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vtk m a toolkit of scientific visualization algorithms for emerging processor architectures vtk m welcome resources building vtk m contributing doxygen documentation mailing list software dependencies tutorial user guide vtk m assets publications contact download a toolkit of scientific visualization algorithms for emerging processor architectures image credit matthew larsen llnl this image is of an idealized inertial confinement fusion icf simulation of a rayleigh taylor instability with two fluids mixing in a spherical geometry getting started download the latest release and then follow the instructions if you re not already a vtk user and want to learn more about the original toolkit check it out download what is vtk m one of the biggest recent changes in high performance computing is the increasing use of accelerators accelerators contain processing cores that independently are inferior to a core in a typical cpu but these cores are replicated and grouped such that their aggregate execution provides a very high computation rate at a much lower power current and future cpu processors also require much more explicit parallelism each successive version of the hardware packs more cores into each processor and technologies like hyperthreading and vector operations require even more parallel processing to leverage each core s full potential vtk m is a toolkit of scientific visualization algorithms for emerging processor architectures vtk m supports the fine grained concurrency for data analysis and visualization algorithms required to drive extreme scale computing by providing abstract models for data and execution that can be applied to a variety of algorithms across many different processor architectures vtk m resources are you funded by the ecp vtk m project see ecp vtk m project management building vtk m software dependencies contributing mailing list user guide doxygen documentation tutorial vtk m assets vtk m publications please use the first paper when referencing vtk m in scientific publications bib file moreland k sewell c usher w lo l t meredith j pugmire d kress j schroots h ma k l childs h larsen m chen c m maynard r geveci b 2016 vtk m accelerating the visualization toolkit for massively threaded architectures ieee computer graphics and applications 36 3 48 58 https doi org 10 1109 mcg 2016 48 moreland k 2024 the vtk m user s guide techreport ornl tm 2024 3443 number ornl tm 2024 3443 oak ridge national laboratory https gitlab kitware com vtk vtk m user guide wikis home moreland k athawale t m bolea v bolstad m brugger e childs h huebl a lo l t geveci b marsaglia n philip s pugmire d rizzi s wang z yenpure a 2024 visualization at exascale making it all work with vtk m the international journal of high performance computing applications 38 5 508 526 https doi org 10 1177 10943420241270969 bolstad m moreland k pugmire d rogers d lo l t geveci b childs h rizzi s 2023 august vtk m visualization for the exascale era and beyond acm siggraph 2023 talks https doi org 10 1145 3587421 3595466 philip s moreland k maynard r 2023 vtk m accelerated filters in vtk and paraview kitware source https www kitware com vtk m accelerated filters in vtk and paraview wang z athawale t m moreland k chen j johnson c r pugmire d 2023 may funmc2 a filter for uncertainty visualization of marching cubes on multi core devices eurographics symposium on parallel graphics and visualization egpgv https doi org 10 2312 pgv 20231081 carr h a rübel o weber g h ahrens j p 2021 optimization and augmentation for data parallel contour trees ieee transactions on visualization and computer graphics 28 10 3471 3485 https doi org 10 1109 tvcg 2021 3064385 farber r 2021 ecp brings much needed visualization software to exascale and gpu accelerated systems ecp technical highlights https www exascaleproject org highlight ecp brings much needed visualization software to exascale and gpu accelerated systems moreland k maynard r pugmire d yenpure a vacanti a larsen m childs h 2021 minimizing development costs for efficient many core visualization using mcd 3 parallel computing 108 102834 https doi org 10 1016 j parco 2021 102834 pugmire d ross c thompson n kress j atkins c klasky s geveci b 2021 fides a general purpose data model library for streaming data isc high performance 495 507 https doi org https doi org 10 1007 978 3 030 90539 2_34 sane s johnson c r childs h 2021 investigating in situ reduction via lagrangian representations for cosmology and seismology applications computational science iccs 2021 436 450 https doi org 10 1007 978 3 030 77961 0_36 winner best paper sane s yenpure a bujack r larsen m moreland k garth c johnson c r childs h 2021 june scalable in situ computation of lagrangian representations via local flow maps eurographics symposium on parallel graphics and visualization egpgv https doi org 10 2312 pgv 20211040 winner best paper schwartz s d childs h pugmire d 2021 machine learning based autotuning for parallel particle advection eurographics symposium on parallel graphics and visualization egpgv https doi org 10 2312 pgv 20211039 lessley b li s childs h 2020 hashfight a platform portable hash table for multi core and many core architectures electronic imaging visualization and data analysis 376 371 376 313 13 https doi org 10 2352 issn 2470 1173 2020 1 vda 376 perciano t heinemann c camp d lessley b bethel e w 2020 shared memory parallel probabilistic graphical modeling optimization comparison of threads openmp and data parallel primitives high performance computing 127 145 https doi org 10 1007 978 3 030 50743 5_7 wang k c xu j woodring j shen h w 2019 statistical super resolution for data analysis and visualization of large scale cosmological simulations ieee pacific visualization symposium pacificvis 303 312 https doi org 10 1109 pacificvis 2019 00043 yenpure a childs h moreland k 2019 efficient point merging using data parallel techniques eurographics symposium on parallel graphics and visualization egpgv https doi org 10 2312 pgv 20191112 lessley b perciano t heinemann c camp d childs h bethel e w 2018 dpp pmrf rethinking optimization for a probabilistic graphical model using data parallel primitives proceedings of ieee symposium on large data analysis and visualization ldav 34 44 https doi org 10 1109 ldav 2018 8739239 pugmire d yenpure a kim m kress j maynard r childs h hentschel b 2018 performance portable particle advection with vtk m eurographics symposium on parallel graphics and visualization egpgv 45 55 https doi org 10 2312 pgv 20181094 lessley b moreland k larsen m childs h 2017 techniques for data parallel searching for duplicate elements ieee symposium on large data analysis and visualization ldav https doi org 10 1109 ldav 2017 8231845 lessley b perciano t mathai m childs h bethel e w 2017 maximal clique enumeration with data parallel primitives ieee symposium on large data analysis and visualization ldav https doi org 10 1109 ldav 2017 8231847 li s marsaglia n chen v sewell c clyne j childs h 2017 achieving portable performance for wavelet compression using data parallel primitives proceedings of eurographics symposium on parallel graphics and visualization egpgv 73 81 https doi org 10 2312 pgv 20171095 carr h weber g sewell c ahrens j 2016 parallel peak pruning for scalable smp contour tree computation proceedings of the ieee symposium on large data analysis and visualization ldav https doi org 10 1109 ldav 2016 7874312 lessley b binyahib r maynard r childs h 2016 external facelist calculation with data parallel primitives eurographics symposium on parallel graphics and visualization egpgv https doi org 10 2312 pgv 20161178 larsen m labasan s navrátil p meredith j childs h 2015 volume rendering via data parallel primitives eurographics symposium on parallel graphics and visualization https doi org 10 2312 pgv 20151155 note work initially performed in predecessor framework to vtk m and was subsequently ported to vtk m larsen m meredith j s navratil p a childs h 2015 ray tracing within a data parallel framework ieee pacific visualization symposium pacificvis 279 286 https doi org 10 1109 pacificvis 2015 7156388 note work initially performed in predecessor framework to vtk m and was subsequently ported to vtk m moreland k larsen m childs h 2015 visualization for exascale portable performance is critical supercomputing frontiers and innovations 2 3 https doi org 10 14529 jsfi150306 schroots h a ma k l 2015 volume rendering with data parallel visualization frameworks for emerging high performance computing architectures siggraph asia visualization in high performance computing 3 1 3 4 https doi org 10 1145 2818517 2818546 sewell c lo l ta heitmann k habib s ahrens j 2015 utilizing many core accelerators for halo and center finding within a cosmology simulation ieee 5th symposium on large data analysis and visualization ldav https doi org 10 1109 ldav 2015 7348076 note work initially performed in predecessor framework to vtk m and was subsequently ported to vtk m maynard r moreland k ayachit u geveci b ma k l 2013 optimizing threshold for extreme scale analysis visualization and data analysis 2013 proceedings of spie is t electronic imaging https doi org 10 1117 12 2007320 note work initially performed in predecessor framework to vtk m and was subsequently ported to vtk m kitware privacy policy contact