![]() The ‘exa’ metric prefix stands for quintillion, and the proposed exascale computers would approximately perform as many operations per second as 50 million laptops. At the time of writing, the following implementations exist: (1) QMP-GM - Uses GM (2) QMP-MPI - Uses MPI tested above MPICH-GM, MPICH-SM (shared memory), and MPICH-P4 (sockets).As supercomputing moves towards exascale, scientists, engineers and medical researchers will look for efficient and cost effective ways to enable data analysis and visualization for the products of their computational efforts. These are meant to more fully illustrate the functionality, and are not intended as the final design. Interspersed with the API description are some descriptions for how the API could be implemented for myrinet clusters and the QCDOC machine. Further, the new API has been implemented atop MPI so that new applications using this new API can still be run on older machines for which only MPI is available. Depending upon demand, a subset of MPI could be implemented above this new API so that legacy codes which use MPI could function on the new architectures which implement (only) the new API. Because of the highly regular grid communications with LQCD, MPI calls (which are more general) impose some additional overhead that is predicted to be non-negligible for large machines. The API is intended to be sufficiently flexible more » to be used by all Lattice QCD applications, and execute efficiently on all existing and anticipated platforms, so that there is no need to directly call non-portable message passing routines. This note presents: (1) the requirements for message passing within Lattice QCD applications (2) a draft message API for both C and C++ and (3) implementation design ideas. (2) Methods related to node numbers have been changed (some dropped, some added). Thus there are two styles of messaging: messages are sent to a node by node number, or messages are sent to a relative (logical) node. ![]() Recent changes are: (1) There is no longer a logical node number, only a node number which does not change as the logical machine is define. Here, we provide an overview of the Catalyst framework and some of the success stories. Built on and designed to interoperate with the standard visualization toolkit VTK and the ParaView application, Catalyst enables simulations to intelligently perform analysis, generate relevant output data, and visualize results concurrent with a running simulation. ParaView Catalyst is a data processing and visualization library that enables in situ analysis and visualization. In situ analysis moves more » some of the post-processing tasks in line with the simulation code thus short circuiting the need to communicate the data between the simulation and analysis via storage. In situ analysis is recognized as one of the ways to address these challenges. However, the increasing data sizes, and limited storage and bandwidth make high fidelity post-processing impractical. Interactive visualization tools, like ParaView, have been used for post-processing of simulation results. However these advances come with several costs including massive increases in data size, difficulties examining output data, challenges in configuring simulation runs, and difficulty debugging running codes. This trend has been enabled by advances in numerical methods and increasing computing power. Computer simulations are growing in sophistication and producing results of ever greater fidelity.
0 Comments
Leave a Reply. |