Background

This page provides background on running AMBER (PMEMD) with NVIDIA GPU acceleration.

One of the new features of AMBER 11 was the ability to use NVIDIA GPUs to massively accelerate PMEMD for both explicit solvent PME and implicit solvent GB simulations. This has been further extended in AMBER 12. While this GPU acceleration is considered to be production ready, and indeed is widely used, it has not been tested anywhere near as extensively as the CPU code has over the last 30 years. Therefore, users should still exercise caution when using this code. The error checking is not as verbose in the GPU code as it is on the CPU. If you encounter problems during a simulation on the GPU you should first try to run the identical simulation on the CPU to ensure that it is not your simulation setup which is causing problems. Feedback and questions should be posted to the Amber mailing list.

New in AMBER 12

AMBER 12 includes full GPU support in PMEMD and is a major update over AMBER 11. Focus for AMBER 12 has been on increasing the features available in the GPU accelerated code. Key new features in the AMBER 12 GPU version of PMEMD include:

1. Support for NMROpt = 1 (restraints and temp/pressure scaling)
2. Replica Exchange (1-D Temperature)
3. Simulated Annealing (via temp0 scaling)
4. Accelerated Molecular Dynamics
5. Isotropic Periodic Sum
6. Extra Points
7. IGB = 7 & 8
8. Jarzynski Sampling (NEW with bugfix.14)
9. GBSA support (NEW with bugfix.14)
10. Support for mask inputs (e.g. use of restraint_mask is now supported)
11. Support for Kepler I (K10), Kepler II (K20), GTX-Titan, GTX-7XX and GTX6XX GPUs
12. Faster precision model: Single Precision Fixed point (SPFP)

Authorship & Support

NVIDIA CUDA Implementation:

Scott Le Grand (NVIDIA)
Duncan Poole (NVIDIA)
Ross C. Walker (SDSC)*

*Corresponding author.

Further information relating to the specifics of the implementation, methods used to achieve performance while controlling accuracy, and details of validation are available or will be shortly from the following publications:

• Romelia Salomon-Ferrer; Andreas W. Goetz; Duncan Poole; Scott Le Grand;  & Ross C. Walker* "Routine microsecond molecular dynamics simulations with AMBER - Part II: Particle Mesh Ewald" , J. Chem. Theory Comput., 2013, 9 (9), pp 3878-3888 DOI: 10.1021/ct400314y
   
• Andreas W. Goetz; Mark J. Williamson; Dong Xu; Duncan Poole; Scott Le Grand;  & Ross C. Walker* "Routine microsecond molecular dynamics simulations with AMBER - Part I: Generalized Born", J. Chem. Theory Comput., 2012, 8 (5), pp 1542-1555, DOI: 10.1021/ct200909j
   
• Scott Le Grand; Andreas W. Goetz; & Ross C. Walker* "SPFP: Speed without compromise - a mixed precision model for GPU accelerated molecular dynamics simulations.", Comp. Phys. Comm, 2013, 184, pp374-380, DOI: 10.1016/j.cpc.2012.09.022
  

Funding for this work has been graciously provided by NVIDIA, The University of California (UC Lab 09-LR-06-117792), The National Science Foundation's (NSF) TeraGrid Advanced User Support Program through the San Diego Supercomputer Center and NSF SI2-SSE grants to Ross Walker (NSF1047875 / NSF1148276) and Adrian Roitberg (NSF1047919 / NSF1147910)

Citing the GPU Code

If you make use of any of this GPU support in your work please use the following citations:

• Romelia Salomon-Ferrer; Andreas W. Goetz; Duncan Poole; Scott Le Grand;  & Ross C. Walker* "Routine microsecond molecular dynamics simulations with AMBER - Part II: Particle Mesh Ewald" , J. Chem. Theory Comput., 2013, 9 (9), pp 3878-3888. DOI: 10.1021/ct400314y
   
• Andreas W. Goetz; Mark J. Williamson; Dong Xu; Duncan Poole; Scott Le Grand;  & Ross C. Walker* "Routine microsecond molecular dynamics simulations with AMBER - Part I: Generalized Born", J. Chem. Theory Comput., 2012, 8 (5), pp 1542-1555 , DOI: 10.1021/ct200909j
   
• D.A. Case, T.A. Darden, T.E. Cheatham, III, C.L. Simmerling, J. Wang, R.E. Duke, R. Luo, R.C. Walker, W. Zhang, K.M. Merz, B. Roberts, S. Hayik, A. Roitberg, G. Seabra, J. Swails, A.W. Goetz, I. Kolossvai, K.F. Wong, F. Paesani, J. Vanicek, R.M. Wolf, J. Liu, X. Wu, S.R. Brozell, T. Steinbrecher, H. Gohlke, Q. Cai, X. Ye, J. Wang, M.-J. Hsieh, G. Cui, D.R. Roe, D.H. Mathews, M.G. Seetin, R. Salomon-Ferrer, C. Sagui, V. Babin, T. Luchko, S. Gusarov, A. Kovalenko, and P.A. Kollman (2012), AMBER 12, University of California, San Francisco.

^

Supported Features

The GPU accelerated version of PMEMD 12, supports both explicit solvent PME or IPS simulations in all three canonical ensembles (NVE, NVT and NPT) and implicit solvent Generalized Born simulations. It has been designed to support as many of the standard PMEMD v12 features as possible, however, there are some current limitations that are detailed below. Some of these may be addressed in the near future, and patches released, with the most up to date list posted on the web page. The following options are NOT supported (as of the Jan 2013 AMBER GPU v12.2 update - bugfix.14):

Additionally there are some minor differences in the output format. For example the Ewald error estimate is NOT calculated when running on a GPU. It is recommended that you first run a short simulation using the CPU code to check the Ewald error estimate is reasonable and that your system is stable. With the exception of item 11 the above limitations are tested for in the code, however, it is possible that there are additional simulation features that have not been implemented or tested on GPUs.

^

Supported GPUs

GPU accelerated PMEMD has been implemented using CUDA and thus will only run on NVIDIA GPUs at present. Due to accuracy concerns with pure single precision the code makes use of fixed and double precision in several places. This places the requirement that the GPU hardware supports double precision meaning only GPUs with hardware revision 1.3 or 2.0 or 3.0 and later can be used. At the time of writing (Mar 2014) this comprises the following NVIDIA cards (* = untested):

GTX-Titan and GTX-780 cards require AMBER 12 up to and including bugfix.19 and NVIDIA Driver version >= 319.60.

GTX-Titan-Black Edition and GTX-780TI are NOT recommended at this time due to the high number of numerical failures observed we these cards in our AMBER certification testing with Exxact. We are working closely with NVIDIA to resolve this issue and an update will be posted here when available.

Note that you should ensure that all GPUs on which you plan to run PMEMD are connected to PCI-E 2.0 x 16 lane slots or better, especially when running in parallel across multiple GPUs. If this is not the case then you will likely see degraded performance, although this effect is lessened in serial if you write to the mdout or mdcrd files infrequently (e.g. every 2000 steps or so). Scaling over multiple GPUs within a single node is possible, if all are in x16 or better slots. It is also possible to run over multiple nodes using infiniband. Ideally this should be 1 GPU per node with QDR or better IB. The main advantage of AMBER's approach to GPU implementation over other implementations such as NAMD and Gromacs is that it is possible to run multiple single GPU runs on a single node with little or no slow down. If the node is setup with the GPUs in compute exclusive mode, as described below, then it will auto select available GPUs. For example a node with 4 GTX-Titan cards in could run 4 individual AMBER DHFR NVE calculations all at the same time without slowdown providing an aggregate throughput in excess of 400ns/day.

Optimum Hardware Designs for Running GPU Amber are provided on the Recommended Hardware page.

^

System Size Limits

In order to obtain the extensive speedups that we see with GPUs it is critical that all of the calculation take place on the GPU within the GPU memory. This avoids the performance hit that one takes copying to and from the GPU and also allows us to achieve extensive speedups for realistic size systems. This avoids the need to create systems with millions of atoms to show reasonable speedups even when sampling lengths are unrealistic. This unfortunately means that the entire calculation must fit within the GPU memory. Additionally we make use of a number of scratch arrays to achieve high performance. This means that the GPU memory usage can actually be higher than a typical CPU run. It also means, due to the way we had to initially implement parallel GPU support that the memory usage per GPU does NOT decrease as you increase the number of GPUs. This is something we hope to fix in the future but for the moment the atom count limitations imposed on systems by the GPU memory is roughly constant whether you run in serial or in parallel.

Since, unlike CPUs it is not possible to add more memory to a GPU (without replacing it entirely) and there is no concept of swap as there is on the CPU the size of the GPU memory imposes hard limits on the number of atoms supported in a simulation. Early on within the mdout file you will find information on the GPU being used and an estimate of the amount of GPU and CPU memory required:

|------------------- GPU DEVICE INFO --------------------
|
| CUDA Capable Devices Detected: 1
| CUDA Device ID in use: 0
| CUDA Device Name: Tesla C2070
| CUDA Device Global Mem Size: 6143 MB
| CUDA Device Num Multiprocessors: 14
| CUDA Device Core Freq: 1.15 GHz
|
|--------------------------------------------------------

...

| GPU memory information:
| KB of GPU memory in use: 4638979
| KB of CPU memory in use: 790531

The reported GPU memory usage is likely an underestimate and meant for guidance only to give you an idea of how close you are to the GPU's memory limit. Just because it is less than the available Device Global Mem Size does not necessarily mean that it will run. You should also be aware that the GPU's available memory is reduced by 1/9th if you have ECC turned on.

Memory usage is affected by the run parameters. In particular the size of the cutoff, larger cutoffs needing more memory, and the ensemble being used. Additionally the physical GPU hardware affects memory usage since the optimizations used are non-identical for different GPU types. Typically, for PME runs, memory usage runs:

NPT > NVT > NVE
NTT=3 > NTT=1 == NTT=2 > NTT=0

Use of restraints etc will also increase the amount of memory in use. As will the density of your system. The higher the density the more pairs per atom there are and thus the more GPU memory will be required. The following table provides an UPPER BOUND to the number of atoms supported as a function of GPU model (as of Jan 2013 v12.2 bugfix.14 patch). These numbers were estimated using boxes of TIP3P water (PME) and solvent caps of TIP3P water (GB). These had lower than optimum densities and so you may find you are actually limited for dense solvated proteins to around 20% less than the numbers here. Nevertheless these should provide reasonable estimates to work from.

All numbers are for SPFP precision and are approximate limits. The actual limits will depend on system density, simulation settings etc. These numbers are thus designed to serve as guidelines only.

Explicit Solvent (PME)

8 angstrom cutoff. Cubic box of TIP3P Water, NTT=3, NTB=1, NTP=1,NTF=2,NTC=2,DT=0.002.

Implicit Solvent (GB)

No cutoff, Sphere of TIP3P Water (for testing purposes only), NTT=3, NTB=0, NTF=2, NTC=2, DT=0.002, IGB=1

Note: The Kepler patch (Aug 2012 v12.1 - bugfix.9) reduced memory usage substantially for GB simulations, thereby allowing maximum atom counts similar to that for explicit solvent. It also optimized explicit solvent memory usage and thus increased the maximum atom counts for PME by approximately 20 to 30%.

^

Accuracy Considerations

The nature of current generation GPUs is such that single precision arithmetic is considerably faster (>8x for C1060, >2x for C2050 and > 20x for GTX680) than double precision arithmetic. This poses an issue when trying to obtain good performance from GPUs. Traditionally the CPU code in Amber has always used double precision throughout the calculation. While this full double precision approach has been implemented in the GPU code it gives very poor performance. Thus AMBER GPU implementation was initially written to use a combination of single and double precision, termed hybrid precision (SPDP), that is discussed in further detail in the references provided at the beginning of this page. This approach used single precision for individual calculations within the simulation but double precision for all accumulations. It also used double precision for shake calculations and for other parts of the code where loss of precision was deemed to be unacceptable. Tests have shown that energy conservation is equivalent to the full double precision code and specific ensemble properties, such as order parameters, match the full double precision CPU code. The user should understand though that this approach leads to divergence between GPU and CPU simulations, similar to that observed when running the CPU code across different processor counts in parallel but occurring more rapidly. For this reason the GPU test cases are more sensitive to rounding differences caused by hardware and compiler variations and will likely require manual inspection of the test case diff files in order to verify that the installation is providing correct results. There can also be differences between CPU and GPU runs and between GPU and GPU on different GPU models for runs that rely on the random number stream. For example NTT=2 and NTT=3. This is because the random number stream is different between CPUs and GPUs.

The Kepler patch (Aug 2012, GPU V12.1 - bugfix.9) changed the default precision model to a new hybrid model that combines single precision calculation with fixed precision accumulation. This is described in detail in the following manuscript and tests have shown that it provides accuracy that is as good or better than the original SPDP model. This new precision model is supported on v2.0 hardware and newer (it will run on v1.3 hardware but may be slower than SPDP) and is especially beneficial on Kepler hardware.

Scott Le Grand; Andreas W. Goetz; & Ross C. Walker* "SPFP: Speed without compromise - a mixed precision model for GPU accelerated molecular dynamics simulations.", Comp. Phys. Comm, 2013, 184, pp374-380, DOI: 10.1016/j.cpc.2012.09.022

While the default precision model is currently the new SPFP model four different precision models have been implemented within the GPU code to facilitate advanced testing and comparison. The precision models supported, and determined at compile time as described later, are:

• SPSP - Use single precision for the entire calculation with the exception of SHAKE which is always done in double precision. This provides the highest performance and is probably the most directly comparable model to other GPU MD implementations. However, insufficient testing has been done to know if the use of single precision throughout the simulation (certainly NVE simulations are not possible) is acceptable, we see stability issues as described in 10.1021/ct200909j, and thus this precision model should be used for testing and debugging purposes only. Note this model has been deprecated and is no longer supported as of AMBER 12 bugfix.9 (Aug 2012 GPU v12.1)
   
• SPFP - (Default) Uses a combination of single precision for calculation and fixed (integer) precision for accumulation. This approach provides optimum speedup on Kepler cards, minimizes memory overhead and provides greater net precision that the SPDP model. It is designed for optimum performance on hardware revision 2.0 or later. SPFP will run on v1.3 hardware but may not give optimum performance so you may prefer to build the SPDP version manually if you have v1.3 hardware.
   
• SPDP - Use a combination of single precision for calculation and double precision for accumulation. This approach provided the optimum tradeoff (prior to SPFP) between accuracy and performance and hence at the time of the AMBER11/12 releases was the default model invoked when using the executable pmemd.cuda / pmemd.cuda.MPI. It is now recommended that you use the SPFP model unless you are utilizing hardware earlier than revision 2.0 in which performance with SPDP can be better.
   
• DPDP - Use double precision for the entire calculation. This provides for careful regression testing against the CPU code. It makes no additional approximations above and beyond the CPU implementation and would be the model of choice if performance was not a consideration. On v1.3 NVIDIA hardware (e.g. C1060) the performance is approximately a fifth that of the SPDP model, on v2.0 NVIDIA hardware (e.g. C2050) the performance is approximately half that of the SPDP model (1/8th for GTX cards) while on v3.0 NVIDIA hardware (e.g. K10, GTX6XX) the performance is 1/25th that of the SPDP model.

Recommendation for Minimization

One limitation of the SPFP precision model is that force can be truncated if they overflow the fixed precision representation. This should never be a problem during MD simulations for any well behaved system. However, for minimization or very early in the heating phase it can present a problem. This is especially true if two atoms are close to each other and thus have large VDW repulsions. It is recommended therefore that for minimization you use either the CPU version of the code or compile the SPDP model and use that. Only in situations where you are confident the structure is reasonable, for example if it was a snapshot from dynamics, should you use the GPU code (SPFP) for minization.

^

Installation and Testing

The single GPU version of PMEMD is called pmemd.cuda while the multi-GPU version is called pmemd.cuda.MPI. These are built separately from the standard serial and parallel installations. Before attempting to build the GPU versions of PMEMD you should have built and tested at least the serial version of Amber and preferably the parallel version as well. This will help ensure that basic issues relating to standard compilation on your hardware and operating system do not lead to confusion with GPU related compilation and testing problems. You should also be reasonably familiar with Amber’s compilation and test procedures. The minimum requirements for building the GPU version of PMEMD are, as of the AMBER 12 release:

• NVIDIA Toolkit v4.2, 5.0 or 5.5. (v5.0 recommended)
   
• NVIDIA GPU supporting Hardware Revision 1.3 or 2.0 or 3.0 and later. (This excludes revision 1.5)
   
• NVIDIA CUDA Driver v295.41 or later (v319.60 or later required for GTX-780 and GTX-Titan)
   
• MPI library supporting MPI v2 or later for parallel GPU. (MVAPICH2 v1.8 or later / MPICH2 v1.4p1 or later recommended, OpenMPI is NOT recommended.)

It is assumed that you have already correctly installed and tested CUDA support on your GPU. Before attempting to build pmemd.cuda you should make sure you have correctly installed both the NVIDIA Toolkit (nvcc compiler) and a CUDA supporting NVIDIA driver. You may want to try downloading the NVIDIA CUDA SDK (available from http://www.nvidia.com/ or included as 'samples' in the toolkit v5.0 or later) and see if you can build that. In particular you should try to build and run the deviceQuery program provided with this SDK since the output from this can be helpful in debugging things on the Amber mailing list. Additionally the environment variable CUDA_HOME should be set to point to your NVIDIA Toolkit installation and $CUDA_HOME/bin/ should be in your path.

Building and Testing the Default SPFP Precision Model

Single GPU (pmemd.cuda)

Assuming you have a working CUDA installation you can build the single GPU version, pmemd.cuda, using the default SPFP precision model as follows:

cd $AMBERHOME
make clean
./configure -cuda gnu    (or intel)
make install

Next you can run the tests using the default GPU (the one with the largest memory) with:

make test.cuda

The majority of these tests should pass. However, given the parallel nature of GPUs, meaning the order of operation is not well defined, the limited precision of the SPFP precision model, and variations in the random number generator on different GPU hardware, it is not uncommon for there to be several possible failures. You may also see some tests, particularly the cellulose test, fail on GPUs with limited memory. You should inspect the diff file created in the $AMBERHOME/logs/test_amber_cuda/ directory to manually verify any possible failures. Differences which occur on only a few lines and are minor in nature can be safely ignored. Any large differences, or if you are unsure, should be posted to the Amber mailing list for comment.

Multiple GPU (pmemd.cuda.MPI)

Once you have built and tested the serial GPU version you can optionally build the parallel version (if you have multiple GPUs of the same model). Unlike the CPU code it is not necessary to build the parallel version of the GPU code in order to access specific simulation options (except REMD). Thus you only need to build the parallel GPU code if you plan to run a single calculation across multiple GPUs. You should note that at the present time scaling is not ideal across multiple GPUs. The recommended approach for multi-GPU nodes is to run several independent calculations where each uses a different GPU. The instructions here assume that you can already successfully build the MPI version of the CPU code. If you cannot, then you should focus on solving this before you move on to attempting to build the parallel GPU version.

The parallel GPU version of the code works using MPI and requires support for MPI v2.0 or later. We recommend using MVAPICH2, or MPICH2. OpenMPI tends to give poor performance and may not support all MPI v2.0 features. It is also recommended that you build your own version of MPI (or one of the ones included with AMBER) and NOT rely on a system installed copy (supercomputers and managed clusters excepted). The reason for this is that system installed MPI's can lack Fortran support, or full MPI v2.0 support and also often have files in non-standard locations (relative to MPI_HOME) and so can cause issues when compiling.

Additionally if you plan on running over multiple nodes you should enable GPU Direct if your hardware supports it. Your MPI installation must also have been configured to support Fortran 90, C and C++ and all three compilers must be compatible. E.g. mixing of GNU C/C++ with Intel Fortran 90 is NOT supported.

You can build the multi-GPU code as follows:

cd $AMBERHOME
make clean
./configure -cuda -mpi gnu    (or intel)
make install

Next you can run the tests using GPUs enumerated sequentially within a node (if you have multiple nodes or more complex GPU setups within a node then you should refer to the discussion below on running on multiple GPUs):

export DO_PARALLEL='mpirun -np 2' # for bash/sh
setenv DO_PARALLEL 'mpirun -np 2' # for csh/tcsh
./test_amber_cuda_parallel.sh

Segfaults in Parallel: If you find that runs across multiple nodes (i.e. using the infiniband adapter) segfault almost immediately then this is most likely an issue with GPU Direct v2 (CUDA v4.2) not being properly supported by your hardware and driver installations. In most cases setting the following environment variable on all nodes (put it in your .bashrc) will fix the problem:

export CUDA_NIC_INTEROP=1

Building non-standard Precision Models

You can build different precision models as described below. However, be aware that this is meant largely as a debugging and testing issue and NOT for running production calculations. Please post any questions or comments you may have regarding this to the Amber mailing list. You should also be aware that the variation in test case results due to rounding differences will be markedly higher when testing the SPDP precision model. You select which precision model to compile as follows:

cd $AMBERHOME
make clean
./configure -cuda_DPDP gnu (use -cuda_SPDP for the SPDP model)
make install

This will produce executables named pmemd.cuda_XXXX where XXXX is the precision model selected at configure time (SPDP or DPDP). You can then test this on the GPU with the most memory as follows:

cd $AMBERHOME/test/
./test_amber_cuda.sh DPDP (to test the DPDP precision model)

Testing Alternative GPUs

Should you wish to run the serial GPU tests on a GPU different from the one with the most memory (and lowest GPU ID if more than one identical GPU exists) then you can provide this by setting the CUDA_VISIBLE_DEVICES environment variable. For example, to test the GPU with ID = 2 and the default SPFP precision model you would specify:

cd $AMBERHOME
export CUDA_VISIBLE_DEVICES=2
make test.cuda

^

Running GPU Accelerated Simulations

Single GPU

In order to run a single GPU accelerated MD simulation the only change required is to use the executable pmemd.cuda in place of pmemd. E.g.

$AMBERHOME/bin/pmemd.cuda -O -i mdin -o mdout -p prmtop \
-c inpcrd -r restrt -x mdcrd

This will automatically run the calculation on the GPU with the most memory even if that GPU is already in use (see below for system settings to have the code auto select unused GPUs). If you have only a single CUDA capable GPU in your machine then this is fine, however if you want to control which GPU is used, for example you have a Tesla C2050 (3GB) and a Tesla C2070 (6GB) in the same machine and want to use the C2050 which has less memory, or you want to run multiple independent simulations using different GPUs then you manually need to specify the GPU ID to use using the CUDA_VISIBLE_DEVICES environment variable. The environment variable CUDA_VISIBLE_DEVICES lists which devices are visible as a comma-separated string. For example, if your desktop has two tesla cards and a Quadro:

$ ./deviceQuery -noprompt | egrep "^Device"
Device 0: "Tesla C2050"
Device 1: "Tesla C2070"
Device 2: "Quadro FX 3800"

By setting CUDA_VISIBLE_DEVICES you can make only a subset of them visible to the runtime:

$ export CUDA_VISIBLE_DEVICES="0,2"
$ ./deviceQuery -noprompt | egrep "^Device"
Device 0: "Tesla C2050"
Device 1: "Quadro FX 3800"

Hence if you wanted to run two pmemd.cuda runs, with the first running on the C2050 and the second on the C2070 you would run as follows:

$ export CUDA_VISIBLE_DEVICES="0"
nohup $AMBERHOME/bin/pmemd.cuda -O -i mdin -o mdout -p prmtop \
-c inpcrd -r restrt -x mdcrd </dev/null &

$ export CUDA_VISIBLE_DEVICES="1"
nohup $AMBERHOME/bin/pmemd.cuda -O -i mdin -o mdout -p prmtop \
-c inpcrd -r restrt -x mdcrd </dev/null &

In this way you only ever expose a single GPU to the pmemd.cuda executable and so avoid issues with the running of multiple runs on the same GPU. This approach is the basis of how you can control GPU usage in parallel runs.

An alternative approach, and the one recommended if you have multiple GPUs in a single node is to set them Persistence and Compute Exclusive Modes. In this mode a GPU will reject more than one job. pmemd.cuda and pmemd.cuda.MPI are capable of detecting this and will automatically run on a free GPU. For example supposed you have 4 GPUs in your node and you run 5 calculations:

$AMBERHOME/bin/pmemd.cuda -O -i mdin -o mdout.1 -x mdcrd.1 -inf mdinfo.1 &
$AMBERHOME/bin/pmemd.cuda -O -i mdin -o mdout.2 -x mdcrd.2 -inf mdinfo.2 &
$AMBERHOME/bin/pmemd.cuda -O -i mdin -o mdout.3 -x mdcrd.3 -inf mdinfo.3 &
$AMBERHOME/bin/pmemd.cuda -O -i mdin -o mdout.4 -x mdcrd.4 -inf mdinfo.4 &
$AMBERHOME/bin/pmemd.cuda -O -i mdin -o mdout.5 -x mdcrd.5 -inf mdinfo.5 &

The disadvantage of this is that you need to have root permissions to set it and the setting is also lost on a reboot. We recommend that you add these settings to your system's startup scripts. In Redhat or Centos this can be accomplished with:

echo "nvidia-smi -pm 1" >> /etc/rc.d/rc.local
echo "nvidia-smi -c 3" >> /etc/rc.d/rc.local

#!/bin/bash

# gpu-info
# Written by Jodi Hadden, University of Georgia, 2011. Updated 2013.
# List GPUs by deviceQuery ID with corresponding temperature and utilization from NVIDIA-SMI 
# Works for NVIDIA-SMI 3.295.41 Driver Version: 295.41 for Tesla series cards

# Check for deviceQuery
if ! type deviceQuery >/dev/null
then
        echo "Please add the location of deviceQuery to your PATH!"
        exit
fi

# Make all GPUs temporarily visible
num_gpus=`nvidia-smi -a | grep "Attached GPUs" | cut -b35`
export CUDA_VISIBLE_DEVICES=`seq -s , 0 $num_gpus`

# Obtain PCI bus IDs that correspond to deviceQuery IDs, converting decimal to hex for use with NVIDIA-SMI
pci_bus_array=( $( ( echo "obase=16" ; deviceQuery -noprompt | grep "Device PCI Bus ID / PCI location ID" | cut -b50-60 | sed "s/\/ 0//" ) | bc ) )
rm -f deviceQuery.txt SdkMasterLog.csv

# Print a neat table
echo "==================================================="
echo -e "Device\tModel\t\tTemperature\tUtilization"
echo "==================================================="

# For each deviceQuery ID, use corresponding PCI bus ID to print temperature and utilization from NVIDIA-SMI 
gpu_id=0
while [ $gpu_id -lt ${#pci_bus_array[*]} ]
do

        # Get and print information from NVIDIA-SMI
        model=`nvidia-smi -a -i 0:${pci_bus_array[$gpu_id]}:0 | grep "Product Name" | cut -b35-45`
        temperature=`nvidia-smi -a -i 0:${pci_bus_array[$gpu_id]}:0 | grep -A1 "Temperature" | grep "Gpu" | cut -b35-45`
        utilization=`nvidia-smi -a -i 0:${pci_bus_array[$gpu_id]}:0 | grep -A1 "Utilization" | grep "Gpu" | cut -b35-45`

        echo -e "$gpu_id\t$model\t$temperature\t\t$utilization"

        let gpu_id=$gpu_id+1

done

echo "==================================================="

Reliable use of this method requires setting GPUs to persistence and exclusive process modes upon each reboot as described above.

#!/bin/bash

# gpu-free
# Written by Jodi Hadden, University of Georgia, 2011. Updated 2013.
# Print free GPU deviceQuery IDs to export as visible with CUDA_VISIBLE_DEVICES
# Works for NVIDIA-SMI 3.295.41 Driver Version: 295.41 for Tesla series cards

# Check for deviceQuery
if ! type deviceQuery >/dev/null
then
        echo "Please add the location of deviceQuery to your PATH!"
        exit
fi

# Make all GPUs temporarily visible
num_gpus=`nvidia-smi -a | grep "Attached GPUs" | cut -b35`
export CUDA_VISIBLE_DEVICES=`seq -s , 0 $num_gpus`

# Obtain PCI bus IDs that correspond to deviceQuery IDs, converting decimal to hex for use with NVIDIA-SMI
pci_bus_array=( $( ( echo "obase=16" ; deviceQuery -noprompt | grep "Device PCI Bus ID / PCI location ID" | cut -b50-60 | sed "s/\/ 0//" ) | bc ) )
rm -f deviceQuery.txt SdkMasterLog.csv

# For each PCI bus ID, check to see if that GPU is being utilized
gpu_id=0
while [ $gpu_id -lt ${#pci_bus_array[*]} ]
do

        # Get utilization from NVIDIA-SMI
        utilization=`nvidia-smi -a -i 0:${pci_bus_array[$gpu_id]}:0 | grep -A1 "Utilization" | grep "Gpu" | cut -b35-36`

        # If a GPU is not being utilized, add its deviceQuery ID to the free GPU array
        # Note: GPUs can show 1% utilization if NVIDIA-SMI is running in the background, so used -le 1 instead of -eq 0 here
        if [ $utilization -le 1 ]
        then
                free_gpu_array[$gpu_id]=$gpu_id
        fi

        let gpu_id=$gpu_id+1

done

# Print free GPUs to export as visible
free_gpus=`echo ${free_gpu_array[*]} | sed "s/ /,/g"`
echo $free_gpus

Multi GPU

When running in parallel across multiple GPUs and or nodes the selection of which GPU to run on becomes more complicated. Ideally you would have a batch scheduling system that will set everything up for you correctly, however, the following instructions provide a way to control this yourself. To understand how to control which GPUs are used it is first necessary to understand the GPU scheduling algorithm used by pmemd.cuda.MPI and where to look in the mdout file to verify which GPUs are actually being used.

When running in parallel pmemd.cuda.MPI keeps track of the GPU ID's already assigned on each node and will not reuse a GPU unless there are insufficient available for the number of MPI threads specified. Currently the assignment is 1 GPU per MPI thread and so if you want to run on 4 GPUs you would run with 4 MPI threads. It is your responsibility to ensure, when running across multiple nodes that threads are handed out sequentially to alternate nodes. For example if you had 4 dual quad core nodes, each with 1 GPU in it is essential that your MPI nodefile be laid out such that 'mpirun -np 4' will give you 1 thread on each of the 4 nodes and NOT 4 threads on the first node. This is normally accomplished by listing each node once in the nodefile. E.g. if you had 2 GPUs per node and 2 nodes then, to run across 4 GPUs your nodefile would typically look like this:

node0
node1

Considerations for Maximizing GPU Performance

There are a number of considerations above and beyond those typically used on a CPU for maximizing the performance achievable for a GPU accelerated PMEMD simulation. The following provides some tips for ensuring good performance.

1. Avoid using small values of NTPR, NTWX, NTWV, NTWE and NTWR. Writing to the output, restart and trajectory files too often can hurt performance even on CPU runs, however, this is more acute for GPU accelerated simulations because there is a substantial cost in copying data to and from the GPU. Performance is maximized when CPU to GPU memory synchronizations are minimized. This is achieved by computing as much as possible on the GPU and only copying back to CPU memory when absolutely necessary. There is an additional overhead in that performance is boosted by only calculating the energies when absolutely necessary, hence setting NTPR or NTWE to low values will result in excessive energy calculations. You should not set any of these values to less than 100 (except 0 to disable them) and ideally use values of 500 or more. >100000 for NTWR is ideal.
    
2. Avoid setting ntave /= 0. Turning on the printing of running averages results in the code means needing to calculate both energy and forces on every step. This can lead to a performance loss of 8% or more when running on the GPU. This can also affect performance on CPU runs although the difference is not as marked. Similar arguments apply to setting the value of ene_avg_sampling to small values.
    
3. Avoid using the NPT ensemble (NTB=2) when it is not required. Performance will generally be NVE~NVT>NPT. However, for explicit solvent simulations it is always necessary to run at least some NPT in order to allow the density to equilibrate. However, once this is done one can typically switch back to NVT for production.
    
4. Avoid the use of GBSA in implicit solvent GB simulations unless required. The GBSA term is calculated on the CPU and thus requires a synchronization between GPU and CPU memory on every MD step as opposed to every NTPR or NTWX steps when running without this option.
    
5. Use the Berendsen Thermostat (ntt=1) or Anderson Thermostat (ntt=2) instead of the Langevin Thermostat (ntt=3). Langevin simulations require very large numbers of random numbers which slows performance slightly.
    
6. Do not assume that for small systems the GPU will always be faster. Typically for GB simulations of less than 150 atoms and PME simulations of less than 9,000 atoms it is not uncommon for the CPU version of the code to outperform the GPU version on a single node. Typically the performance differential between GPU and CPU runs will increase as atom count increases. Additionally the larger the non-bond cutoff used the better the GPU to CPU performance gain will be.
    
7. When running in parallel across multiple GPUs you should NOT attempt to share nodes and thus interconnects. For example you should avoid running 2 separate MPI jobs on individual nodes. For example if you have 2 nodes, each with a QDR IB card in, 1 C2050 and 1 C1060 you will likely get very poor performance if you attempt to run a dual GPU job on the 2 C2050's and a second dual GPU job on the 2 C1060's. It is also not advisable to mix GPU models when running in parallel. In this situation you are advised to physically place both C2050's in one node and both C1060's in the other. You could of course run a dual C2050 job across the two nodes and then 2 single GPU jobs on each of the C1060's.
    
8. When running in parallel for maximum performance you should use the latest interconnect technology. At the time of writing this is Gen 2 QDR IB. You should also make sure it is setup for GPU Direct support (Mellanox IB cards) which will typically give between a 10 and 25% performance improvement depending on the specifics of the calculation being run. Ideally you should also make sure that ALL GPU cards AND infiniband cards are in full x16 slots. This means that they are electrically wired as x16 slots and not just physically x16 but in reality split across multiple slots making the slots effectively x8 or x4 slots. (beware of junk^tm like the Dell 8 way break out boxes)
    
9. Turn off ECC (C2050 and later). ECC can cost you up to 10% in performance and hurts parallel scaling. You should verify that your GPUs are working correctly, and not giving ECC errors for example before attempting this. You can turn this off on Fermi based cards and later by running the following command for each GPU ID as root, followed by a reboot:
   
   nvidia-smi -g 0 --ecc-config=0   (repeat with -g x for each GPU ID)
   
   Extensive testing of AMBER on a wide range of hardware has established that ECC has little to no benefit on the reliability of AMBER simulations. This is part of the reason it is acceptable (see recommended hardware) to use the GeForce gaming cards for AMBER simulations.
    
10. Turn on boost clocks if supported. Newer GPUs from NVIDIA, such as the K40, support boost clocks which allow the clock speed to be increased if there is power and temperature headroom. This must be turned on to obtain optimum performance with AMBER. If you have a K40 or newer GPU supporting boost clocks then run the following:
    
    sudo nvidia-smi -i 0 -ac 3004,875       which puts device 0 into the highest boost state.
    
    To return to normal do: sudo nvidia-smi -rac
    
    To enable this setting without being root do: nvidia-smi -acp 0

Recommended Hardware

For examples of hardware that has been optimized for running GPU AMBER simulations, in terms of both price and performance please see the following page.

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Benchmarks

Benchmarks are available on the following page.

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