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Physics tables and multi-core

Physics tables and multi-core. J. Apostolakis. Motivation. Limited reuse of memory in Multi-Processing A forked process shares all pages of memory which read-only (called ‘Copy-on-write’=COW) With Geant4 less than 30% can be shared Leading reasons: Caching in physics tables

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Physics tables and multi-core

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  1. Physics tables and multi-core J. Apostolakis

  2. Motivation • Limited reuse of memory in Multi-Processing • A forked process shares all pages of memory which read-only (called ‘Copy-on-write’=COW) • With Geant4 less than 30% can be shared • Leading reasons: • Caching in physics tables • Replicas’ copy numbers. • With simple changes could increase reuse

  3. Inside a Physics Table Physics Vector (material 1) Physics Vector (material 2) Physics Vector (material 3) Physics Table

  4. Inside a Physics Vector Last bin Last value Last 2nd Deriv. Scalars Energy Vector Value Vector 2nd Derivative Vector Cache: Read/Write Read-only after filling (initialization) 3 doubles Typically 70-150 double values each Typically created in consecutive areas of the heap. The result is: By writing the 3 doubles (cache) a process creates copy of page(s) which containing ~ 300 doubles

  5. Multiprocessing and ‘timing’ • BeamOn is called • Initialization of geometry • Initialization of physics processes • First event is processed • Worker processes (or threads) are created • Fork waits until the physics tables are initialized!

  6. Requirement for multiprocessing • Use separate areas of memory • One for the scalars (which are rewritten) • A different one for the vectors • Fork waits until the tables are initialized & shared.

  7. Complications? Other large arrays used by physics processes • Static arrays in Brems, pair production, Goldsmith-SaundersonMSc • C-arrays in • Hadron Elastic (no caching) • CHIPS Elastic (?caching?)

  8. Outcome • Revision of design of physics vector • To separate areas of memory for scalars and vectors • Hisaya was present – he maintains phys. vector

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