Article

Using Basic Capabilities of Multi-Device Systems with OpenCL™

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Criado por Última atualização em 31/05/2019 - 14:10
Article

Monte Carlo Method for Stock Options Pricing Sample

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Criado por Última atualização em 31/05/2019 - 14:40
Article

Introducing DNN primitives in Intel® Math Kernel Library

Please notes: Deep Neural Network(DNN) component in MKL is deprecated since intel® MKL ​2019 and will be removed in the next intel® MKL Release.

Criado por Vadim Pirogov (Intel) Última atualização em 21/03/2019 - 12:00
Article

在英特尔® 数学核心函数库中引入 DNN 基元

    深度神经网络 (DNN) 处于机器学习领域的前沿。这些算法在 20 世纪 90 年代后期得到了行业的广泛采用,最初应用于诸如银行支票手写识别等任务。深度神经网络在这一任务领域已得到广泛运用,达到甚至超过了人类能力。如今,DNN 已用于图像识别、视频和自然语言处理以及解决复杂的视觉理解问题,如自主驾驶等。DNN 在计算资源及其必须处理的数据量方面要求非常苛刻。

Criado por Vadim Pirogov (Intel) Última atualização em 21/03/2019 - 12:08
Article

OpenCL™ Platform/Device Capabilities Viewer Sample

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Criado por Última atualização em 16/09/2019 - 10:46
Article

General Matrix Multiply Sample

General Matrix Multiply (GEMM) sample demonstrates how to efficiently utilize an OpenCL™ device to perform general matrix multiply operation on two dense square matrices. The primary target devices that are suitable for this sample are the devices with cache memory: Intel® Xeon Phi™ and Intel® Architecture CPU devices.
Criado por Última atualização em 16/09/2019 - 10:49
Article

Median Filter

The sample demonstrates how to implement efficient median filter with OpenCL™ standard. This implementation relies on auto-vectorization performed by Intel® SDK for OpenCL Applications compiler.
Criado por Última atualização em 17/09/2019 - 13:26
Article

HDR Rendering with God Rays Using OpenCL™ Technology

This sample demonstrates a CPU-optimized implementation of the God Rays effect, showing how to: Implement calculation kernels using the OpenCL™ technology C99 Parallelize the kernels by running several work-groups in parallel Organize data exchange between the host and the OpenCL device
Criado por Última atualização em 17/09/2019 - 13:50
Article

Bitonic Sorting

Demonstrates how to implement an efficient sorting routine with the OpenCL™ technology that operates on arbitrary input array of integer values. The sample uses properties of bitonic sequence and principles of sorting networks and enables efficient SIMD-style parallelism through OpenCL vector data types. The code is designed to work well on modern CPUs.
Criado por Última atualização em 18/09/2019 - 16:24
Article

Simple Optimizations of OpenCL™ Code

Simple Optimizations sample demonstrates simple ways of measuring the performance of OpenCL™ kernels in an application. It describes basics of profiling and important caveats like having dedicated “warming” run. It also demonstrates several simple optimizations, some of optimizations are rather CPU-specific (like mapping buffers), while others are more general (like using relaxed-math). The...
Criado por Última atualização em 18/09/2019 - 16:33