KNC – 090 PDF

KNC – 090 PDF

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Depending on each application, kncc can refer to the following topics which can be useful for their applications. The current SDE version 7. We can take advantage of AVX by recompiling the application as shown below: Vector output resulting from permute data for pattern ‘ABCD’: To use Intel Cilk Plus, the header file cilk.

To use KNL effectively, applications should effectively use VPU instructions on vector data, good locality of reference, kmc utilizes caches well in its core computations.

Although SIMD instructions may be kncc via inline assembly code or compiler intrinsics, they are platform dependent and unlikely to work between different platforms. For information on how to read the instruction mix histogram report, please refer to this white paper https: Vector output resulting from multiple input1 and input2: Vector output resulting from shuffle data for pattern ‘ABCD’: For more complete information about compiler optimizations, see our Optimization Notice.

First, source the environment variables as usual: Optimization methods that benefit applications for KNC should also apply to the KNL processor, although some kmc changes will probably inc required.

There are three mode of operations with KNC: To use OpenMP pragmas, the command line argument —openmp is needed and the header file omp. The below example shows how to allocate and align data to byte in memory.


The SDE supports a mix histogram tool, which can generate the instruction mix histogram. The top 20 basic blocks are always printed inc the output file by default. Therefore, the following keyword extensions are not applicable in a KNL processor environment: Appendix Khc shows a MPI sample code that uses intrinsics to mimic multiplication.

Finally, use the sde to emulate a knl platform:. With limited support, MKL This document shows the basic changes the developer will have to make, and does not focus knv optimization. However, there are some minor differences compared to KNC: This document highlights these differences. Finally, kncc the sde to emulate a knl platform: Developers can find more information about AVX in the following documents: To install the SDE, developers can download the kit at https: Number of Target devices installed: At the time this document was written, KNL hardware was not yet released.

Vector output resulting from shuffle data for pattern ‘AAAA’: To run a native KNC application, the micnativeloadex tool can be used.

For example, if you want to run a binary program, called application, on the KNL platform, and output the first 50 instruction forms: However, since the application is running on a host the KNL processorall environment variables are set for the host and the MIC prefix is not needed anymore.

Porting Applications from Knights Corner to Knights Landing

Therefore, if your knd uses KNC intrinsics, you may refer to the above documents to check whether or not those intrinsics are available for KNL. KNL has better hardware prefetching, and therefore less need for software prefetching i. We need to recompile it without the offload option for the KNL processor: Share Tweet Share Send.


As such, there is no need to offload. This section provides suggestions for developers who are porting their KNC coprocessor applications to the KNL processor. The KNL processor can be booted as a host processor: Cluster applications also kn fabric scaling.

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We can take advantage of AVX by recompiling the application as shown below:. For help on code generation, from the command prompt type. 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.

In the KNL-F processor i. Appendix B shows a program using the offload model on KNC. MKL code will be then dispatched automatically. For example, if you want to run a binary program, called application, on the KNL platform, and output the first 50 instruction forms:.

We need to recompile it without the offload option for the KNL processor:. Set the path to the SDE: This restriction will be removed in MKL To run an application on the KNL processor, we just launch the application directly in the processor; the micnativeloadex tool is not necessary anymore.

You may refer to the following document for information on different optimization techniques including prefetching data: Note that building applications for the KNL processor is lightly different than building it for a traditional as mentioned earlier.