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For example, if you want to run a binary program, called application, on the KNL platform, and output the first 50 instruction forms:. You may refer to the following document for information on different optimization techniques including prefetching data: We can take advantage of AVX by recompiling the application as shown below: This tool detects all dependent libraries, then transfers the native application and all dependent libraries to the coprocessor and finally runs it on the coprocessor.

Number of Target devices installed: We need to recompile it without the offload option for the KNL processor: Appendix B shows a program using the offload model on KNC. KNL has better hardware prefetching, and therefore less need for software prefetching i.

First, source the environment variables as usual: In the KNL-F processor i. To use OpenMP pragmas, the command line argument —openmp is needed and the header file omp.

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Vector output resulting from shuffle data for pattern ‘AAAA’: Therefore, if your application uses KNC intrinsics, you may refer to the above documents to check whether or not those intrinsics are available for KNL. At the time this document was written, KNL hardware was not yet released. Note that building applications for the KNL processor is lightly different than building it for a traditional as mentioned earlier.

For example, if you want to run a binary program, called application, kbc the KNL platform, and output the first 50 instruction forms: To use Intel Cilk Plus, the header file cilk. For information on how to read the instruction mix histogram report, please refer to this white paper https: We can take advantage of AVX by recompiling the application as shown below:.

To use KNL effectively, applications should effectively use VPU instructions on vector data, good locality of reference, and utilizes caches well in its core computations. The below example shows how to allocate and align data to byte in memory.

Share Tweet Share Send. The current SDE version 7. Finally, use the sde to emulate a knl platform:.

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There are three mode of operations with KNC: Cluster applications also need fabric scaling. To install the SDE, developers can download the kit at https: Set the path to the SDE: Optimization methods that benefit 00 for KNC should also apply to the KNL processor, although some minor changes will probably be required.

This document shows the basic changes the developer will have to make, and does not focus on optimization.

The KNL processor can be booted as a host processor: Vector output kknc from shuffle data for pattern ‘ABCD’: Vector output resulting from multiple input1 and input2: To run a native KNC application, the micnativeloadex tool can be used.

Vector output resulting from permute data for pattern ‘ABCD’: Developers can find more information about AVX in the following documents: