Parallel GC and Heap Management in Poly/ML and Isabelle David C.J. Matthews

1. Parallel GC and Heap Management in Poly/ML and Isabelle David C.J. Matthews David.Matthews@prolingua.co.uk

2. Summary • Minor GC • Major GC • Sharing pass • Heap sizing

3. Poly/ML and Isabelle Poly/ML David Matthews Isabelle Larry Paulson Tobias Nipkow Makarius Wenzel

4. Standard ML Mutable data • Ref • Array • Mutex / Condition variable Immutable data • List • Closure Mostly immutable Most cells have short lifetime Equality of immutables is by value

5. Minor Collector Values initially allocated in allocation area Copying collector Mutable cells to mutable area Immutable cells to immutable area Only mutable area is scanned for roots

6. Parallelising Minor Collector Multiple threads process roots Work sharing Lock-free update of forwarding pointers No locking on immutable data Weak coherence

7. Major GC Mark-sweep Compacting

8. Parallelising Major GC Lock-free marking Marking is idempotent Sweep phase can operate on separate segments

9. Sharing phase • Combine cells with same contents • Recursively apply for lists and trees • Quicksort • Parallel • Expensive - O(n log n) • Reduces the live heap size

10. Heap sizing Paging Live data GC Cost Heap size

11. Heap sizing: Small Live Data Live data GC Cost Target Heap size

12. Heap sizing: Large live data Live data GC Cost Target Heap size

13. Heap sizing • Estimate a cost function • Calculate a minimum • Trigger sharing pass

14. Parallel Isabelle

15. JinjaThreads: 64 bit

16. JinjaThreads: 32 bit