Parallel Application Paradigms CS433 Spring 2001 Laxmikant Kale

2 Now that we know about the prog. Models... Programming models: –SAS, MPI, Charm++, …. What are the implications for what kinds of machines to build? Can we compare the models to decide which one is “better”? Answers to both questions depend on the characteristics of the applications one programs on these machines. We will next study several “classes” of applications –Often a real application will include a mixture of these –However, years of experience allows us to attempt taxanomy

3 Application paradigms Master-Slave Tree-structured, dynamic, computations –Divide-and-conquer, state-space search, branch-and-bound, game- trees Physical Simulations: continuum –Largest class of parallel applications –(Parallel Databases and Traction-processing systems?) Discrete event simulations Transaction processing

4 Physical simulations Typical scenario: –Continuous physical domain e.g. interior of rocket, bridge, airplane-wing with air flowing around, global weather, –Modeled by partial differential equations –Study/predict either steady-state behavior or time-varying behavior –Implicit or explicit timesteps Variations and subclasses –Structured and unstructured meshes arrays vs graphs –Dynamic behavior vs static Adaptive refinements –Particles

5 Master-slave May be called manager-workers One master process, and several slave processes –Manager sends tasks to the workers –worker return results to the master –No other communication (esp. among workers) Examples: –SETI, graphics, coarse-grained multiple simulations, Issues: –Master may become a bottleneck grainsize control –Load balancing Adaptive : worker asks for work when they are idle Master assigns all pieces of work to slaves at the beginning Use a “leash”? Master assigns multiple pieces to workers, –and sends new work as they complete