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將GPU加速數(shù)學(xué)計(jì)算的強(qiáng)大CUDA架構(gòu)的優(yōu)勢利用到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
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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)。
NMath Stats 已與NMath標(biāo)準(zhǔn)版打包,最新版本請點(diǎn)擊跳轉(zhuǎn)下載
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.
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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.
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)
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.
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
NMath Stats
更新時(shí)間:2023-01-03 13:41:08.000 | 錄入時(shí)間:2014-02-13 15:42:22.000 | 責(zé)任編輯:胡濤
是一款科學(xué)計(jì)算軟件,為網(wǎng)絡(luò)、 圖像、 幾何學(xué)、 數(shù)據(jù)科學(xué)、 可視化、機(jī)器學(xué)習(xí)等領(lǐng)域提供了有效算法。
軟件它是一個(gè)將 Wolfram|Alpha 和Mathematica 精華融匯于一身的獨(dú)立統(tǒng)一工具。
軟件使用動(dòng)態(tài)內(nèi)存中列式技術(shù)加速分析數(shù)據(jù)
控件提供統(tǒng)計(jì)計(jì)算和生物統(tǒng)計(jì)學(xué)領(lǐng)域的處理功能,包括描述統(tǒng)計(jì)、概率分布、組合功能、多重線型回歸、假設(shè)檢驗(yàn)、方差分析計(jì)算和多元統(tǒng)計(jì)
控件NMath是一個(gè).NET的數(shù)學(xué)庫,包含了NET平臺(tái)上的面向?qū)ο髷?shù)字計(jì)算的基礎(chǔ)類。
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