When viewing the Technical Program schedule, on the far righthand side
is a column labeled "PLANNER." Use this planner to build your own
schedule. Once you select an event and want to add it to your personal
schedule, just click on the calendar icon of your choice (outlook
calendar, ical calendar or google calendar) and that event will be
stored there. As you select events in this manner, you will have your
own schedule to guide you through the week.
You can also create your personal schedule on the SC11 app (Boopsie) on your smartphone. Simply select a session you want to attend and "add" it to your plan. Continue in this manner until you have created your own personal schedule. All your events will appear under "My Event Planner" on your smartphone.
S08: Linear Algebra Libraries for High-Performance Computing: Scientific Computing with Multicore and Accelerators
SESSION: S08: Linear Algebra Libraries for High-Performance Computing: Scientific Computing with Multicore and Accelerators
EVENT TYPE: Tutorial
TIME: 8:30AM - 5:00PM
Presenter(s):Jack Dongarra, James Demmel, Michael Heroux, Jakub Kurzak
ABSTRACT: Today, a desktops with a multicore processor and a GPU accelerator can already provide a TeraFlop/s of performance, while the performance of the high-end systems, based on multicores and accelerators, is already measured in PetaFlop/s. This tremendous computational power can only be fully utilized with the appropriate software infrastructure, both at the low end (desktop, server) and at the high end (supercomputer installation). Most often a major part of the computational effort in scientific and engineering computing goes in solving linear algebra subproblems. This tutorial surveys the state-of-the-art numerical libraries for solving problems in linear algebra, both dense and sparse. We highlight recent algorithms that minimize communication, i.e. moving data, which is much more expensive than arithmetic. The following software packages are presented: PLASMA & MAGMA for solving dense systems using multicores and accelerators, Trilinos for solving sparse systems using multicore, accelerators and distributed memory systems.
Jack Dongarra - University of Tennessee, Knoxville