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Memory Reuse Strategies in Multiprocessing: Physics Tables Optimization for Efficient Sharing

This document delves into the limited memory reuse in multi-processing, focusing on strategies to increase efficiency, such as caching in physics tables and revising designs for better reuse. It discusses the challenges and outcomes of optimizing memory sharing for improved performance.

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Memory Reuse Strategies in Multiprocessing: Physics Tables Optimization for Efficient Sharing

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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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