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OPTIMIZING INTEL® ARCHITECTURE MULTIMEDIA APPLICATIONS BY SOFTWARE TUNING AND HARDWARE ACCELERATION.

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Title: OPTIMIZING INTEL® ARCHITECTURE MULTIMEDIA APPLICATIONS BY SOFTWARE TUNING AND HARDWARE ACCELERATION.
Authors: Yuming Li1 Yuming.li@intel.com
Source: Intel Technology Journal. 2014, Vol. 18 Issue 2, p148-170. 23p.
Subjects: SIMD (Computer architecture), Intel computers, Computer architecture, Android (Operating system), Mathematical optimization
Abstract: Multimedia applications are becoming increasingly common in personal computers. For the reason of data level parallelism for multimedia data, SIMD (Single Instruction, Multiple Data) has commonly been adapted for multimedia optimization in general-purpose processors. Because SIMD (MMX and SSE) is supported by all of the Intel CPUs for mobile and desktop devices, many past PC code optimizations and technology can be used for Intel architecture-based Android applications. On the other hand, hardware acceleration is used to improve performance too. Offloading the compute-intensive multimedia work from software running on the CPU to dedicated video acceleration hardware will save a large amount of CPU resources and power. In this article, an open-source full format player is given as an example to explain the software and hardware optimization technology. The author gives a general introduction of SIMD and Intel® Threaded Building Blocks (Intel® TBB) for Intel architecture. For the hardware acceleration, an unofficial hardware encoder solution that directly calls Openmax-IL and two official solutions, Openmax-Al and MediaCodec, which Google provides, are discussed. MediaCodec should be a very important API after Jelly Bean. In this article, improvements for MediaCodec are given. With the freeing up of additional CPU power, some amazing ideas can be realized by using software optimization and hardware acceleration. [ABSTRACT FROM AUTHOR]
Copyright of Intel Technology Journal is the property of Intel Corporation and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: OPTIMIZING INTEL® ARCHITECTURE MULTIMEDIA APPLICATIONS BY SOFTWARE TUNING AND HARDWARE ACCELERATION.
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  Data: <searchLink fieldCode="AR" term="%22Yuming+Li%22">Yuming Li</searchLink><relatesTo>1</relatesTo><i> Yuming.li@intel.com</i>
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  Data: <searchLink fieldCode="DE" term="%22SIMD+%28Computer+architecture%29%22">SIMD (Computer architecture)</searchLink><br /><searchLink fieldCode="DE" term="%22Intel+computers%22">Intel computers</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+architecture%22">Computer architecture</searchLink><br /><searchLink fieldCode="DE" term="%22Android+%28Operating+system%29%22">Android (Operating system)</searchLink><br /><searchLink fieldCode="DE" term="%22Mathematical+optimization%22">Mathematical optimization</searchLink>
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  Data: Multimedia applications are becoming increasingly common in personal computers. For the reason of data level parallelism for multimedia data, SIMD (Single Instruction, Multiple Data) has commonly been adapted for multimedia optimization in general-purpose processors. Because SIMD (MMX and SSE) is supported by all of the Intel CPUs for mobile and desktop devices, many past PC code optimizations and technology can be used for Intel architecture-based Android applications. On the other hand, hardware acceleration is used to improve performance too. Offloading the compute-intensive multimedia work from software running on the CPU to dedicated video acceleration hardware will save a large amount of CPU resources and power. In this article, an open-source full format player is given as an example to explain the software and hardware optimization technology. The author gives a general introduction of SIMD and Intel® Threaded Building Blocks (Intel® TBB) for Intel architecture. For the hardware acceleration, an unofficial hardware encoder solution that directly calls Openmax-IL and two official solutions, Openmax-Al and MediaCodec, which Google provides, are discussed. MediaCodec should be a very important API after Jelly Bean. In this article, improvements for MediaCodec are given. With the freeing up of additional CPU power, some amazing ideas can be realized by using software optimization and hardware acceleration. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
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  Data: <i>Copyright of Intel Technology Journal is the property of Intel Corporation and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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      – SubjectFull: Intel computers
        Type: general
      – SubjectFull: Computer architecture
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