Assessing the Scalability of Garbage Collectors on Many Cores
This study evaluates the scalability of garbage collectors on multi-core systems, analyzing empirical results and factors affecting performance. Explore the impact of varying core counts and GC threads on application scalability, highlighting challenges and negative scalability trends.
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Presentation Transcript
Assessing the Scalability of Garbage Collectors on Many Cores (Funded by ANR projects: Prose and ConcoRDanT) Lokesh Gidra Julien Sopena Regal-LIP6/INRIA Ga l Thomas Marc Shapiro
Introduction Why? MREs are ubiquitous! GC, a vital component of it performance is critical? Hardware is more and more multi-resourced. Are GCs scaling with such hardware? Current solutions not evaluated on true many-cores! What? Assesses GC scalability : Empirical Results. Possible factors affecting the GC scalability. Lokesh Gidra 2
Multi-Node Architecture C0 C1 C5 C0 C1 C5 15 40 L2 L2 L2 L2 L2 L2 L3 L3 315 125 MC MC To other nodes DRAM DRAM Remote access >> Local access Our machine has 8 nodes with 6 cores each Lokesh Gidra 3
Parallel Copying Garbage Collection Mutator Threads GC Threads Application Time Pause Time Total Time Live Object Dead Object From Space To Space Lokesh Gidra 4
GCs effect on Application Scalability (Lusearch) Mutator Threads = GC Threads = Varying Number of Cores Up-to 6 cores: 3X performance improvement. More than 6 cores: No improvement in total time. Proportion of pause time increases up-to 50%. Lokesh Gidra 5
GC Scalability (Lusearch) Mutator Threads = Cores = 48 and, Varying Number of GC Threads Pause time increases with GC threads Negative Scalability! Lokesh Gidra 6
1. Remote Scanning GC Threads Node 0 Node 1 87.7% scans were remote! Node 2 Random (Default) object allocation GC0 GC1 GC2 GC3 Node 3 Live Object Dead Object From Space To Space Lokesh Gidra 7
2. Remote Copying GC Threads Node 0 82.7% copies were remote! Node 1 Node 2 GC0 GC1 GC2 GC3 Node 3 Live Object Dead Object From Space To Space Lokesh Gidra 8
3. Load Balancing Based on work stealing technique. 1 task queue per GC thread. Task Queue Owner: Push and Pop Other GC Threads: Steal (Pop) Shared Variable: size (task queue size) Highly unbalanced load: Requires a lot of stealing. Keep doing until all are done. Performance Impact: 2-4 cache misses/stealing! 33.3% improvement in pause time by disabling it! Lokesh Gidra 9
Conclusion GC does affect application s scalability it matters! GC doesn t scale with the hardware! Bottlenecks: Remote Scanning Remote Copying Load Balancing Future Work: Fix the bottlenecks does it help GC to scale? Lokesh Gidra 10
DaCapo Benchmarks Scalability Lokesh Gidra 11
Revisiting App. (Lusearch) Scalability Lokesh Gidra 12
