Additional Information for GPU Workstations

Installation and usage of GENESIS on GPU machines are complicated. The scheme strongly depends on the user’s environment, and our explanation described here may not work in your machine. If you have a trouble in installing GENESIS, you may get a hint from here or here to solve the problem.


CUDA setup

You have to install CUDA Toolkit before GENESIS. Official package by NVIDIA is available for Fedora, OpenSUSE, RHEL, CentOS, SLES, Ubuntu. In addition to those distributions, some distributions has CUDA Toolkit package in their package system.


NVIDIA CUDA Toolkit package may be available in “non-free” component. If you need newer version, you have to download from the NVIDIA site.

# "apt-get" can be used instead
$ apt install nvidia-cuda-toolkit

The configure script will find the location of nvcc and libraries automatically. If configure fails to find files, please specify manually by using “--with-cuda” option.


In the case of Gentoo linux, CUDA packages are installed to /opt/cuda by the package manager:

$ emerge nvidia-cuda-sdk

So, the PATH and LIBRARY paths are already set, and there is no need to change .bashrc. But, if the configure command cannot find the path automatically, you have to give the path through “--with-cuda” as follows:

$ ./configure --enable-single --enable-gpu --with-cuda=/opt/cuda

Setup and installation of GENESIS

Make sure that the path to CUDA libraries is specified in .bashrc before the compilation. Here, we assume that cuda libraries are installed in /usr/local/cuda-6.5/.

export PATH=/usr/local/cuda-6.5/bin:$PATH
export LD_LIBRARY_PATH=/usr/local/cuda-6.5/lib64:/lib:$LD_LIBRARY_PATH

Status of the GPU cards is checked by the following command:

$ nvidia-smi

For the installation, we specify the “--enable-gpu” option in the configure command:

$ cd /home/user/genesis
$ ./configure --enable-single --enable-gpu
$ make
$ make install


After the job is submitted, the users are recommended to check whether GPU and CPU are working correctly by using the nvidia-smi and top commands, respectively. In the message displayed by the nvidia-smi command, if Pwr:Usage, Memory-Usage, and GPU-Util show larger values, and each MPI process is assigned to the corresponding GPU card ID, the calculation may work fine.

Important Notice: a GPU card will be assigned to a process. Single MPI process cannot use multiple GPU cards. On the other hand, multiple MPI processes can be assigned to a single GPU card. (Total # of processes >= GPU cards used) If there are 8 processes and 2 GPU cards (id: 0 or 1), 4 processes will be assigned to GPU of id 0, and the other 4 processes will be assigned to GPU of id 1.

When you use all available GPUs:

$ export OMP_NUM_THREADS=2 
$ mpirun -np 8 -cpus-per-proc 2 ./spdyn INP > log &

You don’t need to add special settings for GPU.

When you use NOT all GPUs:

$ export OMP_NUM_THREADS=2
$ mpirun -np 8 -cpus-per-proc 2 ./spdyn INP > log &

You have to specify GPU devices by their IDs. The device IDs can be checked by deviceQuery utility in CUDA samples or nvidia-smi command. Occasionally, IDs in deviceQuery and nvidia-smi are different. if you met problems in GPU IDs, please check the output of deviceQuery utility because deviceQuery shall use the same IDs as GENESIS.

Please note that NVIDIA GPU cards with compute capability < 3.5 are automatically ignored in GENESIS 1.1.5 or later. (On 1.1.4 or before, users should add special settings to ignore GPUs with CC < 3.5.) You may not need special settings when you use all available GPUs.

 deviceQuery can be found in CUDA sample directory (ex. /usr/local/cuda/samples/1_Utilities/deviceQuery). You may need to compile the executable. (You may also need to copy it to your directory before compilation.) Once successfully compiled, run it and check the IDs. For example,

$ ./deviceQuery | grep "^Device"
Device 0: "Tesla K20c"
Device 1: "Quadro K600"
Device 2: "Tesla K20c"

Old information (GENESIS 1.1.0-1.1.4)


Workstation with single GPU card

The following command is an example for hybrid MPI/OpenMP computation with 8 MPI processors and 2 OpenMP threads.

$ export OMP_NUM_THREADS=2 
$ mpirun -np 8 -cpus-per-proc 2 ./spdyn INP > log &
Workstation with multiple GPU cards (use all GPU cards)

The method depends on GENESIS version.

GENESIS 1.1.2 or later

You don’t need any special settings in this case. (Of course you can specify OpenMP thread number as usual. It is omitted just for simplicity in the following example.)

# Example
$ mpirun -np 8 ./spdyn INP > log &

In GENESIS 1.1.2 or later, spdyn automatically detects available GPU cards and uses them, where GPU id of mod(processid,# of processes) or (process id)%(# of processes) will be used by processidth process. If you wanna use only one of GPUs or GPUs of specified ids, please check next section.

GENESIS 1.1.1 or before

If you wanna use all GPU cards for single simulation, you need some wrapper script,, when running spdyn. This is available only for OpenMPI. If you are using non-OpenMPI library of MPI (such as IntelMPI), please update GENESIS version to 1.1.2 or later. Otherwise, it is very hard to use all the GPU cards in a single simulation.

# Example
$ export OMP_NUM_THREADS=2
$ mpirun -np 8 -cpus-per-proc 2 ./ ./spdyn INP > log &

This script assigns each process to a GPU. The following is the example for two GPU cards. If you have 3 or more GPUs, you must change the value colored with red.

gpuid=`expr ${lr} \% 2`
export CUDA_VISIBLE_DEVICES=${gpuid}

Workstation with multiple GPU cards (use NOT all GPU cards)

On GENESIS 1.1.5 or later, GPU cards with compute capability < 3.5 are automatically ignored.

You may have multiple GPU cards in a workstation. First, you need to know the GPU IDs. Assuming that you have two Tesla cards for GPGPU calculation and one Quadro card for Graphics Accelerator, you could find their device IDs by using deviceQuery. DeviceQuery is available in NVIDIA CUDA Samples. To compile deviceQuery, copy NVIDIA CUDA Sample from CUDA installed directory (ex. /usr/local/cuda/samples) to your directory, move to samples /1_Utilities/deviceQuery, and run make. GPU device IDs can be easily obtained by just running nvidia-smi command. You may use those IDs for first try. But IDs from nvidia-smi and this deviceQuery are occasionally different. If you met a trouble about GPU IDs,  please check deviceQuery output and use IDs of this output.

$ ./deviceQuery | grep "^Device"
Device 0: "Tesla K20c"
Device 1: "Quadro K600"
Device 2: "Tesla K20c"

In this example, Device IDs of Tesla cards are 0 and 2. For a single simulation on a Tesla GPU card, specify the GPU device ID using CUDA_VISIBLE_DEVICES environment variable as follows:

$ mpirun -np 8 ./spdyn INP > log &

For two independent simulations on two Tesla GPUs, specify the GPU device ID to each run using CUDA_VISIBLE_DEVICES:

$ mpirun -np 8 ./spdyn INP1 > log1 &

$ mpirun -np 8 ./spdyn INP2 > log2 &

For a single simulation using two Tesla GPUs, you may use the command as followed. (This is an example for hybrid MPI/OpenMP computation with two GPU cards.) Please note that this method depends on your GENESIS version.

How to do in GENESIS 1.1.2 or later

$ export OMP_NUM_THREADS=2
$ mpirun -np 8 -cpus-per-proc 2 ./spdyn INP > log &

CUDA_VISIBLE_DEVICES must contain GPU card IDs which are used in computations. GENESIS 1.1.2 or later no longer requires

How to do in GENESIS 1.1.0 or 1.1.1

$ export OMP_NUM_THREADS=2
$ mpirun -np 8 -cpus-per-proc 2 ./ ./spdyn INP > log & is a script (note that this is different from the sample above) to separate 8 MPI processes into two GPU cards:

gpuids=(0 2)
gpuid=${gpuids[`expr ${ompi_rank} \% ${#gpuids[@]}`]}
export CUDA_VISIBLE_DEVICES=${gpuid}

GPU IDs you want to use are specified at line 2 (numbers in blue, two Tesla K20 cards in this example) in wrap.shNvidia-smi command allows you to check if each MPI process is properly assigned to the corresponding GPU ID.

It must be noted here that this is available only in OpenMPI environment. If you are using multiple GPUs on non-OpenMPI environment (such as IntelMPI), please update GENESIS to 1.1.2 or later version. Otherwise, utilization of multiple GPUs might be very tricky.

You can check the activities of GPUs by nvidia-smi command.

$ nvidia-smi
Tue Jul 12 18:35:26 2016       
| NVIDIA-SMI 367.27                 Driver Version: 367.27                    |
| GPU  Name        Persistence-M| Bus-Id        Disp.A | Volatile Uncorr. ECC |
| Fan  Temp  Perf  Pwr:Usage/Cap|         Memory-Usage | GPU-Util  Compute M. |
|   0  Quadro K600         Off  | 0000:03:00.0     Off |                  N/A |
| 29%   58C    P8    N/A /  N/A |      0MiB /   980MiB |      0%      Default |
|   1  Tesla K20c          Off  | 0000:04:00.0     Off |                    0 |
| 32%   43C    P0    73W / 225W |    454MiB /  4742MiB |     52%      Default |
|   2  Tesla K20c          Off  | 0000:21:00.0     Off |                    0 |
| 35%   48C    P0    74W / 225W |    453MiB /  4742MiB |     47%      Default |
| Processes:                                                       GPU Memory |
|  GPU       PID  Type  Process name                               Usage      |
|    1     25390    C   ./spdyn                                        116MiB |
|    1     25394    C   ./spdyn                                        113MiB |
|    1     25398    C   ./spdyn                                        115MiB |
|    1     25401    C   ./spdyn                                        105MiB |
|    2     25392    C   ./spdyn                                        107MiB |
|    2     25396    C   ./spdyn                                        113MiB |
|    2     25400    C   ./spdyn                                        114MiB |
|    2     25402    C   ./spdyn                                        114MiB |

 The IDs of GPUs displayed by nvidia-smi are not always the same as those obtained from deviceQuery. In the above case the ID of Quadro from nvidia-smi is different from that from deviceQuery. You should use the GPU IDs from deviceQuery for assigning them to CUDA_VISIBLE_DEVICES.