Pretenuring for Java

Pretenuring is a technique for reducing copying costs in garbage collectors. When pretenuring, the allocator places long-lived objects into regions that the garbage collector will rarely, if ever, collect. We extend previous work on profiling-driven pretenuring as follows. (1) We develop a collector-neutral approach to obtaining object lifetime profile information. We show that our collection of Java programs exhibits a very high degree of homogeneity of object lifetimes at each allocation site. This result is robust with respect to different inputs, and is similar to previous work on ML, but is in contrast to C programs, which require dynamic call chain context information to extract homogeneous lifetimes. Call-site homogeneity considerably simplifies the implementation of pretenuring and makes it more efficient. (2) Our pretenuring advice is neutral with respect to the collector algorithm, and we use it to improve two quite different garbage collectors: a traditional generational collector and an older-first collector. The system is also novel because it classifies and allocates objects into 3 categories: we allocate immortal objects into a permanent region that the collector will never consider, long-lived objects into a region in which the collector placed survivors of the most recent collection, and shortlived objects into the nursery, i.e., the default region. (3) We evaluate pretenuring on Java programs. Our simulation results show that pretenuring significantly reduces collector copying for generational and older-first collectors. 1

Ramasse-miettes générationnel et incémental gérant les cycles et les gros objets en utilisant des frames délimités

Ces dernières années, des recherches ont été menées sur plusieurs techniques reliées à la collection des déchets. Plusieurs découvertes centrales pour le ramassage de miettes par copie ont été réalisées. Cependant, des améliorations sont encore possibles. Dans ce mémoire, nous introduisons des nouvelles techniques et de nouveaux algorithmes pour améliorer le ramassage de miettes. En particulier, nous introduisons une technique utilisant des cadres délimités pour marquer et retracer les pointeurs racines. Cette technique permet un calcul efficace de l'ensemble des racines. Elle réutilise des concepts de deux techniques existantes, card marking et remembered sets, et utilise une configuration bidirectionelle des objets pour améliorer ces concepts en stabilisant le surplus de mémoire utilisée et en réduisant la charge de travail lors du parcours des pointeurs. Nous présentons aussi un algorithme pour marquer récursivement les objets rejoignables sans utiliser de pile (éliminant le gaspillage de mémoire habituel). Nous adaptons cet algorithme pour implémenter un ramasse-miettes copiant en profondeur et améliorer la localité du heap. Nous améliorons l'algorithme de collection des miettes older-first et sa version générationnelle en ajoutant une phase de marquage garantissant la collection de toutes les miettes, incluant les structures cycliques réparties sur plusieurs fenêtres. Finalement, nous introduisons une technique pour gérer les gros objets. Pour tester nos idées, nous avons conçu et implémenté, dans la machine virtuelle libre Java SableVM, un cadre de développement portable et extensible pour la collection des miettes. Dans ce cadre, nous avons implémenté des algorithmes de collection semi-space, older-first et generational. Nos expérimentations montrent que la technique du cadre délimité procure des performances compétitives pour plusieurs benchmarks. Elles montrent aussi que, pour la plupart des benchmarks, notre algorithme de parcours en profondeur améliore la localité et augmente ainsi la performance. Nos mesures de la performance générale montrent que, utilisant nos techniques, un ramasse-miettes peut délivrer une performance compétitive et surpasser celle des ramasses-miettes existants pour plusieurs benchmarks. ______________________________________________________________________________ MOTS-CLÉS DE L’AUTEUR : Ramasse-Miettes, Machine Virtuelle, Java, SableVM

Using Class-Level Static Properties to Predict Object Lifetimes

Today, most modern programming languages such as C # or Java use an automatic memory management system also known as a Garbage Collector (GC). Over the course of program execution, new objects are allocated in memory, and some older objects become unreachable (die). In order for the program to keep running, it becomes necessary to free the memory of dead objects; this task is performed periodically by the GC. Research has shown that most objects die young and as a result, generational collectors have become very popular over the years. Yet, these algorithms are not good at handling long-lived objects. Typically, long-lived objects would first be allocated in the nursery space and be promoted (copied) to an older generation after surviving a garbage collection, hence wasting precious time. By allocating long-lived and immortal objects directly into infrequently or never collected regions, pretenuring can reduce garbage collection costs significantly. Current state of the art methodology to predict object lifetime involves off-line profiling combined with a simple, heuristic classification. Profiling is slow (can take days), requires gathering gigabytes of data that need to be analysed (can take hours), and needs to be repeated for every previously unseen program. This thesis explores the space of lifetime predictions and shows how object lifetimes can be predicted accurately and quickly using simple program characteristics gathered within minutes. Following an innovative methodology introduced in this thesis, object lifetime predictions are fed into a specifically modified Java virtual machine. Performance tests show gains in GC times of as much as 77% for the “SPEC jvm98” benchmarks, against a generational copying collector

A Study of Large Object Spaces

This paper examines the design space for copying garbage collectors (GCs) in which "large objects" are managed in a separate, non-copy-collected space. We focus on two main issues: 1. how to set the policy for classifying objects as "large" 2. how to manage the large object space We explore these issues experimentally using the Oscar GC testbed. In particular, we vary the threshold size of large objects and also whether the objects may contain pointers. Furthermore, we compare the performance of treadmill collection to that of mark-and-sweep collection for managing the large object space. We find that for some heaps there is a minimum cutoff size below which adding objects to the large object space does not result in a performance improvement, while for others no such cutoff exists. In general, including pointercontaining objects in the large object space seems beneficial. Finally, the exact method used to collect the large object space does not significantly influence overall performa..