In　this　paper,　an　octree　pattern-based　massively　parallel　preconditioned　conjugate　gradient　(PCG)　solver　is　developed　for　large-scale　elastostatic　and　implicit　elastodynamic　applications.　The　problem　domain　is　discretised　using　octree　cells　to　enable　a　fully　automatic　mesh　generation.　The　compatibility　between　neighbouring　octree　cells　of　different　sizes　is　satisfied　by　employing　polyhedral　elements.　Matrix　operations　within　the　solver　are　carried　out　by　adopting　an　octree　pattern-based　pre-computation　approach.　Here,　a　limited　number　of　master　cells　in　a　balanced　octree　mesh　is　exploited　to　reduce　memory　requirements　and　perform　highly　efficient　matrix　product　computations.　The　parallelism　is　achieved　by　employing　a　mesh-partitioning　strategy　and　message-passing-interface　(MPI)　directives.　The　results　show　that　the　developed　PCG　solver　attains　a　significant　parallel　speed-up　and　efficiency　with　an　increasing　number　of　cores　on　distributed　memory　systems.　The　practical　applications　of　the　proposed　framework　are　demonstrated　using　large-scale　examples　for　both　CAD　and　image-based　analyses.(c)　2022　Elsevier　B.V.　All　rights　reserved.