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.
Architectures for Iterative Data Intensive Analysis Computations on Clouds and Heterogeneous Environments
SESSION: Doctoral Research Showcase (2 of 2)
EVENT TYPE: Doctoral Research Showcase
TIME: 4:45PM - 5:00PM
SESSION CHAIR: Volodymyr Kindratenko
Presenter(s):Thilina Gunarathne
ROOM:TCC LL1
ABSTRACT: Iterative computations are at the core of the vast majority of data-intensive scientific computations. Recent advancements in data intensive computational fields are fueling a dramatic growth in number as well as usage of such data intensive iterative computations. The utility computing model introduced by cloud computing combined with the rich set of cloud infrastructure services offers a very viable environment for the scientists to perform data intensive computations. However, clouds by nature offer unique reliability and sustained performance challenges to large scale distributed computations necessitating computation frameworks specifically tailored for cloud characteristics to harness the power of clouds easily and effectively. My research focuses on identifying and developing user-friendly distributed parallel computation frameworks to facilitate the optimized efficient execution of iterative as well as non-iterative data-intensive computations in cloud environments, alongside the evaluation of heterogeneous cloud resources offering GPGPU resources in addition to CPU resources, for data-intensive iterative computations.
