More bang for your buck: Improved use of GPU nodes for GROMACS 2018

We identify hardware that is optimal to produce molecular dynamics (MD) trajectories on Linux compute clusters with the GROMACS 2018 simulation package. Therefore, we benchmark the GROMACS performance on a diverse set of compute nodes and relate it to the costs of the nodes, which may include their...

Full description

Saved in:
Bibliographic Details
Published inJournal of computational chemistry Vol. 40; no. 27; pp. 2418 - 2431
Main Authors Kutzner, Carsten, Páll, Szilárd, Fechner, Martin, Esztermann, Ansgar, Groot, Bert L., Grubmüller, Helmut
Format Journal Article
LanguageEnglish
Published Hoboken, USA John Wiley & Sons, Inc 15.10.2019
Wiley Subscription Services, Inc
Subjects
Online AccessGet full text

Cover

Loading…
Abstract We identify hardware that is optimal to produce molecular dynamics (MD) trajectories on Linux compute clusters with the GROMACS 2018 simulation package. Therefore, we benchmark the GROMACS performance on a diverse set of compute nodes and relate it to the costs of the nodes, which may include their lifetime costs for energy and cooling. In agreement with our earlier investigation using GROMACS 4.6 on hardware of 2014, the performance to price ratio of consumer GPU nodes is considerably higher than that of CPU nodes. However, with GROMACS 2018, the optimal CPU to GPU processing power balance has shifted even more toward the GPU. Hence, nodes optimized for GROMACS 2018 and later versions enable a significantly higher performance to price ratio than nodes optimized for older GROMACS versions. Moreover, the shift toward GPU processing allows to cheaply upgrade old nodes with recent GPUs, yielding essentially the same performance as comparable brand‐new hardware. © 2019 Wiley Periodicals, Inc. Hardware that is optimal to produce MD trajectories on Linux compute clusters with the GROMACS 2018 simulation package is identified. The trajectory output per invested Euro is extremely dependent on the type of acquired hardware. Nodes with inexpensive consumer GPUs produce up to six times as much trajectory per Euro as CPU nodes or nodes with Tesla GPUs. Upgrading old nodes with modern GPUs yields even higher performance to price ratios.
AbstractList We identify hardware that is optimal to produce molecular dynamics (MD) trajectories on Linux compute clusters with the GROMACS 2018 simulation package. Therefore, we benchmark the GROMACS performance on a diverse set of compute nodes and relate it to the costs of the nodes, which may include their lifetime costs for energy and cooling. In agreement with our earlier investigation using GROMACS 4.6 on hardware of 2014, the performance to price ratio of consumer GPU nodes is considerably higher than that of CPU nodes. However, with GROMACS 2018, the optimal CPU to GPU processing power balance has shifted even more toward the GPU. Hence, nodes optimized for GROMACS 2018 and later versions enable a significantly higher performance to price ratio than nodes optimized for older GROMACS versions. Moreover, the shift toward GPU processing allows to cheaply upgrade old nodes with recent GPUs, yielding essentially the same performance as comparable brand‐new hardware. © 2019 Wiley Periodicals, Inc. Hardware that is optimal to produce MD trajectories on Linux compute clusters with the GROMACS 2018 simulation package is identified. The trajectory output per invested Euro is extremely dependent on the type of acquired hardware. Nodes with inexpensive consumer GPUs produce up to six times as much trajectory per Euro as CPU nodes or nodes with Tesla GPUs. Upgrading old nodes with modern GPUs yields even higher performance to price ratios.
We identify hardware that is optimal to produce molecular dynamics (MD) trajectories on Linux compute clusters with the GROMACS 2018 simulation package. Therefore, we benchmark the GROMACS performance on a diverse set of compute nodes and relate it to the costs of the nodes, which may include their lifetime costs for energy and cooling. In agreement with our earlier investigation using GROMACS 4.6 on hardware of 2014, the performance to price ratio of consumer GPU nodes is considerably higher than that of CPU nodes. However, with GROMACS 2018, the optimal CPU to GPU processing power balance has shifted even more toward the GPU. Hence, nodes optimized for GROMACS 2018 and later versions enable a significantly higher performance to price ratio than nodes optimized for older GROMACS versions. Moreover, the shift toward GPU processing allows to cheaply upgrade old nodes with recent GPUs, yielding essentially the same performance as comparable brand-new hardware.
We identify hardware that is optimal to produce molecular dynamics (MD) trajectories on Linux compute clusters with the GROMACS 2018 simulation package. Therefore, we benchmark the GROMACS performance on a diverse set of compute nodes and relate it to the costs of the nodes, which may include their lifetime costs for energy and cooling. In agreement with our earlier investigation using GROMACS 4.6 on hardware of 2014, the performance to price ratio of consumer GPU nodes is considerably higher than that of CPU nodes. However, with GROMACS 2018, the optimal CPU to GPU processing power balance has shifted even more toward the GPU. Hence, nodes optimized for GROMACS 2018 and later versions enable a significantly higher performance to price ratio than nodes optimized for older GROMACS versions. Moreover, the shift toward GPU processing allows to cheaply upgrade old nodes with recent GPUs, yielding essentially the same performance as comparable brand-new hardware. © 2019 Wiley Periodicals, Inc.
Author Páll, Szilárd
Grubmüller, Helmut
Esztermann, Ansgar
Kutzner, Carsten
Fechner, Martin
Groot, Bert L.
Author_xml – sequence: 1
  givenname: Carsten
  orcidid: 0000-0002-8719-0307
  surname: Kutzner
  fullname: Kutzner, Carsten
  email: ckutzne@gwdg.de
  organization: Max Planck Institute for Biophysical Chemistry
– sequence: 2
  givenname: Szilárd
  surname: Páll
  fullname: Páll, Szilárd
  organization: KTH Royal Institute of Technology
– sequence: 3
  givenname: Martin
  surname: Fechner
  fullname: Fechner, Martin
  organization: Max Planck Institute for Biophysical Chemistry
– sequence: 4
  givenname: Ansgar
  surname: Esztermann
  fullname: Esztermann, Ansgar
  organization: Max Planck Institute for Biophysical Chemistry
– sequence: 5
  givenname: Bert L.
  surname: Groot
  fullname: Groot, Bert L.
  organization: Max Planck Institute for Biophysical Chemistry
– sequence: 6
  givenname: Helmut
  surname: Grubmüller
  fullname: Grubmüller, Helmut
  organization: Max Planck Institute for Biophysical Chemistry
BackLink https://www.ncbi.nlm.nih.gov/pubmed/31260119$$D View this record in MEDLINE/PubMed
https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-255767$$DView record from Swedish Publication Index
BookMark eNp10btOwzAUBmALgaAFBl4AWWKBIcWXOrbZqgAFVATiJjYrsR1IaeNiN6C-PaYpDEhMHvzp1zn_6YL12tUWgD2MehghcjzWukdShPEa6GAk00QK_rwOOghLkoiU4S3QDWGMEKIs7W-CLYqXXHZAdu28hUVev8DSebhwjYdFo99O4OV05t2HNbAJFroSDm8fYe2MDUs4vLu5HmT3kCAsdsBGmU-C3V292-Dx_Owhu0hGN8PLbDBKdJ9xnBSaYYI0MykX1DKac4K5ZgJrq0UujKDcGInK-NvnBSsKKlkckiLDCSuJodsgaXPDp501hZr5apr7hXJ5pU6rp4Fy_kW9zV8VYYynPPrD1sdF3hsb5mpaBW0nk7y2rgmKEIZSLCUnkR78oePYRB23iUpQLCki34FHrdLeheBt-TsCRur7EipeQi2rjXZ_ldgUU2t-5U_1ERy34LOa2MX_Seoqy9rIL72kjrg
CitedBy_id crossref_primary_10_1038_s41524_022_00946_w
crossref_primary_10_1155_2022_2102937
crossref_primary_10_1021_acs_jpcb_1c09159
crossref_primary_10_3389_fmolb_2021_676976
crossref_primary_10_3390_molecules27238417
crossref_primary_10_1021_acsomega_3c00364
crossref_primary_10_1016_j_jmgm_2023_108587
crossref_primary_10_1063_1674_0068_cjcp2111261
crossref_primary_10_1038_s41467_023_44208_9
crossref_primary_10_1021_acschemneuro_0c00758
crossref_primary_10_1063_5_0014282
crossref_primary_10_1021_acs_jcim_1c00879
crossref_primary_10_1016_j_intimp_2021_108424
crossref_primary_10_1007_s00894_024_05901_8
crossref_primary_10_1021_acs_jcim_2c00044
crossref_primary_10_3390_toxins15090542
crossref_primary_10_1039_D1MD00140J
crossref_primary_10_1016_j_jhazmat_2022_129517
crossref_primary_10_1016_j_ejmech_2020_112474
crossref_primary_10_1038_s41598_022_26149_3
crossref_primary_10_3390_ph13090253
crossref_primary_10_1039_D3CP02989A
crossref_primary_10_3390_polym15143133
crossref_primary_10_1021_acs_cgd_4c00507
crossref_primary_10_1016_j_fbio_2022_102090
crossref_primary_10_1021_acs_jcim_0c01179
crossref_primary_10_1177_10943420231213013
crossref_primary_10_3390_antiox13030261
crossref_primary_10_7554_eLife_77032
crossref_primary_10_1039_D0NJ02840A
crossref_primary_10_1002_wcms_1534
crossref_primary_10_1016_j_ymeth_2021_03_005
crossref_primary_10_1021_acsomega_1c03563
crossref_primary_10_1021_acsomega_2c00959
crossref_primary_10_1063_5_0018516
crossref_primary_10_1007_s00894_022_05369_4
crossref_primary_10_1063_5_0019045
crossref_primary_10_1038_s41467_022_28685_y
crossref_primary_10_1080_07391102_2020_1796811
crossref_primary_10_1080_19420862_2020_1725365
crossref_primary_10_1016_j_bbadis_2022_166384
crossref_primary_10_1016_j_chemosphere_2023_138393
crossref_primary_10_1016_j_molliq_2023_123414
crossref_primary_10_1038_s41598_021_84488_z
crossref_primary_10_1002_slct_202401597
crossref_primary_10_1007_s00210_024_03138_z
crossref_primary_10_1016_j_jmgm_2022_108282
crossref_primary_10_1021_acs_jchemed_1c00022
crossref_primary_10_1063_5_0047663
crossref_primary_10_3390_ijms24043580
crossref_primary_10_5808_gi_21040
crossref_primary_10_1021_acs_jctc_3c00372
crossref_primary_10_1038_s41477_022_01145_7
crossref_primary_10_1038_s41467_021_26222_x
crossref_primary_10_3389_fnbot_2020_617327
crossref_primary_10_7124_FEEO_v28_1389
crossref_primary_10_1039_D4MA00246F
crossref_primary_10_1080_07391102_2023_2300134
crossref_primary_10_1126_sciadv_abj5836
crossref_primary_10_1021_acs_jcim_9b00883
crossref_primary_10_1007_s12010_023_04451_8
crossref_primary_10_1039_D4MH00066H
crossref_primary_10_1021_acs_langmuir_3c01318
crossref_primary_10_3390_polym14204409
crossref_primary_10_1073_pnas_2025121118
crossref_primary_10_1039_D3CP01039B
crossref_primary_10_1080_23746149_2021_1932589
crossref_primary_10_1021_acs_langmuir_3c03862
crossref_primary_10_1080_00319104_2023_2263897
crossref_primary_10_1039_D0RA08123J
crossref_primary_10_1126_science_abe0763
crossref_primary_10_1038_s41598_024_61991_7
crossref_primary_10_3390_gels7030087
crossref_primary_10_1016_j_molstruc_2024_137509
crossref_primary_10_1038_s41598_022_17920_7
crossref_primary_10_1016_j_compositesb_2022_109712
crossref_primary_10_1016_j_polymer_2020_122881
crossref_primary_10_1016_j_compbiomed_2021_104818
crossref_primary_10_1177_1094342020964857
crossref_primary_10_3389_fphys_2021_798102
crossref_primary_10_1016_j_virol_2023_109901
crossref_primary_10_3390_membranes12060619
crossref_primary_10_1016_j_foodchem_2022_132686
crossref_primary_10_1080_07391102_2023_2272750
crossref_primary_10_3390_ijms23116330
crossref_primary_10_3390_jfb14040219
crossref_primary_10_1007_s11030_022_10437_1
crossref_primary_10_1007_s42250_022_00411_7
crossref_primary_10_1002_jcc_26979
crossref_primary_10_1002_slct_202203773
crossref_primary_10_1016_j_fluid_2024_114080
crossref_primary_10_1021_acs_jctc_0c00744
crossref_primary_10_3389_fmicb_2020_01393
crossref_primary_10_3390_cryst11060588
crossref_primary_10_1021_acs_jctc_1c00176
crossref_primary_10_1002_mgg3_1344
crossref_primary_10_1039_D2NR00882C
crossref_primary_10_1016_j_matdes_2021_109902
crossref_primary_10_1080_07391102_2023_2279279
crossref_primary_10_3390_e22010013
crossref_primary_10_1038_s41467_021_22590_6
crossref_primary_10_3389_fbioe_2023_1249196
crossref_primary_10_1021_acs_jcim_2c00414
crossref_primary_10_3390_membranes13110851
crossref_primary_10_1021_acs_jpcc_2c06093
crossref_primary_10_1002_cbic_202300373
crossref_primary_10_1021_acs_jafc_1c05256
crossref_primary_10_1038_s41598_022_13738_5
crossref_primary_10_1039_D1CP01825F
crossref_primary_10_3390_biom12040499
crossref_primary_10_1155_2021_6623912
crossref_primary_10_31857_S2308114723700231
crossref_primary_10_1177_10943420231166608
crossref_primary_10_3390_nano12142398
crossref_primary_10_1063_5_0095581
crossref_primary_10_1016_j_bpc_2022_106765
crossref_primary_10_1074_jbc_RA120_013073
crossref_primary_10_1134_S1811238223700285
crossref_primary_10_1093_bib_bbaa378
crossref_primary_10_1063_5_0014500
crossref_primary_10_3390_biomedicines11051357
crossref_primary_10_1080_07391102_2020_1821781
crossref_primary_10_1021_acs_jpcb_3c06136
crossref_primary_10_1080_07391102_2023_2222191
crossref_primary_10_1039_D2SC02342C
crossref_primary_10_1021_acschemneuro_0c00692
crossref_primary_10_1080_07391102_2021_1930160
crossref_primary_10_1021_acs_jcim_0c00900
crossref_primary_10_1080_07391102_2023_2291165
crossref_primary_10_3390_ijms241310610
crossref_primary_10_1016_j_bbadis_2022_166514
crossref_primary_10_1111_nyas_14891
crossref_primary_10_1080_07391102_2023_2291168
crossref_primary_10_1016_j_cjche_2023_08_007
crossref_primary_10_1039_D0CP06588A
crossref_primary_10_3389_fmolb_2022_898874
crossref_primary_10_1021_acsomega_3c06826
crossref_primary_10_1021_acs_jctc_1c01214
crossref_primary_10_3390_life12010054
crossref_primary_10_1016_j_colsurfb_2022_112836
crossref_primary_10_1016_j_phymed_2023_154843
crossref_primary_10_3390_pharmaceutics15031018
crossref_primary_10_1039_D1NJ02147H
crossref_primary_10_1016_j_jmgm_2021_108069
crossref_primary_10_1039_D2TA03164G
crossref_primary_10_1016_j_molliq_2022_119805
crossref_primary_10_1007_s00018_023_04839_z
crossref_primary_10_3390_ijms242015194
crossref_primary_10_1021_acs_jcim_1c00598
crossref_primary_10_1039_D2RA01963A
crossref_primary_10_2139_ssrn_4104072
crossref_primary_10_1016_j_ijbiomac_2021_03_032
crossref_primary_10_1021_acs_analchem_1c04760
crossref_primary_10_1016_j_jphotochem_2024_115464
crossref_primary_10_1177_10943420211008288
crossref_primary_10_3390_ijms23031514
crossref_primary_10_3390_molecules29061338
crossref_primary_10_1080_07391102_2023_2183032
crossref_primary_10_1088_1742_6596_2243_1_012078
crossref_primary_10_1016_j_jcis_2022_10_118
crossref_primary_10_3390_biom13020248
crossref_primary_10_1016_j_bpj_2023_01_025
crossref_primary_10_3390_biom13071043
crossref_primary_10_1111_cbdd_13841
crossref_primary_10_58430_jib_v129i2_20
crossref_primary_10_3390_polym14132577
crossref_primary_10_1038_s42003_023_04720_6
crossref_primary_10_1002_prot_26076
crossref_primary_10_1039_D2CP05611A
crossref_primary_10_1002_psc_3594
crossref_primary_10_1016_j_bbalip_2023_159430
crossref_primary_10_1016_j_eng_2022_03_020
crossref_primary_10_1021_acs_jpcb_0c08030
crossref_primary_10_1002_ijch_202000022
crossref_primary_10_1016_j_cjche_2020_11_036
crossref_primary_10_1016_j_polymer_2022_125253
crossref_primary_10_1073_pnas_1921467117
crossref_primary_10_3390_math9111245
crossref_primary_10_1016_j_jtice_2021_05_048
crossref_primary_10_1126_sciadv_abd3718
crossref_primary_10_1016_j_jmgm_2021_108041
crossref_primary_10_1016_j_sbi_2024_102825
crossref_primary_10_3390_ijms232314788
crossref_primary_10_1016_j_enzmictec_2022_110123
crossref_primary_10_1016_j_commatsci_2019_109359
crossref_primary_10_1016_j_jcou_2023_102649
crossref_primary_10_1002_prot_26405
crossref_primary_10_1021_acscatal_1c01062
crossref_primary_10_1007_s00894_021_04957_0
crossref_primary_10_3389_fchem_2021_744376
crossref_primary_10_1002_slct_202201983
crossref_primary_10_1038_s41545_022_00210_0
crossref_primary_10_1016_j_jmgm_2021_108030
crossref_primary_10_1016_j_compbiomed_2022_105552
crossref_primary_10_1016_j_jmgm_2024_108809
crossref_primary_10_1021_acs_jctc_1c00145
crossref_primary_10_1016_j_bpc_2024_107230
crossref_primary_10_1007_s00894_023_05558_9
crossref_primary_10_1016_j_chemgeo_2022_121101
crossref_primary_10_1016_j_heliyon_2023_e19641
crossref_primary_10_1038_s41545_022_00173_2
crossref_primary_10_1016_j_trac_2023_117051
crossref_primary_10_1016_j_biopha_2024_116484
crossref_primary_10_1016_j_materresbull_2022_111927
crossref_primary_10_1016_j_apsusc_2024_159582
crossref_primary_10_1021_acschemneuro_2c00224
crossref_primary_10_3390_molecules27196574
crossref_primary_10_1016_j_bpc_2020_106537
crossref_primary_10_1039_D0SC00129E
crossref_primary_10_3390_pr12061047
crossref_primary_10_1002_wcms_1622
crossref_primary_10_1016_j_mcp_2022_101847
crossref_primary_10_1039_D2GC01808J
crossref_primary_10_3389_fnmol_2023_1104585
crossref_primary_10_1021_acs_jcim_1c00712
crossref_primary_10_1016_j_chphi_2022_100087
crossref_primary_10_1080_07391102_2023_2281635
crossref_primary_10_1093_glycob_cwab049
crossref_primary_10_1146_annurev_biophys_111622_091147
crossref_primary_10_1021_acs_jpcb_4c01637
crossref_primary_10_1016_j_molstruc_2022_133676
crossref_primary_10_1016_j_molliq_2024_124255
crossref_primary_10_1080_07391102_2023_2291544
crossref_primary_10_2142_biophysico_bppb_v20_0047
crossref_primary_10_1039_D1CE01415C
crossref_primary_10_1016_j_jmgm_2022_108279
crossref_primary_10_1021_acs_analchem_3c01355
crossref_primary_10_1080_07391102_2021_1872418
crossref_primary_10_1002_jcc_26203
crossref_primary_10_1007_s12275_022_2279_5
crossref_primary_10_1016_j_compbiomed_2022_105336
crossref_primary_10_1021_acs_jctc_3c00460
crossref_primary_10_3390_molecules28196795
crossref_primary_10_1021_acs_jafc_3c00260
crossref_primary_10_1021_acsami_2c11500
crossref_primary_10_1016_j_bpj_2023_02_017
crossref_primary_10_1016_j_seppur_2021_120440
crossref_primary_10_2139_ssrn_3994655
crossref_primary_10_1016_j_ijbiomac_2024_133233
crossref_primary_10_1021_acs_jpcb_2c05311
crossref_primary_10_1021_acs_jcim_2c01596
crossref_primary_10_1371_journal_pone_0240653
crossref_primary_10_1016_j_chemosphere_2020_127007
crossref_primary_10_1021_acs_langmuir_0c02777
crossref_primary_10_4236_ajmb_2021_111001
crossref_primary_10_1021_acs_jpcb_1c07884
crossref_primary_10_3390_membranes13020148
crossref_primary_10_1016_j_bbagen_2022_130200
crossref_primary_10_1093_bulcsj_uoad008
crossref_primary_10_1039_D3NR03308B
crossref_primary_10_1007_s00894_024_05858_8
crossref_primary_10_1016_j_bpc_2022_106908
crossref_primary_10_1021_acsomega_9b03908
crossref_primary_10_1080_07391102_2023_2247084
crossref_primary_10_1002_minf_202300055
crossref_primary_10_1080_07391102_2023_2280674
crossref_primary_10_3390_biom11111688
Cites_doi 10.1021/ct9000685
10.1073/pnas.1103547108
10.1016/j.cpc.2018.02.003
10.1016/j.cpc.2011.10.012
10.1002/jcc.21287
10.1016/j.sbi.2014.04.002
10.1021/ct700301q
10.1016/j.bpj.2008.11.028
10.1016/j.jconrel.2018.05.026
10.1109/PDP.2010.67
10.1038/nsmb.2690
10.1063/1.470117
10.1126/science.1066115
10.1021/jp071097f
10.1016/j.cpc.2015.09.014
10.1038/nchem.2785
10.1145/1188455.1188544
10.1016/j.softx.2015.06.001
10.1016/j.cpc.2013.06.003
10.1002/jcc.24030
10.1002/jcc.20289
10.1109/JPROC.2004.840301
10.1021/jp036508g
10.1021/ct300857j
10.1021/ct400314y
ContentType Journal Article
Copyright 2019 Wiley Periodicals, Inc.
Copyright_xml – notice: 2019 Wiley Periodicals, Inc.
DBID NPM
AAYXX
CITATION
JQ2
7X8
ADTPV
AOWAS
D8V
DOI 10.1002/jcc.26011
DatabaseName PubMed
CrossRef
ProQuest Computer Science Collection
MEDLINE - Academic
SwePub
SwePub Articles
SWEPUB Kungliga Tekniska Högskolan
DatabaseTitle PubMed
CrossRef
ProQuest Computer Science Collection
MEDLINE - Academic
DatabaseTitleList

PubMed
CrossRef
ProQuest Computer Science Collection
Database_xml – sequence: 1
  dbid: NPM
  name: PubMed
  url: https://proxy.k.utb.cz/login?url=http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed
  sourceTypes: Index Database
DeliveryMethod fulltext_linktorsrc
Discipline Chemistry
EISSN 1096-987X
EndPage 2431
ExternalDocumentID oai_DiVA_org_kth_255767
10_1002_jcc_26011
31260119
JCC26011
Genre article
Research Support, Non-U.S. Gov't
Journal Article
GrantInformation_xml – fundername: European Union
  funderid: H2020‐EINFRA‐2015‐1‐675728
– fundername: Deutsche Forschungsgemeinschaft
  funderid: SPP 1648
GroupedDBID ---
-~X
.3N
.GA
05W
0R~
10A
1L6
1OB
1OC
1ZS
33P
36B
3SF
3WU
4.4
4ZD
50Y
50Z
51W
51X
52M
52N
52O
52P
52S
52T
52U
52W
52X
53G
5GY
5VS
66C
6P2
702
7PT
8-0
8-1
8-3
8-4
8-5
8UM
930
A03
AAESR
AAEVG
AAHHS
AANLZ
AAONW
AASGY
AAXRX
AAZKR
ABCQN
ABCUV
ABIJN
ABJNI
ABLJU
ABPVW
ACAHQ
ACCFJ
ACCZN
ACFBH
ACGFO
ACGFS
ACIWK
ACNCT
ACPOU
ACXBN
ACXQS
ADBBV
ADEOM
ADIZJ
ADKYN
ADMGS
ADOZA
ADXAS
ADZMN
AEEZP
AEGXH
AEIGN
AEIMD
AENEX
AEQDE
AEUQT
AEUYR
AFBPY
AFFPM
AFGKR
AFPWT
AFZJQ
AHBTC
AIAGR
AITYG
AIURR
AIWBW
AJBDE
AJXKR
ALAGY
ALMA_UNASSIGNED_HOLDINGS
ALUQN
AMBMR
AMYDB
ATUGU
AUFTA
AZBYB
AZVAB
BAFTC
BFHJK
BHBCM
BMNLL
BMXJE
BNHUX
BROTX
BRXPI
BY8
CS3
D-E
D-F
DCZOG
DPXWK
DR1
DR2
DRFUL
DRSTM
DU5
EBS
EJD
ESX
F00
F01
F04
F5P
G-S
G.N
GNP
GODZA
H.T
H.X
HBH
HGLYW
HHY
HHZ
HZ~
IX1
J0M
JPC
KQQ
LATKE
LAW
LC2
LC3
LEEKS
LH4
LITHE
LOXES
LP6
LP7
LUTES
LYRES
MEWTI
MK4
MRFUL
MRSTM
MSFUL
MSSTM
MXFUL
MXSTM
N04
N05
N9A
NF~
NNB
O66
O9-
OIG
P2P
P2W
P2X
P4D
PQQKQ
Q.N
Q11
QB0
QRW
R.K
RNS
ROL
RWI
RWK
RX1
RYL
SUPJJ
TN5
UB1
UPT
V2E
V8K
W8V
W99
WBFHL
WBKPD
WH7
WIB
WIH
WIK
WJL
WOHZO
WQJ
WRC
WXSBR
WYISQ
XG1
XPP
XV2
YQT
ZZTAW
~IA
~KM
~WT
NPM
AAYXX
CITATION
JQ2
7X8
.Y3
186
31~
6TJ
ABEFU
ABEML
ABTAH
ACBWZ
ACSCC
ADTPV
AFFNX
AI.
AOWAS
ASPBG
AVWKF
AZFZN
BDRZF
BTSUX
D8V
FEDTE
G8K
HF~
HVGLF
LW6
M21
PALCI
RIWAO
RJQFR
SAMSI
VH1
YCJ
ZCG
ZY4
ID FETCH-LOGICAL-c4571-bc5120c5d6783e53a7217c581cec8a8d837dd90f78347b5bb39501130d725f2d3
IEDL.DBID DR2
ISSN 0192-8651
1096-987X
IngestDate Sat Aug 24 00:13:22 EDT 2024
Fri Aug 16 02:05:25 EDT 2024
Thu Oct 10 18:27:46 EDT 2024
Fri Aug 23 01:46:47 EDT 2024
Sat Sep 28 08:27:04 EDT 2024
Sat Aug 24 01:11:10 EDT 2024
IsDoiOpenAccess false
IsOpenAccess true
IsPeerReviewed true
IsScholarly true
Issue 27
Keywords CUDA
energy efficiency
computer simulations
molecular dynamics
performance to price
GPU
GROMACS
high throughput MD
parallel computing
benchmark
Language English
License 2019 Wiley Periodicals, Inc.
LinkModel DirectLink
MergedId FETCHMERGED-LOGICAL-c4571-bc5120c5d6783e53a7217c581cec8a8d837dd90f78347b5bb39501130d725f2d3
Notes ObjectType-Article-1
SourceType-Scholarly Journals-1
ObjectType-Feature-2
content type line 23
ORCID 0000-0002-8719-0307
PMID 31260119
PQID 2283193027
PQPubID 48816
PageCount 14
ParticipantIDs swepub_primary_oai_DiVA_org_kth_255767
proquest_miscellaneous_2250619972
proquest_journals_2283193027
crossref_primary_10_1002_jcc_26011
pubmed_primary_31260119
wiley_primary_10_1002_jcc_26011_JCC26011
PublicationCentury 2000
PublicationDate October 15, 2019
PublicationDateYYYYMMDD 2019-10-15
PublicationDate_xml – month: 10
  year: 2019
  text: October 15, 2019
  day: 15
PublicationDecade 2010
PublicationPlace Hoboken, USA
PublicationPlace_xml – name: Hoboken, USA
– name: United States
– name: New York
PublicationTitle Journal of computational chemistry
PublicationTitleAlternate J Comput Chem
PublicationYear 2019
Publisher John Wiley & Sons, Inc
Wiley Subscription Services, Inc
Publisher_xml – name: John Wiley & Sons, Inc
– name: Wiley Subscription Services, Inc
References 2012; 183
2015; 36
2018; 283
2018; 228
2013; 20
2014; 25
2006
2013; 184
2008; 4
2005; 26
2004; 108
2017; 9
2013; 9
2018; 8
2009; 96
2009; 30
2001; 294
2011; 108
2015; 1‐2
2007; 111
2016; 199
2015
1995; 103
2009; 5
2005; 93
e_1_2_8_28_1
e_1_2_8_29_1
e_1_2_8_24_1
e_1_2_8_25_1
e_1_2_8_26_1
e_1_2_8_27_1
Chen S. (e_1_2_8_15_1) 2018; 8
e_1_2_8_3_1
e_1_2_8_2_1
e_1_2_8_5_1
e_1_2_8_4_1
e_1_2_8_7_1
e_1_2_8_6_1
e_1_2_8_9_1
e_1_2_8_8_1
e_1_2_8_20_1
e_1_2_8_21_1
e_1_2_8_22_1
e_1_2_8_23_1
e_1_2_8_1_1
e_1_2_8_18_1
e_1_2_8_19_1
e_1_2_8_13_1
e_1_2_8_14_1
e_1_2_8_16_1
Páll S. (e_1_2_8_17_1) 2015
e_1_2_8_10_1
e_1_2_8_11_1
e_1_2_8_12_1
References_xml – volume: 8
  year: 2018
  publication-title: bioRxiv
– volume: 96
  start-page: 1350
  year: 2009
  publication-title: Biophys. J.
– volume: 20
  start-page: 1390
  year: 2013
  publication-title: Nat. Struct. Mol. Biol.
– volume: 4
  start-page: 435
  year: 2008
  publication-title: J. Chem. Theory Comput.
– volume: 26
  start-page: 1781
  year: 2005
  publication-title: J. Comput. Chem.
– volume: 199
  start-page: 1
  year: 2016
  publication-title: Comput. Phys. Commun.
– volume: 9
  start-page: 1005
  year: 2017
  publication-title: Nat. Chem.
– start-page: 1
  year: 2015
– volume: 103
  start-page: 8577
  year: 1995
  publication-title: J. Chem. Phys.
– volume: 93
  start-page: 216
  year: 2005
  publication-title: Proc. IEEE
– volume: 9
  start-page: 461
  year: 2013
  publication-title: J. Chem. Theory Comput.
– volume: 25
  start-page: 135
  year: 2014
  publication-title: Curr. Opin. Struct. Biol.
– volume: 184
  start-page: 2641
  year: 2013
  publication-title: Comput. Phys. Commun.
– volume: 294
  start-page: 2353
  year: 2001
  publication-title: Science
– volume: 9
  start-page: 3878
  year: 2013
  publication-title: J. Chem. Theory Comput.
– volume: 283
  start-page: 269
  year: 2018
  publication-title: J. Control. Release
– year: 2006
– volume: 36
  start-page: 1990
  year: 2015
  publication-title: J. Comput. Chem.
– volume: 183
  start-page: 449
  year: 2012
  publication-title: Comput. Phys. Commun.
– volume: 108
  start-page: 750
  year: 2004
  publication-title: J. Phys. Chem. B
– volume: 30
  start-page: 1545
  year: 2009
  publication-title: J. Comput. Chem.
– volume: 1‐2
  start-page: 19
  year: 2015
  publication-title: SoftwareX
– volume: 111
  start-page: 7812
  year: 2007
  publication-title: J. Phys. Chem. B
– volume: 108
  start-page: 10184
  year: 2011
  publication-title: PNAS
– volume: 5
  start-page: 1632
  year: 2009
  publication-title: J. Chem. Theory Comput.
– volume: 228
  start-page: 146
  year: 2018
  publication-title: Comput. Phys. Commun.
– ident: e_1_2_8_3_1
  doi: 10.1021/ct9000685
– ident: e_1_2_8_13_1
  doi: 10.1073/pnas.1103547108
– start-page: 1
  volume-title: Lect. Notes Comput. Sci. 8759, EASC 2014
  year: 2015
  ident: e_1_2_8_17_1
  contributor:
    fullname: Páll S.
– ident: e_1_2_8_29_1
  doi: 10.1016/j.cpc.2018.02.003
– ident: e_1_2_8_7_1
  doi: 10.1016/j.cpc.2011.10.012
– ident: e_1_2_8_5_1
  doi: 10.1002/jcc.21287
– ident: e_1_2_8_12_1
  doi: 10.1016/j.sbi.2014.04.002
– ident: e_1_2_8_18_1
  doi: 10.1021/ct700301q
– ident: e_1_2_8_2_1
  doi: 10.1016/j.bpj.2008.11.028
– volume: 8
  year: 2018
  ident: e_1_2_8_15_1
  publication-title: bioRxiv
  contributor:
    fullname: Chen S.
– ident: e_1_2_8_11_1
  doi: 10.1016/j.jconrel.2018.05.026
– ident: e_1_2_8_23_1
  doi: 10.1109/PDP.2010.67
– ident: e_1_2_8_1_1
  doi: 10.1038/nsmb.2690
– ident: e_1_2_8_19_1
  doi: 10.1063/1.470117
– ident: e_1_2_8_21_1
– ident: e_1_2_8_20_1
  doi: 10.1126/science.1066115
– ident: e_1_2_8_26_1
  doi: 10.1021/jp071097f
– ident: e_1_2_8_28_1
  doi: 10.1016/j.cpc.2015.09.014
– ident: e_1_2_8_27_1
– ident: e_1_2_8_14_1
  doi: 10.1038/nchem.2785
– ident: e_1_2_8_6_1
  doi: 10.1145/1188455.1188544
– ident: e_1_2_8_10_1
  doi: 10.1016/j.softx.2015.06.001
– ident: e_1_2_8_22_1
  doi: 10.1016/j.cpc.2013.06.003
– ident: e_1_2_8_16_1
  doi: 10.1002/jcc.24030
– ident: e_1_2_8_8_1
  doi: 10.1002/jcc.20289
– ident: e_1_2_8_24_1
  doi: 10.1109/JPROC.2004.840301
– ident: e_1_2_8_25_1
  doi: 10.1021/jp036508g
– ident: e_1_2_8_9_1
  doi: 10.1021/ct300857j
– ident: e_1_2_8_4_1
  doi: 10.1021/ct400314y
SSID ssj0003564
Score 2.7036119
Snippet We identify hardware that is optimal to produce molecular dynamics (MD) trajectories on Linux compute clusters with the GROMACS 2018 simulation package....
SourceID swepub
proquest
crossref
pubmed
wiley
SourceType Open Access Repository
Aggregation Database
Index Database
Publisher
StartPage 2418
SubjectTerms benchmark
Central processing units
Computer simulation
computer simulations
CPUs
CUDA
Energy costs
energy efficiency
GPU
Graphics processing units
GROMACS
Hardware
high throughput MD
Molecular dynamics
Nodes
parallel computing
performance to price
Title More bang for your buck: Improved use of GPU nodes for GROMACS 2018
URI https://onlinelibrary.wiley.com/doi/abs/10.1002%2Fjcc.26011
https://www.ncbi.nlm.nih.gov/pubmed/31260119
https://www.proquest.com/docview/2283193027
https://search.proquest.com/docview/2250619972
https://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-255767
Volume 40
hasFullText 1
inHoldings 1
isFullTextHit
isPrint
link http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1LT9wwEB4hLnBpKfQRCsigquolyzqO84DTKryERFvRbsWhkhU_QtuVkmp3c4Bfz9jeBFGEVHGLZDu2Z-zM9zn2Z4AP3J6eMpqGSGhNGDOVhpJVPJRJVGWaSsWM2yD7OTkbx-dX_GoJDruzMF4fol9wszPDfa_tBC_lbP9eNPSPUgOrh2WpjxXSs4Do8l46inEvHYUIJswSTjtVoWG035d8GIseAcxePfQhcHWR5-Ql_Oza7DecTAbtXA7U7T9yjs_s1Bq8WCBSMvJD6BUsmXodVoruIrgNKC6aqSGyrK8JIlxygwWIbNXkgPgVCaNJOzOkqcjp1zGpG21mLuPp5ZeLUfGNYPDPXsP45Ph7cRYu7l4IVcxTGkqFSGCouMZgxgxnJTLFVPGMKqOyMtPIa7XOh5W9pyOVXEqWc2w2G-o04lWk2RtYrpvavAOSIyehSZRnWsZIx7ISEU3OFVeljI2JaQB7nRfEXy-xIbyYciTQHsLZI4Ctzj9iMctmwkr3IABFZh3Abp-M1rE_PcraNK3NwxGy2OPBAbz1fu1rYdS9Ow_go3d0n2JFt49-_xiJZnotJvNfAplXmmA1n5z3nm6nOC8K97D5_1nfwyp6IrcRkfItWJ5PW7ONUGcud9yYvgNXkPOA
link.rule.ids 230,315,786,790,891,1382,27946,27947,46318,46742
linkProvider Wiley-Blackwell
linkToHtml http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Jb9QwFH6q2kO5sC8pBQxCiEum4zjOgriMUtqhdAoqHdQLsuIlLYyUVDOTQ_vrebYnqQpCQtwi2Y6XZ-d9n2N_D-A1t7enjKYhEloTxkyloWQVD2USVZmmUjHjDsgeJeNpfHDKT9fgfXcXxutD9BtudmW477Vd4HZDeudaNfSnUgMriIXcZ8PGzLDxC3aPr8WjGPfiUYhhwizhtNMVGkY7fdGb3ugPiNnrh96Ers737N2B712r_ZGT2aBdyoG6-k3Q8X-7dRdur0ApGflZdA_WTH0fNosuFtwDKCbN3BBZ1mcEQS65xAJEtmr2jvhNCaNJuzCkqcj-lympG20WLuP-8efJqPhK0P9nD2G69-GkGIer8AuhinlKQ6kQDAwV1-jPmOGsRLKYKp5RZVRWZhqprdb5sLKhOlLJpWQ5x2azoU4jXkWaPYL1uqnNEyA50hKaRHmmZYyMLCsR1ORccVXK2JiYBvCqM4O48CobwuspRwLHQ7jxCGC7M5BYLbSFsOo9iEGRXAfwsk_G0bH_PcraNK3NwxG12BvCATz2hu1rYdS9Ow_gjbd0n2J1t3d_fBuJZn4mZstzgeQrTbCat858f2-nOCgK97D171lfwOb4ZHIoDj8efXoKt9AquXWQlG_D-nLemmeIfJbyuZvgvwCtJPeg
linkToPdf http://utb.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwnV1Lb9QwEB5VRSpcgJZXoA-DEOKS7TqO84DTKu22FFqqwqIekKz4kQIrJdXu5gC_nrG9SVUQEuotku3YnrEz3-fYnwFecnt6ymgaIqE1YcxUGkpW8VAmUZVpKhUzboPsSXI4iY_O-fkKvO3Ownh9iH7Bzc4M9722E_xSV7tXoqE_lBpYPSykPrfiBIemRURnV9pRjHvtKIQwYZZw2skKDaPdvuj1YPQXwuzlQ68jVxd6xvfga9dov-NkOmgXcqB-_aHneMNe3Ye7S0hKRn4MrcOKqTfgdtHdBPcAiuNmZogs6wuCEJf8xAJEtmr6hvglCaNJOzekqcjB6YTUjTZzl_Hg7OPxqPhEMPpnD2Ey3v9cHIbLyxdCFfOUhlIhFBgqrjGaMcNZiVQxVTyjyqiszDQSW63zYWUv6kgll5LlHJvNhjqNeBVp9ghW66Y2T4DkSEpoEuWZljHysaxESJNzxVUpY2NiGsCLzgvi0mtsCK-mHAm0h3D2CGCz849YTrO5sNo9iECRWgfwvE9G69i_HmVtmtbm4YhZ7PngAB57v_a1MOrenQfwyju6T7Gq23vfv4xEM7sQ08U3gdQrTbCa1857_26nOCoK9_D0_7PuwNrp3lh8eHfy_hncQafkNjpSvgmri1lrthD2LOS2G96_AcL29k8
openUrl ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=More+bang+for+your+buck%3A+Improved+use+of+GPU+nodes+for+GROMACS+2018&rft.jtitle=Journal+of+computational+chemistry&rft.au=Kutzner%2C+Carsten&rft.au=P%C3%A1ll%2C+Szil%C3%A1rd&rft.au=Fechner%2C+Martin&rft.au=Esztermann%2C+Ansgar&rft.date=2019-10-15&rft.eissn=1096-987X&rft.volume=40&rft.issue=27&rft.spage=2418&rft.epage=2431&rft_id=info:doi/10.1002%2Fjcc.26011&rft.externalDBID=NO_FULL_TEXT
thumbnail_l http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=0192-8651&client=summon
thumbnail_m http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=0192-8651&client=summon
thumbnail_s http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=0192-8651&client=summon