Achieving Application Performance on the Computational Grid Francine Berman This presentation will probably involve audience discussion, which will create action items. Use PowerPoint to keep track of these action items during your presentation In Slide Show, click on the right mouse button Select “Meeting Minder” Select the “Action Items” tab Type in action items as they come up Click OK to dismiss this box This will automatically create an Action Item slide at the end of your presentation with your points entered.

The Computing Landscape data archives networks visualization instruments MPPs clusters PCs Workstations Wireless

Computing Platforms Combining resources “in the box” –focus is on new hardware, software support Combining resources as a “virtual box” –focus is on software infrastructure

The Computational Grid Computational Grid = ensemble of distributed and heterogeneous resources Metaphor: Electric Power Grid –for users, power is ubiquitous –you can plug in anywhere –you don’t need to know where the power is coming from

Better Toast On the electric power grid, power is either adequate or it’s not –On the computational grid, application performance depends on the underlying system state Major Grid research and development thrusts: –Building the Grid –Achieving performance on the Grid

Scheduling for Performance How much work should each processor be given? Jacobi2D AppLeS solves equations for Area: P1 P2P3

Adaptive Scheduling can leverage deliverable resource performance. Jacobi2D experiments

AppLeS AppLeS = Application-Level Scheduler –agent-based approach –each application integrated with its own AppLeS –each AppLeS develops and implements a custom application schedule NWS (Wolski) User Prefs App Perf Model Planner Resource Selector Application Act. Grid/cluster resources/ infrastructure Joint project with Rich Wolski at U. Tenn

A Bushel of AppLeS … almost During the first “phase” of the project, we’ve focused on developing AppLeS applications –Jacobi2D –DOT –SRB –Simple SARA –magnetohydrodynamics –CompLib –INS2D –Tomography,... Each of these AppLeS constitutes a “point solution” for application scheduling

New Directions AppLeS Templates –distributed data applications –parameter sweeps –master/slave applications –data parallel stencil applications AppLeS Template Retargeting Engineering Environment Application Module Performance Module Scheduling Module Deployment Module API Network Weather Service dynamic benchmarking suite selection

New Directions Expanding AppLeS target execution sites –interactive clusters linux, NT –Globus, Legion –batch systems –high-throughput clusters (Condor) –all of the above SCHED AppLeS

New Directions Real World Scheduling scheduling with –partial information –poor information –dynamically changing information Multischeduling resource economies scheduling “social structure” X

The Brave New World Design, development, and execution of grid-aware applications PSEPSE Config. object program whole program compiler Source appli- cation libraries Realtime perf monitor Dynamic optimizer Grid runtime system negotiation Software components Service negotiator Scheduler Performance feedback Perf problem Grid Application Development System

The AppLeS Project AppLeS Corps: –Fran Berman, UCSD –Rich Wolski, U. Tenn –Henri Casanova –Walfredo Cirne –Marcio Faerman –Jaime Frey –Jim Hayes –Graziano Obertelli –Gary Shao –Shava Smallen –Alan Su –Dmitrii Zagorodnov Thanks to NSF, NASA, NPACI, DARPA, DoD AppLeS Home Page: