Dynamic Load Balancing Library
DLB is a dynamic library designed to speed up HPC hybrid applications (i.e., two levels of parallelism) by improving the load balance of the outer level of parallelism (e.g., MPI) by dynamically redistributing the computational resources at the inner level of parallelism (e.g., OpenMP). at run time.
This dynamism allows DLB to react to different sources of imbalance: Algorithm, data, hardware architecture and resource availability among others.
Lend When Idle
LeWI (Lend When Idle) is the algorithm used to redistribute the computational resources that are not being used from one process to another process inside the same shared memory node in order to speed up its execution.
Dynamic Resource Ownership Manager
DROM (Dynamic Resource Ownership Manager) is the algorithm used to manage the CPU affinity of a process running a shared memory programming model (e.g., OpenMP).
Tracking Application Life Performance
TALP (Tracking Application Life Performance) is the module used to gather performance data from the application. The data can be obtained during the execution or as a report at the end.
- A supported platform running GNU/Linux (i386, x86-64, ARM, PowerPC or IA64)
- C compiler
- Python 2.4 or higher
- GNU Autotools, only needed if you want to build from the repository.
Download the DLB source code:
- Either from our website: DLB Downloads.
- Or from a git repository
Clone DLB repository
git clone https://github.com/bsc-pm/dlb.git
From our internal GitLab repository (BSC users only):
git clone https://pm.bsc.es/gitlab/dlb/dlb.git
Or download from GitHub releases
cd dlb ./bootstrap
configure. Optionally, check the configure flags by running
./configure -hto see detailed information about some features. MPI support must be enabled with
--with-mpiand, optionally, an argument telling where MPI can be located.
./configure --prefix=<DLB_PREFIX> [<configure-flags>]
Build and install
make make install
Optionally, add the installed bin directory to your
For more information about the autotools installation process, please refer to INSTALL
Choose between linking or preloading the binary with the DLB shared library
libdlb.so and configure DLB using the environment variable
Example 1: Share CPUs between MPI processes
# Link application with DLB mpicc -o myapp myapp.c -L<DLB_PREFIX>/lib -ldlb -Wl,-rpath,<DLB_PREFIX>/lib # Launch MPI as usual, each process will dynamically adjust the number of threads export DLB_ARGS="--lewi" mpirun -n <np> ./myapp
Example 2: Share CPUs between MPI processes with advanced affinity control through OMPT.
# Link application with an OMPT capable OpenMP runtime OMPI_CC=clang mpicc -o myapp myapp.c -fopenmp # Launch application: # * Set environment variables # * DLB library is preloaded # * Run application with binary dlb_run export DLB_ARGS="--lewi --ompt" export OMP_WAIT_POLICY="passive" preload="<DLB_PREFIX>/lib/libdlb.so" mpirun -n <np> <DLB_PREFIX>/bin/dlb_run env LD_PRELOAD="$preload" ./myapp
Example 3: Manually reduce assigned CPUs to an OpenMP process.
# Launch an application preloading DLB export OMP_NUM_THREADS=4 export DLB_ARGS="--drom" export LD_PRELOAD=<DLB_PREFIX>/lib/libdlb.so taskset -c 0-3 ./myapp & # Reduce CPU binding to [1,3] and threads to 2 myapp_pid=$! dlb_taskset -p $myapp_pid -c 1,3
Example 4: Get a TALP summary report at the end of an execution
export DLB_ARGS="--talp --talp-summary=app" PRELOAD=<DLB_PREFIX>/lib/libdlb_mpi.so mpirun <opts> env LD_PRELOAD="$PRELOAD" ./app
Please refer to our DLB User Guide for a more complete documentation.
If you want to cite DLB, you can use the following publications:
Hints to improve automatic load balancing with LeWI for hybrid applications at Journal of Parallel and Distributed Computing 2014. (ScienceDirect) (bibtex) (pdf)
LeWI: A Runtime Balancing Algorithm for Nested Parallelism at International Conference in Parallel Processing 2009, ICPP09. (IEEE Xplore) (bibtex) (pdf)
For questions, suggestions and bug reports, you can contact us via e-mail at email@example.com.