#hpc #runtime #pgas #distributed #asynchronous

lamellar

Lamellar is an asynchronous tasking runtime for HPC systems developed in RUST

4 releases

0.3.1 Aug 26, 2021
0.3.0 May 31, 2021
0.2.2 Sep 5, 2020
0.2.1 Sep 2, 2020

#88 in Asynchronous

26 downloads per month

Custom license

440KB
10K SLoC

Lamellar - Rust HPC runtime

Lamellar is an asynchronous tasking runtime for HPC systems developed in RUST

SUMMARY

Lamellar is an investigation of the applicability of the Rust systems programming language for HPC as an alternative to C and C++, with a focus on PGAS approaches.

Lamellar provides several different communication patterns to distributed applications. First, Lamellar allows for sending and executing active messages on remote nodes in a distributed environments. The runtime supports two forms of active messages: The first method works with Stable rust and requires the user the register the active message by implementing a runtime exported trait (LamellarAM) and calling a procedural macro (#[lamellar::am]) on the implementation. The second method only works on nightly, but allows users to write serializable closures that are transfered and exectued by the runtime without registration It also exposes the concept of remote memory regions, i.e. allocations of memory that can read/written into by remote nodes.

Lamellar relies on network providers called Lamellae to perform the transfer of data throughout the system. Currently two such Lamellae exist, one used for single node development purposed ("local"), and another based on the Rust OpenFabrics Interface Transport Layer (ROFI) (https://github.com/pnnl/rofi)

NEWS

  • April 2021: Alpha release -- v0.3
  • September 2020: Add support for "local" lamellae, prep for crates.io release -- v0.2.1
  • July 2020: Second alpha release -- v0.2
  • Feb 2020: First alpha release -- v0.1

EXAMPLES

Selecting a Lamellae and constructing a lamellar world instance

use lamellar::Backend;
fn main(){
 let mut world = lamellar::LamellarWorldBuilder::new()
        .with_lamellae( Default::default() ) //if "enable-rofi" feature is active default is rofi, otherwise  default is local
        //.with_lamellae( Backend::Rofi ) //explicity set the lamellae backend to rofi, using the provider specified by the LAMELLAR_ROFI_PROVIDER env var ("verbs" or "shm")
        //.with_lamellae( Backend::RofiShm ) //explicity set the lamellae backend to rofi, specifying the shm provider
        //.with_lamellae( Backend::RofiVerbs ) //explicity set the lamellae backend to rofi, specifying the verbs provider
        .build();
}

Creating and executing a Registered Active Message

use lamellar::{ActiveMessaging, LamellarAm};
#[derive(serde::Serialize, serde::Deserialize)] 
struct HelloWorld { //the "input data" we are sending with our active message
    my_pe: usize, // "pe" is processing element == a node
}

#[lamellar::am]
impl LamellarAM for HelloWorld {
    fn exec(&self) {
        println!(
            "Hello pe {:?} of {:?}, I'm pe {:?}",
            lamellar::current_pe, 
            lamellar::num_pes,
            self.my_pe
        );
    }
}

fn main(){
    let mut world = lamellar::LamellarWorldBuilder::new().build();
    let my_pe = world.my_pe();
    let num_pes = world.num_pes();
    let am = HelloWorld { my_pe: my_pe };
    for pe in 0..num_pes{
        world.exec_am_pe(pe,am.clone()); // explicitly launch on each PE
    }
    world.wait_all(); // wait for all active messages to finish
    world.barrier();  // synchronize with other pes
    let handle = world.exec_all(am.clone()); //also possible to execute on every PE with a single call
    handle.get(); //both exec_all and exec_am_pe return request handles that can be used to access any returned result
}

A number of more complete examples can be found in the examples folder. Sub directories loosely group examples by the feature they are illustrating

BUILD REQUIREMENTS

  • Crates listed in Cargo.toml

Optional: Lamellar requires the following dependencies if wanting to run in a distributed HPC environment: the rofi lamellae is enabled by adding "enable-rofi" to features either in cargo.toml or the command line when building. i.e. cargo build --features enable-rofi

To enable support for serializable remote closures compile with the nightly compiler and specify the "nightly" feature i.e. cargo build --features nightly

  • RUST nightly compiler with the following features (enables remote closure API)
    • #![feature(unboxed_closures)]

At the time of release, Lamellar has been tested with the following external packages:

GCC CLANG ROFI OFI IB VERBS MPI SLURM
7.1.0 8.0.1 0.1.0 1.9.0 1.13 mvapich2/2.3a 17.02.7

The OFI_DIR environment variable must be specified with the location of the OFI (libfabrics) installation. The ROFI_DIR environment variable must be specified with the location of the ROFI installation. (See https://github.com/pnnl/rofi for instructions installing ROFI (and libfabrics))

BUILDING PACKAGE

In the following, assume a root directory ${ROOT} 0. download Lamellar to ${ROOT}/lamellar-runtime cd ${ROOT} && git clone https://github.com/pnnl/lamellar-runtime

  1. download rofi-sys to ${ROOT}/rofi-sys -- or update Cargo.toml to point to the proper location cd ${ROOT} && git clone https://github.com/pnnl/rofi-sys

  2. Select Lamellae to use

    In Cargo.toml add "enable-rofi" feature in wanting to use rofi (or pass --features enable-rofi to your cargo build command ), otherwise local lamellae will be used it may also be necessary to adjust the heap size (const ROFI_MEM) in rofi_comm.rs on the available memory in your system

  3. Compile Lamellar lib and test executable (feature flags can be passed to command line instead of specifying in cargo.toml)

cargo build (--release) (--features enable-rofi) (--features nightly) (--features experimental)

executables located at ./target/debug(release)/test
  1. Compile Examples

cargo build --examples (--release) (--features enable-rofi) (--features nightly) (--features experimental)

executables located at ./target/debug(release)/examples/

Note: we do an explicit build instead of `cargo run --examples` as they are intended to run in a distriubted envrionment (see TEST section below.)

TESTING

The examples are designed to be run on at least two compute nodes, but most will work on a single node using the "local" lamellae. Here is a simple proceedure to run the tests that assume a compute cluster and SLURM job manager. Please, refer to the job manager documentation for details on how to run command on different clusters. Lamellar grabs job information (size, distribution, etc.) from the job manager and runtime launcher (e.g., MPI, please refer to the BUILDING REQUIREMENTS section for a list of tested software versions).

  1. Allocates two compute nodes on the cluster:

salloc -N 2 -p partition_name

  1. Run lamellar examples

mpiexec -n 2 ./target/release/examples/{example} where <test> is the same name as the Rust filenames in each subdirectory in the examples folder (e.g. "am_no_return")

or alternatively:

srun -N 2 -p partition_name -mpi=pmi2 ./target/release/examples/{example} where <test> is the same name as the Rust filenames in each subdirectory in the examples folder (e.g. "am_no_return")

ENVIRONMENT VARIABLES

The number of worker threads used within lamellar is controlled by setting an environment variable: LAMELLAR_THREADS e.g. export LAMELLAR_THREADS=10

The rofi backend provider can be set by explicitly setting using the world builder: e.g. lamellar::LamellarWorldBuilder::new().with_lamellar(Backend::Rofi) currently three Rofi options exist: Backend::RofiVerbs -- to use the verbs provider (enabling distributed execution) Backend::RofiShm -- to use the shm provider (enabling smp execution) Backend::Rofi -- uses the provider specified by LAMELLAR_ROFI_PROVIDER environment variable if defined, else allows libfabrics to select the provider. Current possible values for LAMELLAR_ROFI_PROVIDER include verbs and shm

Note, if running on a single node, you can use the local lamellaer e.g. Backend::Local to simply execute the binaries directly, no need to use mpiexec or srun.

Currently, Lamellar utilizes a large static allocatio of RDMAable memory for internal Runtime data structures and buffers (work is currently in progress on a more scalable approach), this allocation pool is also used to construct LamellarLocalMemRegions (as this operation should not require communication with other PE's). The size of this allocation pool is set through the LAMELLAR_ROFI_MEM_SIZE environment variable, which can be set to a given number of bytes. The default size is 1GB. For examples setting to 20GB could be accomplished with LAMELLAR_ROFI_MEM_SIZE=$((20*1024*1024*1024)).

HISTORY

  • version 0.3.0
    • recursive active messages
    • subteam support
    • support for custom team architectures (Examples/team_examples/custom_team_arch.rs)
    • initial support of LamellarArray (Am based collectives on distributed arrays)
    • integration with Rofi 0.2
    • revamped examples
  • version 0.2.2:
    • Provide examples in readme
  • version 0.2.1:
    • Provide the local lamellae as the default lamellae
    • feature guard rofi lamellae so that lamellar can build on systems without libfabrics and ROFI
    • added an example proxy app for doing a distributed dft
  • version 0.2:
    • New user facing API
    • Registered Active Messages (enabling stable rust)
    • Remote Closures feature guarded for use with nightly rust
    • redesigned internal lamellae organization
    • initial support for world and teams (sub groups of pe)
  • version 0.1:
    • Basic init/finit functionalities
    • Remote Closure Execution
    • Basic memory management (heap and data section)
    • Basic Remote Memory Region Support (put/get)
    • ROFI Lamellae (Remote Closure Execution, Remote Memory Regions)
    • Sockets Lamellae (Remote Closure Execution, limited support for Remote Memory Regions)
    • simple examples

NOTES

STATUS

Lamellar is still under development, thus not all intended features are yet implemented.

CONTACTS

Ryan Friese - ryan.friese@pnnl.gov
Roberto Gioiosa - roberto.gioiosa@pnnl.gov
Mark Raugas - mark.raugas@pnnl.gov

License

This project is licensed under the BSD License - see the LICENSE.md file for details.

Acknowledgments

This work was supported by the High Performance Data Analytics (HPDA) Program at Pacific Northwest National Laboratory (PNNL), a multi-program DOE laboratory operated by Battelle.

Dependencies

~6–9.5MB
~194K SLoC

(a