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GPU-Quicksort в OpenCL 2.0: вложенные параллельные вычисления и сканирование групп обработки

Введение Краткий курс истории алгоритма быстрой сортировки
Authored by Last updated on 05/31/2019 - 14:20
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使用 OpenCL™ 2.0 读写图片

While Image convolution is not as effective with the new Read-Write images functionality, any image processing technique that needs be done in place may benefit from the Read-Write images. One example of a process that could be used effectively is image composition. In OpenCL 1.2 and earlier, images were qualified with the “__read_only” and __write_only” qualifiers. In the OpenCL 2.0, images can...
Authored by Last updated on 05/31/2019 - 14:20
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A Runtime Generated FFT for Intel® Processor Graphics

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Authored by Dan Petre (Intel) Last updated on 07/06/2019 - 20:30
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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.
Authored by Last updated on 10/15/2019 - 15:20
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Using Basic Capabilities of Multi-Device Systems with OpenCL™

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Authored by Last updated on 10/15/2019 - 16:50
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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...
Authored by Last updated on 10/15/2019 - 16:50
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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.
Authored by Last updated on 10/15/2019 - 16:50
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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.
Authored by Last updated on 10/15/2019 - 16:50
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OpenCL™ Platform/Device Capabilities Viewer Sample

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Authored by Last updated on 10/15/2019 - 16:50