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NMath Premium (產(chǎn)品編號(hào)：13409)

將GPU加速數(shù)學(xué)計(jì)算的強(qiáng)大CUDA架構(gòu)的優(yōu)勢(shì)利用到NMath和NMath Stats中

標(biāo)簽：數(shù)學(xué)計(jì)算

開發(fā)商： CenterSpace

當(dāng)前版本：最新版本

產(chǎn)品類型：控件

產(chǎn)品功能：算法

平臺(tái)語言：

開源水平：不提供源碼

本產(chǎn)品的分類與介紹僅供參考，具體以商家網(wǎng)站介紹為準(zhǔn)，如有疑問請(qǐng)來電 023-68661681 咨詢。

NMath Premium是在.NET平臺(tái)上將GPU加速數(shù)學(xué)計(jì)算的強(qiáng)大CUDA架構(gòu)的優(yōu)勢(shì)利用到NMath和NMath Stats中。CUDA是NVIDIA開發(fā)的一種并行計(jì)算平臺(tái)和編程模型，它可以通過利用圖形處理單元的能力大幅提高計(jì)算性能。GPU計(jì)算是所有NVIDIA 8系列和更高級(jí)別的GPU中的一個(gè)標(biāo)準(zhǔn)功能。整個(gè)NVIDIA Tesla線均支持CUDA技術(shù)。

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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)

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

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)

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