Multi-Core R&D Project
Optimize LHC physics software applications to run on multi-core cpus
This project is part of the CERN
SFT group in the PH DIvision and conducted in collaboration with the
OpenLab and the LHC experiments
The project ended in 2011. A new project
Concurrent Programming Models and Frameworks has been initiated that will take-over the activities carried during this R&D.
General
Latest Additions
"Recent" Summary Reports
"Recent" Event of interest
Track 1:
Objective
- Investigate current and future multi-core architectures.
- Evaluate tools to measure performance.
- Develop a measurement and analysis methodology.
Deliverables
- Assessment of industry trend in multi-core architectures.
- see reports above
- google for Nehalem, SandyBridge, Buldozer,, NuMa, multicore, manycore, gpgpu
- Recommendations on tools, metrics and methodology to assess the performance of LHC physics application software on such architectures
- Instrumentation software
Current/Recent Activities
Previous works
Track 2:
Objective
- Measure and analyze performance of current LHC physics application software on multi-core architectures
- Identify bottlenecks
- Prototype solutions at the level of system and core libraries
Deliverables
- Reports on performance of current LHC physics application software
- Recommendations on best practices to avoid bottlenecks and best exploit multi-core architectures
- courses given at the INFN school on "Architectures, tools and methodologies for developing efficient large scientific computing applications" in 2009, 2010
- Eventual materialization in software library components to implement them
Current/Recent Activities
Track 3:
Objective
- Investigate solutions to parallelize current LHC physics software at application framework level
- Identify reusable design patterns and implementation technologies to achieve parallelization
- produce prototypes
Deliverables
- Recommendations on reusable design patterns and implementation technologies to use to achieve parallelization:
- Eventual materialization in software library components to implement them
Current/Recent Activities
- parallelization of Gaudi using python
- PROOF-lite
- parallelization of ATLAS event processing framework
- parallelization of CMS event processing framework
Track 4:
- Investigate solutions to parallelize algorithms used in current LHC physics application software
- Identify reusable design patterns and implementation technologies to achieve effective high granularity parallelization
- produce prototypes
Deliverables
- Recommendations on reusable design patterns and implementation technologies to use to achieve effective high granularity parallelization
- Eventual materialization in software library components to implement them
- Parallel Minuit2 (google for it)
Current/Recent Activities
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VincenzoInnocente - 21 Oct 2007
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MarcMagransDeAbril - 04 Jul 2008