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doku:gromacs [2022/06/23 13:24] – msiegel | doku:gromacs [2023/11/23 12:19] – [Many nodes with many GPUs] msiegel | ||
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====== GROMACS ====== | ====== GROMACS ====== | ||
- | ===== GPU Partition ===== | + | Our recommendation: |
- | First you have to decide on which hardware | + | - Use the **most recent version** of GROMACS |
- | this a '' | + | - Use the newest Hardware: use **1 GPU** on the partitions |
- | '' | + | - Do some **performance analysis** |
- | setup has different hardware, for example the partition | + | |
- | '' | + | |
- | with 2 hyperthreads each core, listed at [[doku: | + | |
- | The partition has to be set in the batch script, see the example | + | |
- | below. Thus here it makes sense to let GROMACS run on 8 threads | + | |
- | ('' | + | |
- | that, as this would lead to oversubscribing. GROMACS decides mostly on | + | |
- | its own how it wants to work, so don't be surprised if it ignores | + | |
- | settings like environment variables. | + | |
- | === Batch Script | + | In most cases it does not make sense to run on multiple GPU nodes with MPI; Whether using one or two GPUs per node. |
- | In order to be scheduled efficiently with [[doku: | + | |
- | script'' | + | ===== CPU or GPU Partition? ===== |
+ | |||
+ | First you have to decide on which hardware GROMACS should run, we call this a '' | ||
+ | |||
+ | ===== Installations ===== | ||
+ | |||
+ | Type ''spack find -l gromacs'' | ||
+ | |||
+ | Because of the low efficiency of GROMACS on many nodes with many GPUs via MPI, we do not provide '' | ||
+ | |||
+ | We provide the following GROMACS variants: | ||
+ | |||
+ | ==== GPU but no MPI ==== | ||
+ | |||
+ | We recommend GPU Nodes, use the '' | ||
+ | |||
+ | **cuda-zen**: | ||
+ | * Gromacs +cuda ~mpi, all compiled with **GCC** | ||
+ | |||
+ | Since the '' | ||
+ | |||
+ | ==== MPI but no GPU ==== | ||
+ | |||
+ | For Gromacs on CPU only but with MPI, use '' | ||
+ | |||
+ | **zen**: | ||
+ | * Gromacs +openmpi +blas +lapack ~cuda, all compiled with **GCC** | ||
+ | * Gromacs +openmpi +blas +lapack ~cuda, all compiled with **AOCC** | ||
+ | * | ||
+ | **skylake**: | ||
+ | * Gromacs +**open**mpi +blas +lapack ~cuda, all compiled with **GCC** | ||
+ | * Gromacs +**open**mpi +blas +lapack ~cuda, all compiled with **Intel** | ||
+ | * Gromacs +**intel**mpi +blas +lapack ~cuda, all compiled with **GCC** | ||
+ | * Gromacs +**intel**mpi +blas +lapack ~cuda, all compiled with **Intel** | ||
+ | |||
+ | In some of these packages, there is no '' | ||
+ | |||
+ | ===== Batch Script ===== | ||
+ | |||
+ | Write a '' | ||
* some SLURM parameters: the ''# | * some SLURM parameters: the ''# | ||
- | * exporting | + | * export |
- | * cleaning the environment: '' | + | * clean modules: '' |
- | * loading | + | * load modules: '' |
- | * last but not least starting the program in question: '' | + | * starting the program in question: '' |
- | < | + | < |
#!/bin/bash | #!/bin/bash | ||
#SBATCH --job-name=myname | #SBATCH --job-name=myname | ||
- | #SBATCH --partition=gpu_gtx1080single | + | #SBATCH --partition=zen2_0256_a40x2 |
+ | #SBATCH --qos=zen2_0256_a40x2 | ||
#SBATCH --gres=gpu: | #SBATCH --gres=gpu: | ||
- | #SBATCH --nodes=1 | ||
unset OMP_NUM_THREADS | unset OMP_NUM_THREADS | ||
Line 37: | Line 67: | ||
module purge | module purge | ||
- | module load gcc/7.3 nvidia/1.0 cuda/ | + | module load gromacs/2022.2-gcc-9.5.0-... |
- | + | gmx mdrun -s topol.tpr | |
- | gmx_mpi | + | |
</ | </ | ||
- | Type '' | + | Type '' |
- | get the job id, and your job will be scheduled, and executed | + | |
- | automatically. | + | |
- | === CPU / GPU Load === | + | ===== Performance ===== |
- | There is a whole page dedicated to [[doku: | ||
- | GROMACS the relevant sections are section [[doku: | ||
- | === Performance | + | ==== CPU / GPU Load ==== |
- | As an example we ran '' | + | There is a whole page dedicated to [[doku: |
- | options, where '' | + | |
- | actually care about the result. Without any options GROMACS already | + | |
- | runs fine (a). Setting the number of tasks (b,c) is not needed; if set | + | ==== Short Example ==== |
- | wrong can even slow the calculation down significantly (over | + | |
- | provisioning)! Enforcing | + | As a short example we ran '' |
- | we assume that the tasks are pinned automatically already. The only | + | |
- | improvement we had was using the '' | + | The following table lists our 5 tests: |
- | load on the GPU. This might not work however if we use more than one | + | |
- | GPU. | + | |
^ # ^ cmd ^ ns / day ^ cpu load / % ^ gpu load / % ^ notes ^ | ^ # ^ cmd ^ ns / day ^ cpu load / % ^ gpu load / % ^ notes ^ | ||
- | | a | -- | 160 | 100 | 80 | | | + | | a | '' |
- | | b | -ntomp 8 | 160 | 100 | 80 | | | + | | b | '' |
- | | c | -ntomp 16 | 140 | 40 | 70 | gromacs warning: over provisioning | | + | | c | '' |
- | | d | -pin on | 160 | 100 | 80 | | | + | | d | '' |
- | | e | -update gpu | 170 | 100 | 90 | | | + | | e | '' |
+ | |||
+ | |||
+ | ==== 7 Test Cases ==== | ||
+ | |||
+ | Since GROMACS is used in many and very different ways, it makes sense to | ||
+ | benchmark various scenarios: | ||
+ | |||
+ | - R-143a in hexane (20,248 atoms) with very high output rate | ||
+ | - a short RNA piece with explicit water (31,889 atoms) | ||
+ | - a protein inside a membrane surrounded by explicit water (80,289 atoms) | ||
+ | - a VSC users test case (50,897 atoms) | ||
+ | - a protein in explicit water (170,320 atoms) | ||
+ | - a protein membrane channel with explicit water (615,924 atoms) | ||
+ | - a huge virus protein (1,066,628 atoms) | ||
+ | |||
+ | Take a look at the test results resembling a similar case than your application. | ||
+ | |||
+ | In this chart we tested our various hardware on the 7 test cases, some recent GPUs like '' | ||
+ | |||
+ | < | ||
+ | { | ||
+ | series: [{ | ||
+ | name: 'Test 1', | ||
+ | data: [191, 144, 128, 125, 145, 127, 92, 62, 57, 60, 57, 29, 28, 27, 17, 7.4, 7.4] | ||
+ | }, { | ||
+ | name: 'Test 2', | ||
+ | data: [525, 442, 449, 455, 471, 317, 228, 228, 207, 193, 152, 73, 74, 61, 46, 18, 18] | ||
+ | }, { | ||
+ | name: 'Test 3', | ||
+ | data: [205, 143, 164, 130, 113, 164, 103, 66, 68, 58, 48, 24, 25, 23, 14, 6.2, 6] | ||
+ | }, { | ||
+ | name: 'Test 4', | ||
+ | data: [463, 333, 273, 246, 229, 276, 103, 165, 170, 158, 143, 69, 67, 54, 40, 16, 16] | ||
+ | }, { | ||
+ | name: 'Test 5', | ||
+ | data: [168, 139, 162, 147, 131, 174, 94, 61, 59, 58, 43, 18, 18, 22, 10, 5.2, 5] | ||
+ | }, { | ||
+ | name: 'Test 6', | ||
+ | data: [9.6, 8.1, 16, 8.4, 9.9, 7.3, 12, 4.3, 3.1, 3.1, 4.6, 1.7, 1.7, 1.6, 1, 0.4, 0.4] | ||
+ | }, { | ||
+ | name: 'Test 7', | ||
+ | data: [27.2, 13, 25, 21.8, 1.4, 24.6, 18, 8.6, 8, 7.6, 8, 3.1, 3.1, 3, 1.7, 0.7, 0.7] | ||
+ | }], | ||
+ | chart: { | ||
+ | type: ' | ||
+ | height: 350, | ||
+ | stacked: true, | ||
+ | }, | ||
+ | plotOptions: | ||
+ | bar: { | ||
+ | horizontal: true, | ||
+ | }, | ||
+ | }, | ||
+ | title: { | ||
+ | text: ' | ||
+ | }, | ||
+ | xaxis: { | ||
+ | categories: [ | ||
+ | "1x A40", | ||
+ | "1x RTX2080TI", | ||
+ | "1x A100", | ||
+ | "4x GTX1080 M", | ||
+ | "2x A40", | ||
+ | "8x GTX1080 M", | ||
+ | "2x A100", | ||
+ | "2x GTX1080 M", | ||
+ | "1x GTX1080 M", | ||
+ | "1x GTX1080 S", | ||
+ | "0x A100", | ||
+ | "0x GTX1080 M", | ||
+ | "0x A40", | ||
+ | "1x K20M", | ||
+ | "0x K20M", | ||
+ | "0x GTX1080 | ||
+ | "0x RTX2080TI", | ||
+ | ], | ||
+ | title: { | ||
+ | text: "ns / day" | ||
+ | }, | ||
+ | }, | ||
+ | legend: { | ||
+ | position: ' | ||
+ | horizontalAlign: | ||
+ | title: { | ||
+ | text: "Test #" | ||
+ | }, | ||
+ | } | ||
+ | } | ||
+ | </ | ||
+ | |||
+ | |||
+ | ==== Many GPUs ==== | ||
+ | |||
+ | In most cases 1 GPU is **better** than 2 GPUs! | ||
+ | |||
+ | In some cases, for example a large molecule like Test 7, you might want to run GROMACS on both GPUs. We strongly encourage you to test if you actually benefit from running with GPUs on many nodes. | ||
+ | |||
+ | To find out if more GPUs mean more work done we need some math: the parallel efficiency **η** is the ratio of the [[https:// | ||
+ | |||
+ | η = S(N) / N | ||
+ | |||
+ | In this chart we compare GROMACS parallel efficiency **η** of the 7 test cases with two GPU versus one GPU on VSC-5 '' | ||
+ | |||
+ | Set the number of GPUs on the node visible to GROMACS with '' | ||
+ | |||
+ | < | ||
+ | { | ||
+ | series: [{ | ||
+ | name: '2x A40', | ||
+ | data: [0.38, 0.45, 0.28, 0.25, 0.39, 0.52, 0.03] | ||
+ | }, { | ||
+ | name: '2x A100', | ||
+ | data: [0.36, 0.25, 0.31, 0.19, 0.29, 0.38, 0.36] | ||
+ | }], | ||
+ | chart: { | ||
+ | type: ' | ||
+ | height: 350, | ||
+ | // stacked: true, | ||
+ | // stackType: ' | ||
+ | }, | ||
+ | // plotOptions: | ||
+ | // bar: { | ||
+ | // | ||
+ | // }, | ||
+ | // }, | ||
+ | title: { | ||
+ | text: ' | ||
+ | }, | ||
+ | xaxis: { | ||
+ | categories: [ | ||
+ | "Test 1", | ||
+ | "Test 2", | ||
+ | "Test 3", | ||
+ | "Test 4", | ||
+ | "Test 5", | ||
+ | "Test 6", | ||
+ | "Test 7", | ||
+ | ], | ||
+ | // title: { | ||
+ | // text: "Test #" | ||
+ | // }, | ||
+ | }, | ||
+ | yaxis: { | ||
+ | title: { | ||
+ | text: " | ||
+ | }, | ||
+ | }, | ||
+ | legend: { | ||
+ | position: ' | ||
+ | horizontalAlign: | ||
+ | } | ||
+ | } | ||
+ | </ | ||
+ | |||
+ | |||
+ | ==== Many nodes with many GPUs ==== | ||
+ | |||
+ | In most cases one node is **better** than more nodes. | ||
+ | |||
+ | In some cases, for example a large molecule like Test 7, you might want to run GROMACS on multiple nodes in parallel using MPI, with multiple GPUs (one each node). We strongly encourage you to test if you actually benefit from running with GPUs on many nodes. GROMACS can perform worse on many nodes in parallel than on a single one, even considerably! | ||
+ | |||
+ | Run GROMACS on multiple nodes with: | ||
+ | |||
+ | <code bash> | ||
+ | #SBATCH --nodes 2 | ||
+ | gmx mdrun ... | ||
+ | </ | ||
+ | |||
+ | Take a look at the chapter [[doku: | ||
+ | |||
+ | < | ||
+ | { | ||
+ | series: [{ | ||
+ | name: 'Test 1', | ||
+ | data: [ 42.374, 40.176, 39.439, 38.252, 35.744, 30.811 ] | ||
+ | }, { | ||
+ | name: 'Test 2', | ||
+ | data: [ 82.513, 81.25, 84.805, 81.894, 72.589, 62.855 ] | ||
+ | }, { | ||
+ | name: 'Test 3', | ||
+ | data: [ 94.069, 99.788, 97.9, 100.509, 95.666, 83.485 ] | ||
+ | }, { | ||
+ | name: 'Test 4', | ||
+ | data: [ 115.179, 117.999, 115.028, 114.967, 103.8, 0 ] | ||
+ | }, { | ||
+ | name: 'Test 5', | ||
+ | data: [ 67.147, 76.027, 80.627, 80.903, 83.031, 68.702 ] | ||
+ | }, { | ||
+ | name: 'Test 6', | ||
+ | data: [ 10.612, 11.963, 10.996, 14.37, 35.482, 34.988 ] | ||
+ | }, { | ||
+ | name: 'Test 7', | ||
+ | data: [ 17.92, 21.604, 30.482, 37.497, 35.448, 43.254 ] | ||
+ | }], | ||
+ | chart: { | ||
+ | type: ' | ||
+ | height: 350, | ||
+ | stacked: true, | ||
+ | }, | ||
+ | plotOptions: | ||
+ | bar: { | ||
+ | horizontal: true, | ||
+ | }, | ||
+ | }, | ||
+ | title: { | ||
+ | text: ' | ||
+ | }, | ||
+ | xaxis: { | ||
+ | categories: [ | ||
+ | "1 Node", | ||
+ | "2 Nodes", | ||
+ | "4 Nodes", | ||
+ | "8 Nodes", | ||
+ | "16 Nodes", | ||
+ | "32 Nodes", | ||
+ | ], | ||
+ | title: { | ||
+ | text: "ns / day" | ||
+ | }, | ||
+ | }, | ||
+ | legend: { | ||
+ | position: ' | ||
+ | horizontalAlign: | ||
+ | title: { | ||
+ | text: "Test #" | ||
+ | }, | ||
+ | } | ||
+ | } | ||
+ | </ | ||
+ | |||
+ | Note: the computation timed out for 4 with 32 nodes, before gromacs was able to estimate a performance. We can safely assume this example case is going to be less performant on 32 than on fewer nodes too. | ||
+ | |||
+ | |||
+ | ==== Many ranks on many nodes with many GPUs==== | ||
+ | |||
+ | Quick summary: | ||
+ | * Most problems (Small): 1 or 2 ranks per node | ||
+ | * Large problem: 8 ranks per node | ||
+ | |||
+ | If you want to run GROMACS on multiple nodes and multiple GPUs in parallel using MPI, best | ||
+ | tell MPI how many processes should be launched on each node | ||
+ | '' | ||
+ | yourself with your specific application. Based on our tests listed in the following chart we | ||
+ | recommend 1 ranks per node for most (small) problems, and only for large problems up to 8 ranks per node: | ||
+ | |||
+ | <code bash> | ||
+ | #SBATCH --nodes 2 | ||
+ | mpirun -np 16 \ | ||
+ | | ||
+ | ... | ||
+ | | ||
+ | </ | ||
+ | |||
+ | The reason for this is that the graphics cards does more work than the CPU. GROMACS needs to copy data between different ranks on the CPUs and all GPUs, which takes more time with more ranks. GROMACS notices that and shows '' | ||
+ | |||
+ | < | ||
+ | { | ||
+ | series: [{ | ||
+ | name: 'Test 1', | ||
+ | data: [ 43.644, 46.385, 32.454, 37.333, 19.084, 16.136, 4.824 ] | ||
+ | }, { | ||
+ | name: 'Test 2', | ||
+ | data: [ 390.057, 138.831, 89.078, 78.769, 39.94, 35.99, 9.545 ] | ||
+ | }, { | ||
+ | name: 'Test 3', | ||
+ | data: [ 82.997, 39.682, 33.176, 80.643, 48.766, 29.216, 13.972 ] | ||
+ | }, { | ||
+ | name: 'Test 4', | ||
+ | data: [ 144.859, 52.099, 35.469, 96.125, 55.373, 32.502, 14.864 ] | ||
+ | }, { | ||
+ | name: 'Test 5', | ||
+ | data: [ 30.174, 35.561, 39.051, 68.824, 39.012, 34.442, 10.475 ] | ||
+ | }, { | ||
+ | name: 'Test 6', | ||
+ | data: [ 18.282, 10.061, 15.62, 20.889, 17.528, 16.452, 7.534 ] | ||
+ | }, { | ||
+ | name: 'Test 7', | ||
+ | data: [ 26.499, 14.855, 22.433, 26.672, 21.686, 19.323, 7.879 ] | ||
+ | }], | ||
+ | chart: { | ||
+ | type: ' | ||
+ | height: 350, | ||
+ | stacked: true, | ||
+ | }, | ||
+ | plotOptions: | ||
+ | bar: { | ||
+ | horizontal: true, | ||
+ | }, | ||
+ | }, | ||
+ | title: { | ||
+ | text: ' | ||
+ | }, | ||
+ | xaxis: { | ||
+ | categories: [ | ||
+ | "1 Rank", | ||
+ | "2 Ranks", | ||
+ | "4 Ranks", | ||
+ | "8 Ranks", | ||
+ | "16 Ranks", | ||
+ | "28* Ranks", | ||
+ | "64 Ranks", | ||
+ | ], | ||
+ | title: { | ||
+ | text: "ns / day" | ||
+ | }, | ||
+ | }, | ||
+ | legend: { | ||
+ | position: ' | ||
+ | horizontalAlign: | ||
+ | title: { | ||
+ | text: "Test #" | ||
+ | }, | ||
+ | } | ||
+ | } | ||
+ | </ | ||
+ | |||
+ | ===== Links ===== | ||
+ | |||
+ | The benchmarks are based on three articles of NHR@FAU, featuring in | ||
+ | depth analysis on GROMACS Performance on various GPU systems, multi | ||
+ | GPU setups and comparison with CPU: | ||
+ | |||
+ | https:// | ||
+ | |||
+ | https:// | ||
+ | |||
+ | https:// | ||
+ | |||