Feb. 26, 2001L. Dennis, FSU The Search for Exotic Mesons – The Critical Role of Computing in Hall D

Talk Outline Description of the Hall D project Computing Requirements Proposed Solution

Hall D Collaboration Map

Production of Mesons and Gluonic Excitations Using 6-12 GeV Photons Fundamental Physics Role of “glue” in strong QCD Experimental Goal Unambiguous identification of gluonic excitations starting with exotic hybrids Experimental Requirements Hybrids are expected to exist precisely where we have Almost no experimental information – photoproduction Requires 6 – 12 GeV photon beam energies

Formation of Flux Tubes

Hybrids

Looking for Hybrids We should observe exotic hybrids precisely where we have no data: PHOTOPRODUCTION S = 0 – For pion and kaon probes where most of our data exist S = 1 – Use a probe with quark spins aligned - the photon where we have essentially no data

Predicted Meson Spectrum Predictions for exotic mesons come from: Lattice QCD Flux Tube Models In flux tube picture, gluons in hadrons are confined to flux tubes. Conventional mesons arise when the flux tube is in its ground state. Hybrid mesons arise when the flux tube is in an excited state. Meson Map

Hall D Online Data Acquisition CEBAF provides us with a tremendous scientific opportunity for understanding one of the fundamental forces of nature. 75 MB/s 900 MB/s

Critical Role for Computing in Hall D The quality of Hall D science depends critically upon the collaboration’s ability to conduct it’s computing tasks.

The Challenge Minimize the effort required to perform computing Data Intensive Application Compute Intensive Applications Information Intensive Analysis Research Application – methods and algorithms are not fully defined.

Trigger Rates for Hall D Detector 180 kev/s Trigger 15 kev/s 5 kB/ev 75 MB/s Trigger requires ~100 CPU’s* * Assume a factor of 10 improvement over existing CPU’s 5 CPU-ms/evFull Reconstruction (CLAS) 50 ms/ev today. 100 CPU-ms/evFull Simulation (CLAS) 1-3 s/ev today. 1/3Assumed detector & accelerator efficiency.

Required Sustained Reconstruction Rate [15 kev/s] * [1/3] * [2] = 10 kev/s Equipment Duty Factor Raw Rate Duplication Factor 10 kev/s * 5 CPU-ms/ev = 50 CPU’s

Required Sustained Simulation Rate 5 kev/s * 100 CPU-ms/ev = 500 CPU’s [15 kev/s] * [1/3] * [10] * [1/10] = 5 kev/s Equipment Duty Factor Raw Rate Systematics Studies Good Event Fraction PWA error is determined by one’s knowledge of systematic errors. This requires extensive simulations, but not all events simulated are accepted events.

Annual Date Rate to Archive Raw Data 75 MB/sec * (3 *10 7 s/yr) * (1/3)= 0.75 PB/yr Simulation Data 25 MB/sec * (3 *10 7 s/yr) = 0.75 PB/yr Reconstructed Data 50 MB/sec * (3 *10 7 s/yr) = 1.50 PB/yr Total Rate to Archive ~ 3 PB/yr

Requirements Summary

Hall D Computing Tasks First Pass Analysis Data Mining Physics Analysis Partial Wave Analysis Physics Analysis Acquisition Monitoring Slow Controls Data Archival Planning Simulation Publication Calibrations

Initial Estimate of Software Tasks & Timeline

Meeting the Hall D Computational Challenges Moore’s law: Computer performance increases by a factor of 2 every 18 months. Gilder’s Law: Network bandwidth triples every 12 months. Solving the information management problems requires people working on the software and developing a workable computing environment. Dennis’ Law: Neither Moore’s Law nor Gilder’s Law will solve our computing problems.

“Chaos of Analysis” Problem: It is impossible to efficiently complete our computing in a single large, common, democratic computer facility. Solution: Provide several sites with the resources required to complete specific tasks. Choose those sites which seek to become lead institutions in specific efforts, such as simulations, calibrations or partial wave analysis.

Hall D Grid

Common access for Physicists everywhere. Common access for Physicists everywhere. Utilizing all intellectual resources Utilizing all intellectual resources  JLab, universities, remote sites  Scientists, students Maximize total funding resources while meeting the total computing need. Maximize total funding resources while meeting the total computing need. Reduce Systems’ complexity Reduce Systems’ complexity  Partitioning of facility tasks, to manage and focus resources. Optimization of computing resources to solve the problem. Optimization of computing resources to solve the problem.  Tier-n or “Grid” Model. Reduce long-term computational management problems. Reduce long-term computational management problems. Grid Computing Advantages

Hall D Offline Data Flow

Digital Hall D Ground Rules Distributed Objects Define all programs and data as objects. Define “or wrap” everything in XML. Implement in Object Model de jour (CORBA, Java, COM, SOAP …) Does not require that we use an Object Database or that we use relational databases inappropriately. Move and query metadata rather than data whenever possible. Move the applications to the data. Assume everybody has wireless access to the “Digital Hall D” through hand-held and conventional computers.

Digital Hall D Technologies  HallD Grid Globus provides infrastructure to access computer resources around the world  HallD Grid. Structure access to Digital Hall D as a Portal –  myHallD.org Use a multi-tier software architecture separating resources, servers/brokers, display engines, display devices. Do not write any HTML – use XML and convert. Program in C++ or Java.

Hall D Grid

Vision for Grid Environment Work toward a Grid-based Operating System. Standard toolkit for manipulating objects. For example: copy, find, create, delete,… Standards for developing additional complex Grid based tools. For example: A tool that builds an acceptance function from available GEANT simulations, whose results are stored in several locations. Tools to share intermediate results of large computations. Many of these tools exist, it is remain to selecting the appropriate ones and wrap them in standardized interfaces so they can work with Hall D objects.

Foundations for Grid Sites Grid Services Data Services Compute Services Information Services Interactive Services Batch Services Needs Very Reliable Hardware & Software at Remote Sites Needs Very Reliable, Easy to Install Software at Remote Sites

Hall D Grid

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……... Hierarchy of Portals and Their Technology Portal Building Tools and Frameworks (XUL, Ninja, iPlanet, E-Speak, Portlets, WebSphere, Enterprise PortalsGeneric Portals Education & Training Portals Science Portals K-12UniversityBiology Chem Eng Collaboration Universal Access Security ……. Databases ……. User customization, component libraries, fixed channels Education ServicesCompute Services Information Services Generic Services

Collaborative Objects Digital objects shared by more than one person. Asynchronous sharing: You create/modify an object. Others access/modify it at a later time. Synchronous Collaboration: Real-time access/modification of objects by several people in distributed locations.

Virtual Experimental Control Room Could be a big win as (unexpected) real-time decisions need “experts-on-demand.” Model being considered by NASA for remote spacecraft mission control and real-time scientific analysis of earthquakes. Need collaborative decision making (vote?) and planning tools. Needs shared streaming data and shared read-outs of experimental monitors (output of all devices must be distributed objects which can be shared). Needs to support experts caught on the beach with poor connectivity or in their car with just a cell phone and a PDA.

Building Computer Science & Physics Teams for Computing System Development Physicists Computer Scientists Computing environment we need to be successful $’s Prestige Tradition $’s Prestige Tradition

Conclusions Hall D provides tremendous opportunities for new physics. Requires unprecedented computing. Grid and portal technology provide a unique new method of involving distributed intellectual resources in this important problem. The resources required to create those solutions are not yet in place.