![]() ![]() Where “-n 2” indicates that two MPI ranks are used. To run the samples simply follow standard MPI invocations, e.g.: $ mpirun -n 2. The samples require two ranks to execute correctly. Where “sm_xx” indicates the Compute Capability of the device. ![]() Then you can compile a sample: $ mpicxx -fsycl -fsycl-targets=nvpt圆4-nvidia-cuda -Xsycl-target-backend -cuda-gpu-arch=sm_xx send_recv_usm.cpp -o. mpicxx) must point to your DPC++ compiler.įirstly, make sure that you have the wrapper in your path: $ export PATH=/path/to/your-mpi-install/bin:$PATH ![]() In order to compile the samples your compiler wrapper (e.g. In addition, the scatter_reduce_gather.cpp sample exhibits how MPI can be used together with the SYCL 2020 reduction and parallel_for interfaces for optimized but simple multi-rank reductions. When using SYCL USM with MPI, users should always call the MPI function directly from the main thread calling MPI functions that take SYCL USM from within a host_task is currently undefined behavior. The use of buffers with CUDA-aware MPI requires that the user makes the MPI calls within a host_task: see the send_recv_buff.cpp sample for full details. The send_recv_buff.cpp and send_recv_usm.cpp samples are introductory examples of how CUDA-aware MPI can be used with DPC++ using either buffers or USM. Using MPI with the CUDA backend link Compiling and running applications link mpicxx) built to point to your DPC++ compiler. You will also need a CUDA-aware build of an MPI implementation, with the compiler wrapper (e.g. ![]() For instructions on how to build DPC++ refer to the getting-started-guide. In this document we assume that you have a working installation of the Intel oneAPI DPC++ compiler supporting the CUDA backend. This document details how to use CUDA-aware MPI with the DPC++ CUDA backend. ![]()
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