Crates.io | lamellar-prof |
lib.rs | lamellar-prof |
version | 0.1.0 |
source | src |
created_at | 2021-05-31 22:45:30.967958 |
updated_at | 2021-05-31 22:45:30.967958 |
description | Lamellar is an asynchronous tasking runtime for HPC systems developed in RUST. |
homepage | https://github.com/pnnl/lamellar-runtime |
repository | https://github.com/pnnl/lamellar-runtime |
max_upload_size | |
id | 404507 |
size | 19,879 |
Lamellar is an asynchronous tasking runtime for HPC systems developed in RUST
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)
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();
}
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
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
#![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))
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
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
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
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
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.)
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).
salloc -N 2 -p partition_name
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")
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))
.
Lamellar is still under development, thus not all intended features are yet implemented.
Ryan Friese - ryan.friese@pnnl.gov
Roberto Gioiosa - roberto.gioiosa@pnnl.gov
Mark Raugas - mark.raugas@pnnl.gov
This project is licensed under the BSD License - see the LICENSE.md file for details.
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.