Description: The Petascale Computing Enabling Technologies (PCET) project addressed challenges arising from current trends in computer architecture that will lead to large-scale systems with many more nodes, each of which uses multicore chips. These factors will soon lead to systems that have over one million processors. Also, the use of multicore chips will lead to less memory and less memory bandwidth per core. We need fundamentally new algorithmic approaches to cope with these memory constraints and the huge number of processors. Further, correct, efficient code development is difficult even with the number of processors in current systems; more processors will only make it harder. The goal of PCET was to overcome these challenges by developing the computer science and mathematical underpinnings needed to realize the full potential of our future large-scale systems. Our research results will significantly increase the scientific output obtained from LLNL large-scale computing resources by improving application scientist productivity and system utilization. Our successes include scalable mathematical algorithms that adapt to these emerging architecture trends and through code correctness and performance methodologies that automate critical aspects of application development as well as the foundations for application-level fault tolerance techniques. PCET's scope encompassed several research thrusts in computer science and mathematics: code correctness and performance methodologies, scalable mathematics algorithms appropriate for multicore systems, and application-level fault tolerance techniques. Due to funding limitations, we focused primarily on the first three thrusts although our work also lays the foundation for the needed advances in fault tolerance. In the area of scalable mathematics algorithms, our preliminary work established that OpenMP performance of the AMG linear solver benchmark and important individual kernels on Atlas did not match the predictions of our simple initial model. Our investigations demonstrated that a poor default memory allocation mechanism degraded performance. We developed a prototype NUMA library to ...
Date: February 14, 2010
Creator: de Supinski, B R
Item Type: Refine your search to only Report
Partner: UNT Libraries Government Documents Department