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[[Category:Software]]
[[Category:Software]][[Category:BiomolecularSimulation]]
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==Introduction== <!--T:1-->
==Introduction== <!--T:1-->
Amber is the collective name for a suite of programs that allow users to perform molecular dynamics simulations, particularly on biomolecules. None of the individual programs carry this name, but the various parts work reasonably well together, and provide a powerful framework for many common calculations.
[https://ambermd.org/ Amber] is the collective name for a suite of programs that allow users to perform molecular dynamics simulations, particularly on biomolecules. None of the individual programs carry this name, but the various parts work reasonably well together, and provide a powerful framework for many common calculations.


==Running Amber 18==
== Amber vs. AmberTools == <!--T:20-->
We have modules for both Amber and AmberTools available in our [[Available software|software stack]].


Currently, versions 18 and 18.10-18.11 are available on all clusters.
<!--T:21-->
* The [https://ambermd.org/AmberTools.php AmberTools] (module <code>ambertools</code>) contains a number of tools for preparing and analyzing simulations, as well as <code>sander</code> to perform molecular dynamics simulations, all of which are free and open source.
* [https://ambermd.org/AmberMD.php Amber] (module <code>amber</code>) contains everything that is included in <code>ambertools</code>, but adds the advanced <code>pmemd</code> program for molecular dynamics simulations.


Non-GPU versions
<!--T:22-->
module load gcc/5.4.0 openmpi/2.1.1 amber/18 scipy-stack/2019a
To see a list of installed versions and which other modules they depend on, you can use the <code>module spider</code> [[Using modules#Sub-command_spider|command]] or check the [[Available software]] page.
or
module load gcc/5.4.0 openmpi/2.1.1 amber/18.10-18.11 scipy-stack/2019a


GPU versions:
== Loading modules == <!--T:42-->
<tabs>
<tab name="StdEnv/2023">
{| class="wikitable sortable"
|-
! AMBER version !! modules for running on CPUs !! modules for running on GPUs (CUDA) !! Notes
|-
| amber/22.5-23.5 || <code> StdEnv/2023 gcc/12.3 openmpi/4.1.5 amber/22.5-23.5</code> || <code>StdEnv/2023 gcc/12.3 openmpi/4.1.5 cuda/12.2 amber/22.5-23.5</code> || GCC, FlexiBLAS & FFTW
|-
| ambertools/23.5 || <code> StdEnv/2023 gcc/12.3 openmpi/4.1.5 ambertools/23.5</code> || <code>StdEnv/2023 gcc/12.3 openmpi/4.1.5 cuda/12.2 ambertools/23.5</code> || GCC, FlexiBLAS & FFTW
|-
|}</tab>
<tab name="StdEnv/2020">
{| class="wikitable sortable"
|-
! AMBER version !! modules for running on CPUs !! modules for running on GPUs (CUDA) !! Notes
|-
| ambertools/21 || <code> StdEnv/2020 gcc/9.3.0 openmpi/4.0.3 scipy-stack ambertools/21  </code> || <code>StdEnv/2020  gcc/9.3.0 cuda/11.4 openmpi/4.0.3 scipy-stack ambertools/21</code> || GCC, FlexiBLAS & FFTW
|-
| amber/20.12-20.15 || <code> StdEnv/2020 gcc/9.3.0 openmpi/4.0.3 amber/20.12-20.15 </code> || <code>StdEnv/2020  gcc/9.3.0 cuda/11.4 openmpi/4.0.3 amber/20.12-20.15</code>  || GCC, FlexiBLAS & FFTW
|-
| amber/20.9-20.15 || <code> StdEnv/2020 gcc/9.3.0 openmpi/4.0.3 amber/20.9-20.15 </code> || <code>StdEnv/2020  gcc/9.3.0 cuda/11.0 openmpi/4.0.3 amber/20.9-20.15 </code> || GCC, MKL & FFTW
|-
| amber/18.14-18.17 || <code> StdEnv/2020 gcc/9.3.0 openmpi/4.0.3 amber/18.14-18.17 </code> || <code>StdEnv/2020  gcc/8.4.0  cuda/10.2  openmpi/4.0.3 </code>  || GCC, MKL
|-
|}</tab>
<tab name="StdEnv/2016">
{| class="wikitable sortable"
|-
! AMBER version !! modules for running on CPUs !! modules for running on GPUs (CUDA) !! Notes
|-
| amber/18 || <code> StdEnv/2016 gcc/5.4.0 openmpi/2.1.1 scipy-stack/2019a amber/18 </code> || <code> StdEnv/2016 gcc/5.4.0 openmpi/2.1.1 cuda/9.0.176 scipy-stack/2019a amber/18</code>  || GCC, MKL
|-
| amber/18.10-18.11 || <code> StdEnv/2016 gcc/5.4.0 openmpi/2.1.1 scipy-stack/2019a amber/18.10-18.11 </code> || <code> StdEnv/2016 gcc/5.4.0 openmpi/2.1.1 cuda/9.0.176 scipy-stack/2019a amber/18.10-18.11</code>  || GCC, MKL
|-
| amber/18.10-18.11 || <code>StdEnv/2016 gcc/7.3.0 openmpi/3.1.2 scipy-stack/2019a amber/18.10-18.11 </code> || <code> StdEnv/2016 gcc/7.3.0  cuda/9.2.148 openmpi/3.1.2 scipy-stack/2019a amber/18.10-18.11 </code>  || GCC, MKL
|-
| amber/16 || <code> StdEnv/2016.4 amber/16 </code> || <code> </code>  || Available only on Graham. Some Python functionality is not supported
|}</tab>


module load gcc/5.4.0  cuda/9.0.176  openmpi/2.1.1 amber/18 scipy-stack/2019a
<!--T:43-->
or
</tabs>
module load gcc/5.4.0  cuda/9.0.176  openmpi/2.1.1 amber/18.10-18.11 scipy-stack/2019a


==Using modules== <!--T:23-->
===AmberTools 21===
Currently, AmberTools 21 module is available on all clusters. AmberTools provide the following MD engines: sander, sander.LES, sander.LES.MPI, sander.MPI, sander.OMP, sander.quick.cuda, and sander.quick.cuda.MPI. After loading the module set AMBER environment variables:


<!--T:44-->
source $EBROOTAMBERTOOLS/amber.sh


===Amber 20=== <!--T:30-->
There are two versions of amber/20 modules: 20.9-20.15 and 20.12-20.15. The first one uses MKL and cuda/11.0, while the second uses FlexiBLAS and cuda/11.4. MKL libraries do not perform well on AMD CPU, and FlexiBLAS solves this problem. It detects CPU type and uses libraries optimized for the hardware. cuda/11.4 is required for running simulations on A100 GPUs installed on Narval.


==Running Amber 16== <!--T:2-->
<!--T:45-->
Amber 16 is currently installed only on [[Graham]] due to license restrictions. Load it using the [[Using modules|module]] command:
CPU-only modules provide all MD programs available in AmberTools/20 plus pmemd (serial) and pmemd.MPI (parallel). GPU modules add pmemd.cuda (single GPU), and pmemd.cuda.MPI (multi - GPU).
[name@server $] module load amber/16


===Job submission=== <!--T:3-->
=== Known issues === <!--T:41-->
For a general discussion about submitting jobs, see [[Running jobs]].
1. Module amber/20.12-20.15 does not have MMPBSA.py.MPI executable.


<!--T:4-->
<!--T:46-->
The following example is a sander serial job script. The input files are <code>in.md, crd.md.23, prmtop</code>.
2. MMPBSA.py from amber/18-10-18.11 and amber/18.14-18.17 modules cannot perform PB calculations. Use more recent amber/20 modules for this type of calculations.
 
==Job submission examples== <!--T:37-->
=== Single GPU job ===
For GPU-accelerated simulations on Narval, use amber/20.12-20.15. Modules compiled with CUDA version < 11.4 do not work on A100 GPUs. Below is an example submission script for a single-GPU job with amber/20.12-20.15.
{{File
  |name=pmemd_cuda_job.sh
  |lang="bash"
  |contents=
#!/bin/bash
#SBATCH --ntasks=1
#SBATCH --gpus-per-node=1
#SBATCH --mem-per-cpu=2000
#SBATCH --time=10:00:00
 
<!--T:54-->
module purge
module load StdEnv/2023 gcc/12.3 openmpi/4.1.5 cuda/12.2 amber/22
 
<!--T:55-->
pmemd.cuda -O -i input.in -p topol.parm7 -c coord.rst7 -o output.mdout -r restart.rst7
}}
 
=== CPU-only parallel MPI job === <!--T:47-->
 
<!--T:56-->
<tabs>
<tab name="Graham">
{{File
  |name=pmemd_MPI_job_graham.sh
  |lang="sh"
  |contents=
#!/bin/bash
#SBATCH --nodes=4
#SBATCH --ntasks-per-node=32
#SBATCH --mem-per-cpu=2000
#SBATCH --time=1:00:00
 
<!--T:57-->
module purge
module load StdEnv/2023 gcc/12.3 openmpi/4.1.5 amber/22
 
<!--T:58-->
srun pmemd.MPI -O -i input.in -p topol.parm7 -c coord.rst7 -o output.mdout -r restart.rst7
}}</tab>
<tab name="Cedar">
{{File
  |name=pmemd_MPI_job_cedar.sh
  |lang="sh"
  |contents=
#!/bin/bash
#SBATCH --nodes=4
#SBATCH --ntasks-per-node=48
#SBATCH --mem-per-cpu=2000
#SBATCH --time=1:00:00
 
<!--T:59-->
module purge
module load StdEnv/2023 gcc/12.3 openmpi/4.1.5 amber/22
 
<!--T:60-->
srun pmemd.MPI -O -i input.in -p topol.parm7 -c coord.rst7 -o output.mdout -r restart.rst7
}}</tab>
<tab name="Béluga">
{{File
  |name=pmemd_MPI_job_beluga.sh
  |lang="sh"
  |contents=
#!/bin/bash
#SBATCH --nodes=4
#SBATCH --ntasks-per-node=40
#SBATCH --mem-per-cpu=2000
#SBATCH --time=1:00:00
 
<!--T:61-->
module purge
module load StdEnv/2023 gcc/12.3 openmpi/4.1.5 amber/22
 
<!--T:62-->
srun pmemd.MPI -O -i input.in -p topol.parm7 -c coord.rst7 -o output.mdout -r restart.rst7
}}</tab>
<tab name="Narval">
{{File
  |name=pmemd_MPI_job_narval.sh
  |lang="sh"
  |contents=
#!/bin/bash
#SBATCH --nodes=4
#SBATCH --ntasks-per-node=64
#SBATCH --mem-per-cpu=2000
#SBATCH --time=1:00:00
 
<!--T:63-->
module purge
module load StdEnv/2023 gcc/12.3 openmpi/4.1.5 amber/22
 
<!--T:64-->
srun pmemd.MPI -O -i input.in -p topol.parm7 -c coord.rst7 -o output.mdout -r restart.rst7
}}</tab>
<tab name="Niagara">
{{File
  |name=pmemd_MPI_job_narval.sh
  |lang="sh"
  |contents=
#!/bin/bash
#SBATCH --nodes=4
#SBATCH --ntasks-per-node=40
#SBATCH --mem-per-cpu=2000
#SBATCH --time=1:00:00
 
<!--T:65-->
module purge
module load StdEnv/2023 gcc/12.3 openmpi/4.1.5 amber/22
 
<!--T:66-->
srun pmemd.MPI -O -i input.in -p topol.parm7 -c coord.rst7 -o output.mdout -r restart.rst7
}}</tab>
</tabs>
 
=== QM/MM distributed multi-GPU job === <!--T:48-->
The example below requests eight GPUs.
{{File
{{File
   |name=amber_serial.sh
   |name=quick_MPI_job.sh
   |lang="bash"
   |lang="bash"
   |contents=
   |contents=
#!/bin/bash
#!/bin/bash
#SBATCH --ntasks=1             # 1 cpu, serial job
#SBATCH --ntasks=8 --cpus-per-task=1
#SBATCH --mem-per-cpu=2G      # memory per cpu
#SBATCH --gpus-per-task=1  
#SBATCH --time=00-01:00       # time (DD-HH:MM)
#SBATCH --mem-per-cpu=4000
#SBATCH --output=cytosine.log  # .log file from scheduler
#SBATCH --time=02:00:00
module load amber/16
 
sander -O -i in.md  -c crd.md.23  -o cytosine.out
<!--T:52-->
module purge
module load StdEnv/2023 gcc/12.3 openmpi/4.1.5 cuda/12.2 ambertools/23.5
 
<!--T:53-->
srun sander.quick.cuda.MPI -O -i input.in -p topol.parm7 -c coord.rst7 -o output.mdout -r restart.rst7
}}
}}


<!--T:5-->
=== Parallel MMPBSA job === <!--T:6-->
The following example is a sander.MPI parallel job script:
The example below uses 32 MPI processes. MMPBSA scales linearly because each trajectory frame is processed independently.  
{{File
{{File
   |name=amber_parallel.sh
   |name=mmpbsa_job.sh
   |lang="bash"
   |lang="bash"
   |contents=
   |contents=
#!/bin/bash
#!/bin/bash
#SBATCH --nodes=1 --ntasks-per-node=32 # 1 node with 32 cpus, MPI job
#SBATCH --ntasks=32  
#SBATCH --mem-per-cpu=2G                # memory, should be less than 4G
#SBATCH --mem-per-cpu=4000
#SBATCH --time=00-01:00                 # time (DD-HH:MM)
#SBATCH --time=1:00:00
#SBATCH --output=sodium.log            # output .log file
 
module load amber/16
<!--T:67-->
srun sander.MPI -ng 2 -groupfile groups
module purge
module load module load StdEnv/2023 gcc/12.3 openmpi/4.1.5 amber/22
 
<!--T:68-->
srun MMPBSA.py.MPI -O -i mmpbsa.in -o mmpbsa.dat -sp solvated_complex.parm7 -cp complex.parm7 -rp receptor.parm7 -lp ligand.parm7 -y trajectory.nc
}}
}}
You can modify scripts to fit your simulation requirements for computing resources. See [[Running jobs]] for more details.
==Performance and benchmarking== <!--T:69-->
<!--T:70-->
A team at [https://www.ace-net.ca/ ACENET] has created a [https://mdbench.ace-net.ca/mdbench/ Molecular Dynamics Performance Guide] for Alliance clusters.
It can help you determine optimal conditions for AMBER, GROMACS, NAMD, and OpenMM jobs. The present section focuses on AMBER performance.


<!--T:6-->
<!--T:50-->
You can modify the script to fit your job's requirements for compute resources. See [[Running jobs]].
View benchmarks of simulations with PMEMD[http://mdbench.ace-net.ca/mdbench/bform/?software_contains=PMEMD&software_id=&module_contains=&module_version=&site_contains=&gpu_model=&cpu_model=&arch=&dataset=6n4o]  


===Examples=== <!--T:7-->
<!--T:51-->
Sample *.sh and input files can be found on Graham under
View benchmarks of QM/MM simulations with SANDER.QUICK [http://mdbench.ace-net.ca/mdbench/bform/?software_contains=&software_id=&module_contains=&module_version=&site_contains=&gpu_model=&cpu_model=&arch=&dataset=4cg1].
/home/jemmyhu/tests/test_Amber/
</translate>
</translate>

Latest revision as of 16:55, 16 October 2024

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Introduction[edit]

Amber is the collective name for a suite of programs that allow users to perform molecular dynamics simulations, particularly on biomolecules. None of the individual programs carry this name, but the various parts work reasonably well together, and provide a powerful framework for many common calculations.

Amber vs. AmberTools[edit]

We have modules for both Amber and AmberTools available in our software stack.

  • The AmberTools (module ambertools) contains a number of tools for preparing and analyzing simulations, as well as sander to perform molecular dynamics simulations, all of which are free and open source.
  • Amber (module amber) contains everything that is included in ambertools, but adds the advanced pmemd program for molecular dynamics simulations.

To see a list of installed versions and which other modules they depend on, you can use the module spider command or check the Available software page.

Loading modules[edit]

AMBER version modules for running on CPUs modules for running on GPUs (CUDA) Notes
amber/22.5-23.5 StdEnv/2023 gcc/12.3 openmpi/4.1.5 amber/22.5-23.5 StdEnv/2023 gcc/12.3 openmpi/4.1.5 cuda/12.2 amber/22.5-23.5 GCC, FlexiBLAS & FFTW
ambertools/23.5 StdEnv/2023 gcc/12.3 openmpi/4.1.5 ambertools/23.5 StdEnv/2023 gcc/12.3 openmpi/4.1.5 cuda/12.2 ambertools/23.5 GCC, FlexiBLAS & FFTW
AMBER version modules for running on CPUs modules for running on GPUs (CUDA) Notes
ambertools/21 StdEnv/2020 gcc/9.3.0 openmpi/4.0.3 scipy-stack ambertools/21 StdEnv/2020 gcc/9.3.0 cuda/11.4 openmpi/4.0.3 scipy-stack ambertools/21 GCC, FlexiBLAS & FFTW
amber/20.12-20.15 StdEnv/2020 gcc/9.3.0 openmpi/4.0.3 amber/20.12-20.15 StdEnv/2020 gcc/9.3.0 cuda/11.4 openmpi/4.0.3 amber/20.12-20.15 GCC, FlexiBLAS & FFTW
amber/20.9-20.15 StdEnv/2020 gcc/9.3.0 openmpi/4.0.3 amber/20.9-20.15 StdEnv/2020 gcc/9.3.0 cuda/11.0 openmpi/4.0.3 amber/20.9-20.15 GCC, MKL & FFTW
amber/18.14-18.17 StdEnv/2020 gcc/9.3.0 openmpi/4.0.3 amber/18.14-18.17 StdEnv/2020 gcc/8.4.0 cuda/10.2 openmpi/4.0.3 GCC, MKL
AMBER version modules for running on CPUs modules for running on GPUs (CUDA) Notes
amber/18 StdEnv/2016 gcc/5.4.0 openmpi/2.1.1 scipy-stack/2019a amber/18 StdEnv/2016 gcc/5.4.0 openmpi/2.1.1 cuda/9.0.176 scipy-stack/2019a amber/18 GCC, MKL
amber/18.10-18.11 StdEnv/2016 gcc/5.4.0 openmpi/2.1.1 scipy-stack/2019a amber/18.10-18.11 StdEnv/2016 gcc/5.4.0 openmpi/2.1.1 cuda/9.0.176 scipy-stack/2019a amber/18.10-18.11 GCC, MKL
amber/18.10-18.11 StdEnv/2016 gcc/7.3.0 openmpi/3.1.2 scipy-stack/2019a amber/18.10-18.11 StdEnv/2016 gcc/7.3.0 cuda/9.2.148 openmpi/3.1.2 scipy-stack/2019a amber/18.10-18.11 GCC, MKL
amber/16 StdEnv/2016.4 amber/16 Available only on Graham. Some Python functionality is not supported

Using modules[edit]

AmberTools 21[edit]

Currently, AmberTools 21 module is available on all clusters. AmberTools provide the following MD engines: sander, sander.LES, sander.LES.MPI, sander.MPI, sander.OMP, sander.quick.cuda, and sander.quick.cuda.MPI. After loading the module set AMBER environment variables: 

source $EBROOTAMBERTOOLS/amber.sh

Amber 20[edit]

There are two versions of amber/20 modules: 20.9-20.15 and 20.12-20.15. The first one uses MKL and cuda/11.0, while the second uses FlexiBLAS and cuda/11.4. MKL libraries do not perform well on AMD CPU, and FlexiBLAS solves this problem. It detects CPU type and uses libraries optimized for the hardware. cuda/11.4 is required for running simulations on A100 GPUs installed on Narval.

CPU-only modules provide all MD programs available in AmberTools/20 plus pmemd (serial) and pmemd.MPI (parallel). GPU modules add pmemd.cuda (single GPU), and pmemd.cuda.MPI (multi - GPU).

Known issues[edit]

1. Module amber/20.12-20.15 does not have MMPBSA.py.MPI executable.

2. MMPBSA.py from amber/18-10-18.11 and amber/18.14-18.17 modules cannot perform PB calculations. Use more recent amber/20 modules for this type of calculations.

Job submission examples[edit]

Single GPU job[edit]

For GPU-accelerated simulations on Narval, use amber/20.12-20.15. Modules compiled with CUDA version < 11.4 do not work on A100 GPUs. Below is an example submission script for a single-GPU job with amber/20.12-20.15.

File : pmemd_cuda_job.sh

#!/bin/bash
#SBATCH --ntasks=1 
#SBATCH --gpus-per-node=1 
#SBATCH --mem-per-cpu=2000 
#SBATCH --time=10:00:00

module purge
module load StdEnv/2023 gcc/12.3 openmpi/4.1.5 cuda/12.2 amber/22

pmemd.cuda -O -i input.in -p topol.parm7 -c coord.rst7 -o output.mdout -r restart.rst7


CPU-only parallel MPI job[edit]

File : pmemd_MPI_job_graham.sh

#!/bin/bash
#SBATCH --nodes=4
#SBATCH --ntasks-per-node=32
#SBATCH --mem-per-cpu=2000
#SBATCH --time=1:00:00

module purge
module load StdEnv/2023 gcc/12.3 openmpi/4.1.5 amber/22

srun pmemd.MPI -O -i input.in -p topol.parm7 -c coord.rst7 -o output.mdout -r restart.rst7
File : pmemd_MPI_job_cedar.sh

#!/bin/bash
#SBATCH --nodes=4
#SBATCH --ntasks-per-node=48
#SBATCH --mem-per-cpu=2000
#SBATCH --time=1:00:00

module purge
module load StdEnv/2023 gcc/12.3 openmpi/4.1.5 amber/22

srun pmemd.MPI -O -i input.in -p topol.parm7 -c coord.rst7 -o output.mdout -r restart.rst7
File : pmemd_MPI_job_beluga.sh

#!/bin/bash
#SBATCH --nodes=4
#SBATCH --ntasks-per-node=40
#SBATCH --mem-per-cpu=2000
#SBATCH --time=1:00:00

module purge
module load StdEnv/2023 gcc/12.3 openmpi/4.1.5 amber/22

srun pmemd.MPI -O -i input.in -p topol.parm7 -c coord.rst7 -o output.mdout -r restart.rst7
File : pmemd_MPI_job_narval.sh

#!/bin/bash
#SBATCH --nodes=4
#SBATCH --ntasks-per-node=64
#SBATCH --mem-per-cpu=2000
#SBATCH --time=1:00:00

module purge
module load StdEnv/2023 gcc/12.3 openmpi/4.1.5 amber/22

srun pmemd.MPI -O -i input.in -p topol.parm7 -c coord.rst7 -o output.mdout -r restart.rst7
File : pmemd_MPI_job_narval.sh

#!/bin/bash
#SBATCH --nodes=4
#SBATCH --ntasks-per-node=40
#SBATCH --mem-per-cpu=2000
#SBATCH --time=1:00:00

module purge
module load StdEnv/2023 gcc/12.3 openmpi/4.1.5 amber/22

srun pmemd.MPI -O -i input.in -p topol.parm7 -c coord.rst7 -o output.mdout -r restart.rst7

QM/MM distributed multi-GPU job[edit]

The example below requests eight GPUs.

File : quick_MPI_job.sh

#!/bin/bash
#SBATCH --ntasks=8 --cpus-per-task=1
#SBATCH --gpus-per-task=1 
#SBATCH --mem-per-cpu=4000 
#SBATCH --time=02:00:00

module purge
module load StdEnv/2023 gcc/12.3 openmpi/4.1.5 cuda/12.2 ambertools/23.5

srun sander.quick.cuda.MPI -O -i input.in -p topol.parm7 -c coord.rst7 -o output.mdout -r restart.rst7


Parallel MMPBSA job[edit]

The example below uses 32 MPI processes. MMPBSA scales linearly because each trajectory frame is processed independently.

File : mmpbsa_job.sh

#!/bin/bash
#SBATCH --ntasks=32 
#SBATCH --mem-per-cpu=4000 
#SBATCH --time=1:00:00

module purge
module load module load StdEnv/2023 gcc/12.3 openmpi/4.1.5 amber/22

srun MMPBSA.py.MPI -O -i mmpbsa.in -o mmpbsa.dat -sp solvated_complex.parm7 -cp complex.parm7 -rp receptor.parm7 -lp ligand.parm7 -y trajectory.nc


You can modify scripts to fit your simulation requirements for computing resources. See Running jobs for more details.

Performance and benchmarking[edit]

A team at ACENET has created a Molecular Dynamics Performance Guide for Alliance clusters. It can help you determine optimal conditions for AMBER, GROMACS, NAMD, and OpenMM jobs. The present section focuses on AMBER performance.

View benchmarks of simulations with PMEMD[1]

View benchmarks of QM/MM simulations with SANDER.QUICK [2].

GNU Compiler Collection, an open source compiler collection

Intel Math Kernel Library, a software library of optimized math routines

Message Passing Interface

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