Decrypting The Java Gene Pool: Predicting Objects' Lifetimes with Micro-patterns

Pretenuring long-lived and immortal objects into infrequently or never collected regions reduces garbage collection costs significantly. However, extant approaches either require computationally expensive, application-specific, off-line profiling, or consider only allocation sites common to all programs, i.e. invoked by the virtual machine rather than application programs. In contrast, we show how a simple program analysis, combined with an object lifetime knowledge bank, can be exploited to match both runtime system and application program structure with object lifetimes. The complexity of the analysis is linear in the size of the program, so need not be run ahead of time. We obtain performance gains between 6-77% in GC time against a generational copying collector for several SPEC jvm98 programs

Parametric Inference of Memory Requirements for Garbage Collected Languages

The accurate prediction of program's memory requirements is a critical component in software development. Existing heap space analyses either do not take deallocation into account or adopt specific models of garbage collectors which do not necessarily correspond to the actual memory usage. We present a novel approach to inferring upper bounds on memory requirements of Java-like programs which is parametric on the notion of object lifetime, i.e., on when objects become collectible. If objects lifetimes are inferred by a reachability analysis, then our analysis infers accurate upper bounds on the memory consumption for a reachability-based garbage collector. Interestingly, if objects lifetimes are inferred by a heap liveness analysis, then we approximate the program minimal memory requirement, i.e., the peak memory usage when using an optimal garbage collector which frees objects as soon as they become dead. The key idea is to integrate information on objects lifetimes into the process of generating the recurrence equations which capture the memory usage at the different program states. If the heap size limit is set to the memory requirement inferred by our analysis, it is ensured that execution will not exceed the memory limit with the only assumption that garbage collection works when the limit is reached. Experiments on Java bytecode programs provide evidence of the feasibility and accuracy of our analysis

Emulating and evaluating hybrid memory for managed languages on NUMA hardware

Non-volatile memory (NVM) has the potential to become a mainstream memory technology and challenge DRAM. Researchers evaluating the speed, endurance, and abstractions of hybrid memories with DRAM and NVM typically use simulation, making it easy to evaluate the impact of different hardware technologies and parameters. Simulation is, however, extremely slow, limiting the applications and datasets in the evaluation. Simulation also precludes critical workloads, especially those written in managed languages such as Java and C#. Good methodology embraces a variety of techniques for evaluating new ideas, expanding the experimental scope, and uncovering new insights. This paper introduces a platform to emulate hybrid memory for managed languages using commodity NUMA servers. Emulation complements simulation but offers richer software experimentation. We use a thread-local socket to emulate DRAM and a remote socket to emulate NVM. We use standard C library routines to allocate heap memory on the DRAM and NVM sockets for use with explicit memory management or garbage collection. We evaluate the emulator using various configurations of write-rationing garbage collectors that improve NVM lifetimes by limiting writes to NVM, using 15 applications and various datasets and workload configurations. We show emulation and simulation confirm each other's trends in terms of writes to NVM for different software configurations, increasing our confidence in predicting future system effects. Emulation brings novel insights, such as the non-linear effects of multi-programmed workloads on NVM writes, and that Java applications write significantly more than their C++ equivalents. We make our software infrastructure publicly available to advance the evaluation of novel memory management schemes on hybrid memories

On Verifying Resource Contracts using Code Contracts

In this paper we present an approach to check resource consumption contracts using an off-the-shelf static analyzer. We propose a set of annotations to support resource usage specifications, in particular, dynamic memory consumption constraints. Since dynamic memory may be recycled by a memory manager, the consumption of this resource is not monotone. The specification language can express both memory consumption and lifetime properties in a modular fashion. We develop a proof-of-concept implementation by extending Code Contracts' specification language. To verify the correctness of these annotations we rely on the Code Contracts static verifier and a points-to analysis. We also briefly discuss possible extensions of our approach to deal with non-linear expressions.Comment: In Proceedings LAFM 2013, arXiv:1401.056

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

Crystal gazer : profile-driven write-rationing garbage collection for hybrid memories

Non-volatile memories (NVM) offer greater capacity than DRAM but suffer from high latency and low write endurance. Hybrid memories combine DRAM and NVM to form scalable memory systems with the promise of high capacity, low energy consumption, and high endurance. Automatically managing hybrid NVM-DRAM memories to achieve their promise without changing user applications or their programming models remains an open question. This paper uses garbage collection in managed languages to exploit NVM capacity while preventing NVM wear out in hybrid memories with no changes to the programming model. We introduce profile-driven write-rationing garbage collection. Allocation sites that produce frequently written objects are predicted based on previous program executions. Objects are initially allocated in a DRAM nursery space. The collector copies surviving nursery objects from highly written sites to a mature DRAM space and read-mostly objects to a mature NVM space.Write-intensity prediction for 15 Java benchmarks accurately places objects in the correct space, eliminating expensive object monitoring from prior write-rationing garbage collectors. Furthermore, our technique exposes a Pareto tradeoff between DRAM usage and NVM lifetime, unlike prior work. Experimental results on NUMA hardware that emulates hybrid NVM-DRAM memory demonstrates that profile-driven write-rationing garbage collection reduces the number of writes to NVM compared to prior work to extend its lifetime, maximizes the use of NVM for its capacity, and achieves good performance