Index of /examples/knl

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RCS examples are provided to assist you in learning the software and the development of your applications on the Shared Computing Cluster (SCC). The instructions provided along with the code assume that the underlying OS is Linux. If these examples are run on a different architecture, you might need to make some changes to the code and/or the way the program is built and executed.

RCS Examples for Intel Xeon Phi Knights Landing (KNL)

Directory Structure

Usage notes

Kinghts Landing (KNL) is an Intel Xeon Phi many-integrated-core (MIC) processor. Currently there are two KNL nodes on SCC. The host names are scc-ib1 and scc-ib2. There are 68 physical cores on each KNL node. Each core supports 4 computing threads by the hyper-threading technique. Each node therefore supports a total of 272 threads for multi-threaded programs: 68 cores, 4 hyper threads per core. The optimal number of threads for any program will have to be determined via testing.

Different from the previous generation of Xeon Phi --- Knights Corner (KNC), the KNL is self-hosted. The operating system runs directly on the KNL architecture, in contrast with the KNC architecture where an additional Xeon CPU was used to host the OS. The CentOS 7 system is installed on the SCC KNL nodes. Please note that this is an updated version compared with the SCC login and compute nodes which run CentOS 6.

Intel Omni-path is installed to support data communication between the two KNL nodes.

Note that a single KNL core is much slower than a regular Xeon CPU core. It is not recommended to run a serial program on KNL as the run time will be much longer compared with the regular SCC compute nodes. To be accelerated on KNL, the programms have to be parallelized, for example by MPI, OpenMP or hybrid MPI-OpenMP.

C or Fortran codes can be compiled and run on KNL. If the C or Fortran codes are parallelized and optimized appropriately, they can be accelerated considerably by the KNL architeture. Intel Math Kernel Library (MKL) functions can be automatically accelerated on KNL. For Python programmers, if the numpy or scipy libraries are built with Intel MKL, the numpy or scipy functions can be automatically accelerated on KNL too. The SCC has several versions of Python provided by Intel available via the module system which are built with the MKL.

Please refer to the following instructions to compile and run C or Fortran programs on the KNL nodes.

Contact Information

Shaohao Chen:
Brian Gregor:

Operating System Requirements

The examples presented in this directory were written in C or Fortran.
- c or Fortran compilers available

External References