visual computing

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.
  • Разработчики
  • Linux*
  • Microsoft Windows* (XP, Vista, 7)
  • Microsoft Windows* 8.x
  • Windows*
  • C/C++
  • Средний
  • OpenCL™ Code Builder
  • visual computing
  • OpenCL*
  • Инструменты для разработки
  • Intel® Many Integrated Core Architecture
  • Параллельные вычисления
  • IIR Gaussian Blur Filter Implementation using Intel® Advanced Vector Extensions

    This white paper proposes an implementation for the Infinite Impulse Response (IIR) Gaussian blur filter using Intel® Advanced Vector Extensions (Intel® AVX) instructions. For a 2048x2048 image size, the AVX implementation is ~2X faster than the SSE code.
  • Разработчики
  • C/C++
  • Intel® Advanced Vector Extensions
  • Intel® Streaming SIMD Extensions
  • visual computing
  • Gaussian blur filter
  • Графика
  • Медиа процессы
  • Векторизация
  • 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.
  • Разработчики
  • Linux*
  • Microsoft Windows* (XP, Vista, 7)
  • Microsoft Windows* 8.x
  • Windows*
  • C/C++
  • Средний
  • OpenCL™ Code Builder
  • visual computing
  • OpenCL*
  • Инструменты для разработки
  • Intel® Many Integrated Core Architecture
  • Параллельные вычисления
  • Подписаться на visual computing