Scientific Computing at SLAC: The Transition to a Multiprogram Future Richard P. Mount Director: Scientific Computing and Computing Services Stanford Linear Accelerator Center January 10, 2007

Richard P. Mount, SLAC2 SLAC Scientific Computing Balancing Act Aligned with the evolving science mission of SLAC but neither Subservient to the science mission nor Unresponsive to SLAC mission needs

January 10, 2007Richard P. Mount, SLAC3 DOE Scientific Computing Funding at SLAC circa 2005 Particle and Particle Astrophysics –$14M SCCS – $5M Other Photon Science – $0M SCCS – $1M SSRL Computer Science – $0.5M to $1.5M

January 10, 2007Richard P. Mount, SLAC4 SLAC Scientific Computing Future Science GoalsComputing Techniques BaBar Experiment (winds down ) Measure billions of collisions to understand matter-antimatter asymmetry (why matter exists today) High-throughput data processing, trivially parallel computation, heavy use of disk and tape storage Experimental HEP (LHC/ATLAS, ILC …) Analyze petabytes of data to understand the origin of mass High-throughput data processing. trivially parallel computation, heavy use of disk and tape storage Accelerator Science Current: World-leading simulation of electromagnetic structures; Future: Simulate accelerator (e.g. ILC) behavior before construction and during operation Parallel computation, visual analysis of large data volumes Particle Astrophysics (mainly simulation) Star formation in the early universe, colliding black holes, … Parallel computation (SMP and cluster), visual analysis of growing volumes of data Particle Astrophysics Major Projects (GLAST, LSST …) Analyze terabytes to petabytes of data to understand the dark matter and dark energy riddles High-throughput data processing, very large databases, visualization Photon Science (biology, physics, chemistry, environment, medicine) Current: SSRL – state-of-the art synchrotron radiation lab; Future LCLS – Femtosecond x-ray pulses, “ultrafast” science, structure of individual molecules … Medium-scale data analysis and simulation. Visualization. High throughput data analysis and large-scale simulation. Advanced visualization New Architectures for SLAC Science Radical new approaches to computing for Stanford-SLAC data- intensive science Current focus: massive solid-state storage for high-throughput, low- latency data analysis

January 10, 2007Richard P. Mount, SLAC5 Scientific Computing Equipment (2007 on: estimate of what SLAC will propose)

January 10, 2007Richard P. Mount, SLAC6 Used/Required Power (Twice the power dissipated by the Computing Equipment)

January 10, 2007Richard P. Mount, SLAC7 Scientific Computing The relationship between Science and the components of Scientific Computing SCCS FTE ~20 ~26

January 10, 2007Richard P. Mount, SLAC8 Challenges (1) Very strong, unquestionably legitimate drivers of diversity: e.g. –Development of new astrophysics and cosmology models is helped by huge SMPs –MPI and low-latency interconnects are really needed

January 10, 2007Richard P. Mount, SLAC9 Challenges (2) Very strong, but more frustrating drivers of diversity: e.g. –Macs –Postoc-supported clusters –Inability to live with HEP security models Making the environment attractive to world-leading faculty candidates and users in general Photon Science history (users bring a sample, take data for 3 days and then take it away with them) Brave new scientific worlds where nobody has any idea how understanding will be extracted from petabytes (but the hardware and software must be ready in months and detailed costing is needed yesterday).

January 10, 2007Richard P. Mount, SLAC10 Or in Other Words It’s a very exciting future.