Using Chapel with libfabric¶

This document describes how to run Chapel across multiple machines using the OpenFabrics Interfaces libfabric-based ofi communication layer. Multilocale Chapel Execution gives general information about running Chapel in a multilocale configuration.

Note

The ofi communication layer is new as of the Chapel 1.19 release. It is complete in terms of initial development and passes Chapel testing, but it is still something of an early-access configuration in the sense that users may need to supply supporting packages and performance may leave something to be desired.

Building Chapel with the ofi Communication Layer¶

1. Make general, non-communication Chapel configuration settings as described in Setting up Your Environment for Chapel.

2. Configure the Chapel runtime to select the ofi communication layer

export CHPL_COMM=ofi

3. Set the variable indicating the path to a libfabric installation. It may be possible to skip this step, if your system has libfabric already installed and your target compiler can find its include and library files itself. But this is not common, so you will probably need to do:

export LIBFABRIC_DIR=<Place where libfabric is installed>


The <Place where libfabric is installed> should be a directory with an include/rdma subdirectory that contains the libfabric include files and a lib subdirectory that contains the libfabric library files. If your system does not have those installed already, you will need to download libfabric and possibly build it.

Note: On a Mac OS X system, libfabric can be obtained through Homebrew with the following command.

brew install libfabric

4. Select a launcher. On Cray XC systems you can skip this step, because on those systems the automatically-selected aprun or srun launcher settings will work with the ofi communication layer. But on other systems, select the Chapel mpirun4ofi launcher. For more information see The mpirun4ofi Launcher, below.

export CHPL_LAUNCHER=mpirun4ofi

5. Having done this, set the variable indicating the path to an OpenMPI installation. It may be possible to skip this step, if your system has OpenMPI already installed and your target compiler can find its include and library files itself. But this is not common, so you will probably need to do:

export MPI_DIR=<Place where OpenMPI is installed>


The <Place where OpenMPI is installed> should be a directory with an include subdirectory that contains the OpenMPI include files and a lib subdirectory that contains the OpenMPI library files. If your system does not have those installed already, you will need to download OpenMPI and possibly build it.

Note

In the future we hope both to be able to support the MPICH MPI package in addition to supporting the OpenMPI MPI package, and to use the regular Chapel mpirun launcher with the ofi communication layer, but for now OpenMPI and mpirun4ofi are the only options on platforms other than Cray XC systems.

Note: On a Mac OS X system, OpenMPI can be obtained through Homebrew with the following command.

brew install open-mpi

6. Re-make the compiler and runtime from CHPL_HOME (see Building Chapel).

cd $CHPL_HOME make  7. Now you are ready to compile and run programs. Compile your Chapel program as usual. chpl$CHPL_HOME/examples/hello6-taskpar-dist.chpl

8. Optionally set any environment variables necessary during execution (see below) and run, specifying the number of locales on the command line. For example, this runs the hello6-taskpar-dist example program on 2 locales:

./hello6-taskpar-dist -nl 2


Execution Environment¶

Libfabric Providers¶

Libfabric defines an abstract network and operations on it, and so-called providers within libfabric define the concrete instances of the network and operations. The provider used by a program is selected at execution time. The ofi communication layer has been tested with 3 different providers:

gni
The gni provider works only on Cray XC systems. It is built on the Cray native uGNI library and communicates over the Cray proprietary Aries network interface. This is the default provider on Cray XC systems. Note that the libfabric gni provider itself is something of a work-in-progress, and Chapel performance using libfabric and gni will probably never match what can be achieved using the native ugni communication layer.
sockets
The sockets provider works on all platforms. It is built on POSIX sockets and communicates over any network interface on which the OS can provide sockets support. This is the default provider on all systems other than Cray XC. The sockets provider is fully functional, indeed to the extent libfabric has a reference provider the sockets provider is it, but its emphasis is definitely functionality rather than performance.
verbs

The verbs provider works on any system with verbs-based network hardware (Infiniband, iWarp, etc.). It is built on the Linux Verbs API.

(Note for libfabric devotees: when the verbs provider is specified to the ofi communication layer as described below, what is actually used is the verbs;ofi_rxm provider, which is the verbs provider plus a utility provider which supports reliable datagrams for remote memory access operations.)

The CHPL_RT_COMM_OFI_PROVIDER environment variable can be set to force use of a provider other than the default. In particular, it can force use of the sockets provider on Cray XC systems, or the verbs provider on verbs-based systems where the default would otherwise be the sockets provider. For example, the following would force use of the verbs provider:

export CHPL_RT_COMM_OFI_PROVIDER=verbs


The Chapel group has done full testing both on a Cray XC system with the gni and sockets providers, and on an InfiniBand-based system with the sockets and verbs providers. Some additional testing has been done with the sockets provider on a MacBook running Mac OS X. All of these configurations are expected to work. Provider settings we have not tested with the ofi communication layer may lead to internal errors. Settings which are at odds with the available networks, such as specifying the gni provider on a vanilla Linux cluster, will definitely lead to internal errors.

As the ofi communication layer evolves toward completion we expect to move from the current name-based technique for selecting the provider to a more capability-based one. Users will probably still be able to force use of a particular provider by naming it, but the need to do so for other than curiosity’s (or performance comparison’s) sake should be reduced.

The gni Provider, Memory Registration, and the Heap¶

(Before you get any further into this section, you should probably re-read the note above about performance being better with the native ugni communication layer than with the ofi communication layer and gni provider.)

Network technologies sometimes require memory registration, meaning that ranges of memory which will be the source or target of communication operations must be made known to the network before any such operations can occur. When the ofi communication layer is used with the gni provider, memory has to be registered. This has certain implications for users, the most notable being that the heap must have a fixed size.

The heap is an area of memory used for dynamic allocation of everything from user data to task stacks to internal management data structures. When memory must be registered, the ofi communication layer needs to know the maximum size the heap will grow to during execution. The default heap size is 16 GiB, but you can change this by setting the CHPL_RT_MAX_HEAP_SIZE environment variable. Set it to a positive number for the desired heap size in bytes optionally followed by k or K for KiB, m or M for MiB, g or G for GiB, or to a positive integer followed by % to indicate a percentage of the node real memory. Either CHPL_RT_MAX_HEAP_SIZE=12g or =20% specifies roughly a 12 GiB heap on a 64 GiB compute node, for example.

We have not yet quantified the effects, but performance with the gni provider may be improved if you have a craype-hugepages module loaded both when you build your program and when you run it. For example:

module load craype-hugepages16M


See Native ugni Communication Layer for more discussion about hugepages, hugepage modules, and the heap size. Note, however, that anything there about a dynamically sized heap does not apply to the ofi communication layer and the libfabric gni provider.

Note

In the future we hope to be able to reduce the user impact of memory registration when using the ofi communication layer.

The mpirun4ofi Launcher¶

Programs built with the ofi communication layer on Cray XC systems can use the existing launchers. On other systems, for now they must use the mpirun4ofi launcher, which is a provisional, thin wrapper around OpenMPI mpirun.

The mpirun4ofi launcher can run libfabric-based Chapel programs either with or without slurm. Outside of a slurm job, it will run all of the per-locale Chapel program instances directly on the launch node. In this situation you should be sure to follow the guidance in Overloading system nodes if you are using Qthreads-based tasking. Within a slurm job, the mpirun4ofi launcher will arrange for the per-locale Chapel program instances to be distributed in a cyclic manner across the nodes assigned to the job. Overloading can still be an issue if there are more Chapel locales (program instances) than nodes in the slurm job, however.