory | Supported since for Shared Memory |
|---|---|---|---|---|
| 16-bit integer | general operations | |||
| 32-bit integer | atomic functions | 1.1 | 1.2 | |
| 64-bit integer | atomic functions | 1.2 | 2.0 | |
| 16-bit floating point | addition, subtraction, multiplication, comparison, warp shuffle functions, conversion | 5.3 | ||
| 32-bit floating point | atomicExch() | 1.1 | 1.2 | |
| 32-bit floating point | atomic addition | 2.0 | 2.0 | |
| 64-bit floating point | general operations | 1.3 | ||
| 64-bit floating point | atomic addition | 6.0 | 6.0 |
Note: Any missing lines or empty entries do reflect some lack of information on that exact item.
| Technical specifications | Compute capability (version) | ||||||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.0 | 1.1 | 1.2 | 1.3 | 2.x | 3.0 | 3.2 | 3.5 | 3.7 | 5.0 | 5.2 | 5.3 | 6.0 | 6.1 | 6.2 | |||
| Maximum number of resident grids per device (Concurrent Kernel Execution) | t.b.d. | 16 | 4 | 32 | 16 | 128 | 32 | 16 | |||||||||
| Maximum dimensionality of grid of thread blocks | 2 | 3 | |||||||||||||||
| Maximum x-dimension of a grid of thread blocks | 65535 | 231 − 1 | |||||||||||||||
| Maximum 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 | 16 | 32 | ||||||||||||||
| Maximum number of resident warps per multiprocessor | 24 | 32 | 48 | 64 | |||||||||||||
| Maximum number of resident threads per multiprocessor | 768 | 1024 | 1536 | 2048 | |||||||||||||
| Number of 32-bit registers per multiprocessor | 8 K | 16 K | 32 K | 64 K | 128 K | 64 K | |||||||||||
| Maximum number of 32-bit registers per thread block | N/A | 32 K | 64 K | 32 K | 64 K | 32 K | 64 K | 32 K | |||||||||
| Maximum number of 32-bit registers per thread | 124 | 63 | 255 | ||||||||||||||
| Maximum amount of shared memory per multiprocessor | 16 KB | 48 KB | 112 KB | 64 KB | 96 KB | 64 KB | 96 KB | 64 KB | |||||||||
| Maximum amount of shared memory per thread block | 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 | 10 KB | |||||||||||||||
| Cache working set per multiprocessor for texture memory | 6 – 8 KB | 12 KB | 12 – 48 KB | 24 KB | 48 KB | N/A | 24 KB | 48 KB | 24 KB | ||||||||
| Maximum width for 1D texture reference bound to a CUDA array | 8192 | 65536 | |||||||||||||||
| Maximum width for 1D texture reference bound to linear memory | 227 | ||||||||||||||||
| Maximum width and number of layers for a 1D layered texture reference | 8192 × 512 | 16384 × 2048 | |||||||||||||||
| Maximum width and height for 2D texture reference bound to a CUDA array | 65536 × 32768 | 65536 × 65535 | |||||||||||||||
| Maximum width and height for 2D texture reference bound to a linear memory | 650002 | ||||||||||||||||
| Maximum width and height for 2D texture reference bound to a CUDA array supporting texture gather | N/A | 163842 | |||||||||||||||
| Maximum width, height, and number of layers for a 2D layered texture reference | 8192 × 8192 × 512 | 16384 × 16384 × 2048 | |||||||||||||||
| Maximum width, height and depth for a 3D texture reference bound to linear memory or a CUDA array | 20483 | 40963 | |||||||||||||||
| Maximum width and number of layers for a cubemap layered texture reference | N/A | 16384 × 2046 | |||||||||||||||
| Maximum number of textures that can be bound to a kernel | 128 | 256 | |||||||||||||||
| Maximum width for a 1D surface reference bound to a CUDA array | Not supported | 65536 | |||||||||||||||
| Maximum width and number of layers for a 1D layered surface reference | 65536 × 2048 | ||||||||||||||||
| Maximum width and height for a 2D surface reference bound to a CUDA array | 65536 × 32768 | ||||||||||||||||
| Maximum width, height, and number of layers for a 2D layered surface reference | 65536 × 32768 × 2048 | ||||||||||||||||
| Maximum width, height, and depth for a 3D surface reference bound to a CUDA array | 65536 × 32768 × 2048 | ||||||||||||||||
| Maximum width and number of layers for a cubemap layered surface reference | 32768 × 2046 | ||||||||||||||||
| Maximum number of surfaces that can be bound to a kernel | 8 | 16 | |||||||||||||||
| Maximum number of instructions per kernel | 2 million | 512 million | |||||||||||||||
| Architecture specifications | Compute capability (version) | ||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| 1.0 | 1.1 | 1.2 | 1.3 | 2.0 | 2.1 | 3.0 | 3.5 | 3.7 | 5.0 | 5.2 | 6.0 | 6.1, 6.2 | |
| Number of ALU lanes for integer and single-precision floating-point arithmetic operations | 8[21] | 32 | 48 | 192 | 128 | 64 | 128 | ||||||
| Number of special function units for single-precision floating-point transcendental functions | 2 | 4 | 8 | 32 | 16 | 32 | |||||||
| Number of texture filtering units for every texture address unit or render output unit (ROP) | 2 | 4 | 8 | 16 | 8 | ||||||||
| Number of warp schedulers | 1 | 2 | 4 | 2 | 4 | ||||||||
| Number of instructions issued at once by scheduler | 1 | 2[22] | |||||||||||
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