Programming Environment

Compilers

C, C++ and Fortran are supported on the Ascend cluster. Intel, oneAPI, GNU Compiler Collectio (GCC) and AOCC are available. The Intel development tool chain is loaded by default. Compiler commands and recommended options for serial programs are listed in the table below. See also our compilation guide.

The Rome/Milan processors from AMD that make up Ascend support the Advanced Vector Extensions (AVX2) instruction set, but you must set the correct compiler flags to take advantage of it. AVX2 has the potential to speed up your code by a factor of 4 or more, depending on the compiler and options you would otherwise use. However, bear in mind that clock speeds decrease as the level of the instruction set increases. So, if your code does not benefit from vectorization it may be beneficial to use a lower instruction set.

In our experience, the Intel compiler usually does the best job of optimizing numerical codes and we recommend that you give it a try if you’ve been using another compiler.

With the Intel/oneAPI compilers, use -xHost and -O2 or higher. With GCC, use -march=native and -O3. 

This advice assumes that you are building and running your code on Ascend. The executables will not be portable.  Of course, any highly optimized builds, such as those employing the options above, should be thoroughly validated for correctness.

Parallel Programming

MPI

OSC systems use the MVAPICH implementation of the Message Passing Interface (MPI), optimized for the high-speed Infiniband interconnect. MPI is a standard library for performing parallel processing using a distributed-memory model. For more information on building your MPI codes, please visit the MPI Library documentation.

MPI programs are started with the srun command. For example,

#!/bin/bash
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=48

srun [ options ] mpi_prog

The srun command will normally spawn one MPI process per task requested in a Slurm batch job. Use the --ntasks-per-node=n option to change that behavior. For example,

#!/bin/bash
#SBATCH --nodes=2
#SBATCh --exclusive

# Use the maximum number of CPUs of two nodes
srun ./mpi_prog

# Run 8 processes per node
srun -n 16 --ntasks-per-node=8  ./mpi_prog

The table below shows some commonly used options. Use srun -help for more information.

OpenMP

The Intel, and GNU compilers understand the OpenMP set of directives, which support multithreaded programming. For more information on building OpenMP codes on OSC systems, please visit the OpenMP documentation.

An OpenMP program by default will use a number of threads equal to the number of CPUs requested in a Slurm batch job. To use a different number of threads, set the environment variable OMP_NUM_THREADS. For example,

#!/bin/bash
#SBATCH --ntasks-per-node=8

# Run 8 threads
./omp_prog

# Run 4 threads
export OMP_NUM_THREADS=4
./omp_prog

Interactive job only

Please use -c, --cpus-per-task=X to request an interactive job. Both result in an interactive job with X CPUs available but only the former option automatically assigns the correct number of threads to the OpenMP program.

Hybrid (MPI + OpenMP)

An example of running a job for hybrid code:

#!/bin/bash
#SBATCH --nodes=2
#SBATCH --ntasks-per-node=80

# Run 4 MPI processes on each node and 40 OpenMP threads spawned from a MPI process
export OMP_NUM_THREADS=40
srun -n 8 -c 40 --ntasks-per-node=4 ./hybrid_prog

Tuning Parallel Program Performance: Process/Thread Placement

To get the maximum performance, it is important to make sure that processes/threads are located as close as possible to their data, and as close as possible to each other if they need to work on the same piece of data, with given the arrangement of node, sockets, and cores, with different access to RAM and caches. 

While cache and memory contention between threads/processes are an issue, it is best to use scatter distribution for code. 

Processes and threads are placed differently depending on the computing resources you requste and the compiler and MPI implementation used to compile your code. For the former, see the above examples to learn how to run a job on exclusive nodes. For the latter, this section summarizes the default behavior and how to modify placement.

OpenMP only

For all three compilers (Intel, GCC and oneAPI), purely threaded codes do not bind to particular CPU cores by default. In other words, it is possible that multiple threads are bound to the same CPU core. 

The following table describes how to modify the default placements for pure threaded code:

1. Threads in the same socket might be bound to the same CPU core.

MPI Only

For MPI-only codes, MVAPICH first binds as many processes as possible on one socket, then allocates the remaining processes on the second socket so that consecutive tasks are near each other. Intel MPI and OpenMPI alternately bind processes on socket 1, socket 2, socket 1, socket 2 etc, as cyclic distribution.

For process distribution across nodes, all MPIs first bind as many processes as possible on one node, then allocates the remaining processes on the second node. 

The following table describe how to modify the default placements on single node for MPI-only code with the command srun:

1. MVP_CPU_BINDING_POLICY will not work if MVP_ENABLE_AFFINITY=0 is set.

To distribute processes evenly across nodes, please set SLURM_DISTRIBUTION=cyclic.

Hybrid (MPI + OpenMP)

For hybrid codes, each MPI process is allocated a number of cores defined by OMP_NUM_THREADS, and the threads of each process are bound to those cores. All MPI processes, along with the threads bound to them, behave similarly to what was described in the previous sections.

The following table describe how to modify the default placements on a single node for Hybrid code with the command srun:

Summary

The above tables list the most commonly used settings for process/thread placement. Some compilers and Intel libraries may have additional options for process and thread placement beyond those mentioned on this page. For more information on a specific compiler/library, check the more detailed documentation for that library.

GPU Programming

244 NVIDIA A100 GPUs are available on Ascend. Please visit our GPU documentation.

Reference

• Slurm: Support for Multi-core/Multi-thread Architecture
• Thread Placement and Thread Affinity