NMath Premium是在.NET平台上将GPU加速数学计算的强大CUDA架构的优势利用到NMath和NMath Stats中。CUDA是NVIDIA开发的一种并行计算平台和编程模型，它可以通过利用图形处理单元的能力大幅提高计算性能。GPU计算是所有NVIDIA 8系列和更高级别的GPU中的一个标准功能。整个NVIDIA Tesla线均支持CUDA技术。
NMath Stats 已与NMath标准版打包，最新版本请点击跳转下载
The Premium Editions of NMath and NMath Stats leverage the power of the CUDA™ architecture for GPU-accelerated mathematics on the .NET platform. CUDA is a parallel computing platform and programming model developed by NVIDIA, which enables dramatic increases in computing performance by harnessing the power of the graphics processing unit. GPU computing is a standard feature in all NVIDIA's 8-Series and later GPUs. The entire NVIDIA Tesla line supports CUDA.
Easy to Use
NMath Premium works with any CUDA-enabled GPU. NMath Premium automatically detects the presence of a CUDA-enabled GPU at runtime and seamlessly redirects appropriate computations to it. The library can be configured to specify which problems should be solved by the GPU, and which by the CPU. If a GPU is not present at runtime, the computation automatically falls back to the CPU without error.
No GPU programming experience is required.
With a few minor exceptions, such as optional GPU configuration settings, the API is identical between NMath and NMath Premium. Existing NMath developers can simply upgrade to NMath Premium and immediately begin to offer their users higher performance from current graphics cards, or from additional GPUs, without writing any new software.
No changes are required to existing NMath code.
Supported Features
GPU acceleration provides a 2-4x speed-up for many NMath functions. With large data sets running on high-performance GPUs, the speed-up can exceed 10x. Furthermore, off-loading computation to the GPU frees up the CPU for additional processing tasks, a further performance gain.
The directly supported features for GPU acceleration of linear algebra (dense systems) are:
GPU acceleration for signal processing includes:
GPU: (1) NVIDIA Tesla M2090: 1 Fermi GPU, 512 CUDA cores, 6GB GDDR5 memory
CPU: Intel Xeon X5670, 2.93 GHz, 6-core with Hyper-Threading (12 threads), 12 MB L3 cache, 32 nm manufacturing process (Westmere)
Of course, many higher-level NMath and NMath Stats classes make use of these functions internally, and so also benefit from GPU acceleration indirectly.
NMath
Least squares, including weighted least squares
Filtering, such as moving window filters and Savitsky-Golay
Nonlinear programming (NLP)
Ordinary differential equations (ODE)
NMath Stats
Two-Way ANOVA, with or without repeated measures
Factor Analysis
Linear regression and logistic regression
Principal component analysis (PCA)
Partial least squares (PLS)
Nonnegative matrix factorization (NMF)
Easy to Use
NMath Premium works with any CUDA-enabled GPU. NMath Premium automatically detects the presence of a CUDA-enabled GPU at runtime and seamlessly redirects appropriate computations to it. The library can be configured to specify which problems should be solved by the GPU, and which by the CPU. If a GPU is not present at runtime, the computation automatically falls back to the CPU without error.
No GPU programming experience is required.
With a few minor exceptions, such as optional GPU configuration settings, the API is identical between NMath and NMath Premium. Existing NMath developers can simply upgrade to NMath Premium and immediately begin to offer their users higher performance from current graphics cards, or from additional GPUs, without writing any new software.
No changes are required to existing NMath code.
Supported Features
GPU acceleration provides a 2-4x speed-up for many NMath functions. With large data sets running on high-performance GPUs, the speed-up can exceed 10x. Furthermore, off-loading computation to the GPU frees up the CPU for additional processing tasks, a further performance gain.
The directly supported features for GPU acceleration of linear algebra (dense systems) are:
- Singular value decomposition (SVD)
- QR decomposition
- Eigenvalue routines
- Solve Ax = B
GPU acceleration for signal processing includes:
- 1D Fast Fourier Transforms (Complex data input)
- 2D Fast Fourier Transforms (Complex data input)
GPU: (1) NVIDIA Tesla M2090: 1 Fermi GPU, 512 CUDA cores, 6GB GDDR5 memory
CPU: Intel Xeon X5670, 2.93 GHz, 6-core with Hyper-Threading (12 threads), 12 MB L3 cache, 32 nm manufacturing process (Westmere)
Of course, many higher-level NMath and NMath Stats classes make use of these functions internally, and so also benefit from GPU acceleration indirectly.
NMath
- Least squares, including weighted least squares
- Filtering, such as moving window filters and Savitsky-Golay
- Nonlinear programming (NLP)
- Ordinary differential equations (ODE)
NMath Stats
- Two-Way ANOVA, with or without repeated measures
- Factor Analysis
- Linear regression and logistic regression
- Principal component analysis (PCA)
- Partial least squares (PLS)
- Nonnegative matrix factorization (NMF)
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