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Performance Analysis Using Intel® Performance Primitives

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Performance Analysis Using Intel® Performance Primitives

White Paper: Intel® Advanced Vector Extensions (Intel® AVX) Signal Processing Embedded Computing

Signal Processing on Intel® Architecture: Performance Analysis using Intel® Performance Primitives

Engineers can quickly determine whether Intel® processor-based platforms with Intel® Advanced Vector Extensions (Intel® AVX) satisfy signal processing requirements.

Signal processing functions have often required special-purpose hardware such as DSPs and FPGAs. However, recent enhancements to Intel® architecture processors are providing developers an alternative: execute signal processing workloads on an Intel processor.

Signal processing on the latest Intel processors is now a viable option due to continued improvements in multi-core architectures. The increased parallelism from vector instructions, along with other continuing performance improvements, enables the efficient execution of data parallel workloads such as digital transforms and filters. Additionally, by consolidating signal processing functions with other workloads on a multi-core Intel processor, it is possible to save hardware cost, simplify the application development environment, and reduce time to market. This approach can be applied to many applications in aerospace (radar, sonar), communications infrastructure (baseband processing, transcoding), and healthcare (medical imaging).

This paper describes an easy process that allows developers to quickly determine how fast the 2nd generation Intel® Core™ i7-2710QE processor will execute their signal processing algorithms, based on performance data that is relatively easy to obtain. Developers can complete the process in a straightforward manner, as demonstrated with two simple examples in this paper: fast convolution and amplitude demodulation. The paper concludes by reviewing some of the development tools available to developers to conduct their own evaluations.

Read the full Intel® Performance Primitives White Paper.