1. Lawrence Livermore National Laboratory BRdeS-1 Science & Technology Principal Directorate - Computation Directorate How to Stop Worrying and Learn to Love the Systems We Have Today September 10, 2009 Bronis R. de Supinski Center for Applied Scientific Computing Performance Measures x.x, x.x, and x.x

2. BRdeS-2 Science & Technology Principal Directorate - Computation Directorate Questions 1 and 5: Current metrics are inadequate; Universal metrics are unlikely; Will anyone pay?  Optimization on emerging systems requires new foci and metrics Extremely large core counts Reduced memory and memory bandwidth  Old issues remain but new ones are added Mean time to interrupt growing in importance Memory footprints must decrease Power efficiency as well as FLOP rate  Raw FLOP rates do not characterize application limits  Percent of peak merely translates FLOP rates into system specific number  Parallel efficiency and scalability are helpful but still miss application limits  Time-to-solution; Application work/unit time do not translate across applications Number of Processors (Problem Size) 10 5 1 10 6000 Time to Solution Diag-CG unscalable Multigrid-CG scalable

3. BRdeS-3 Science & Technology Principal Directorate - Computation Directorate Question 2: We require major software advances to use future systems effectively Debugging 10 3 Cores Load Balance 10 4 Cores Fault Tolerance 10 5 Cores Multicore 10 6 Cores Vector FP Units/ Accelerators? 10 7 Cores Power? 10 8 Cores Purple BG/L Petascale Exascale LLNL is creating essential capabilities for these core counts

4. BRdeS-4 Science & Technology Principal Directorate - Computation Directorate Questions 3 and 4: A hybrid of current programming models is the only sensible solution  We must make better use of vector floating point units and we can avoid more general accelerators only so long  New hardware can simplify parallelism and expose more  Better support for switching thread contexts  Transactional memory  Thread level speculation Evolution of existing programming models will allow ubiquitous support of these features and more  MPI and OpenMP satiisfy critical programming model requirements  Industry acceptance and participation  More than research funding vehicles  Highly usable as evidenced by user acceptance  I don’t know what the future parallel programming model will be but it will be called MPI