Heap Usage Research Articles

Detailed heap profiling

Modern software systems heavily use the memory heap. As systems grow more complex and compute with increasing amounts of data, it can be difficult for developers to understand how their programs actually use the bytes that they allocate on the heap and whether improvements are possible. To answer this question of heap usage efficiency, we have built a new, detailed heap profiler called Memoro. Memoro uses a combination of static instrumentation, subroutine interception, and runtime data collection to build a clear picture of exactly when and where a program performs heap allocation, and crucially how it actually uses that memory. Memoro also introduces a new visualization application that can distill collected data into scores and visual cues that allow developers to quickly pinpoint and eliminate inefficient heap usage in their software. Our evaluation and experience with several applications demonstrates that Memoro can reduce heap usage and produce runtime improvements of 10%.

Summary-based inference of quantitative bounds of live heap objects

This article presents a symbolic static analysis for computing parametric upper bounds of the number of simultaneously live objects of sequential Java-like programs. Inferring the peak amount of irreclaimable objects is the cornerstone for analyzing potential heap-memory consumption of stand-alone applications or libraries. The analysis builds method-level summaries quantifying the peak number of live objects and the number of escaping objects. Summaries are built by resorting to summaries of their callees. The usability, scalability and precision of the technique is validated by successfully predicting the object heap usage of a medium-size, real-life application which is significantly larger than other previously reported case-studies.

Adaptive scanning reduces sweep time for the Lisp2 mark-compact garbage collector

Mark-compact garbage collection helps long-running programs avoid fragmentation. The Lisp2 mark-compact collector is a classic but still widely-used compaction algorithm. It sequentially scans the entire heap to compact all live objects at one end of the heap while preserving their order of addresses. Since the heap is generally large, this scanning takes a long time. Although some collectors adopt a separate bitmap into which mark bits of objects are stored to reduce the scanning time, we observed that scanning the bitmap can take longer than scanning the heap if objects are densely located. We propose a new scanning method from this observation, which adaptively alternates methods of scanning depending on heap usage; it scans those parts of the heap where live objects are densely located whereas it scans the bitmap for the remaining parts. We implemented this scanning method in the Lisp2 collector of Jikes RVM. The experimental results revealed that the adaptive scanner scanned faster than the method that only scanned the heap and the method that only scanned the bitmap.

Heap space analysis for garbage collected languages

Accurately predicting the dynamic memory consumption (or heap space) of programs can be critical during software development. It is well-known that garbage collection (GC) complicates such problem. The peak heap consumption of a program is the maximum size of the data on the heap during its execution, i.e., the minimum amount of heap space needed to safely run the program. 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. This paper presents a novel static analysis for garbage-collected imperative languages that infers accurate upper bounds on the peak heap usage, including exponential, logarithmic and polynomial bounds. A unique characteristic of the analysis is that it is parametric on the notion of object lifetime, i.e., on when objects become collectible.

Microphase

To date, the most commonly used criterion for invoking garbage collection (GC) is based on heap usage; that is, garbage collection is invoked when the heap or an area inside the heap is full. This approach can suffer from two performance shortcomings: untimely garbage collection invocations and large volumes of surviving objects. In this work, we explore a new GC triggering approach called MicroPhase that exploits two observations: (i) allocation requests occur in phases and (ii) phase boundaries coincide with times when most objects also die. Thus, proactively invoking garbage collection at these phase boundaries can yield high efficiency. We extended the HotSpot virtual machine from Sun Microsystems to support MicroPhase and conducted experiments using 20 benchmarks. The experimental results indicate that our technique can reduce the GC times in 19 applications. The differences in GC overhead range from an increase of 1% to a decrease of 26% when the heap is set to twice the maximum live-size. As a result, MicroPhase can improve the overall performance of 13 benchmarks. The performance differences range from a degradation of 2.5% to an improvement of 14%.

Profiling with AspectJ

AbstractThis paper investigates whether AspectJ can be used for efficient profiling of Java programs. Profiling differs from other applications of AOP (e.g. tracing), since it necessitates efficient and often complex interactions with the target program. As such, it was uncertain whether AspectJ could achieve this goal. Therefore, we investigate four common profiling problems (heap usage, object lifetime, wasted time and time‐spent) and report on how well AspectJ handles them. For each, we provide an efficient implementation, discuss any trade‐offs or limitations and present the results of an experimental evaluation into the costs of using it. Our conclusions are mixed. On the one hand, we find that AspectJ is sufficiently expressive to describe the four profiling problems and reasonably efficient in most cases. On the other hand, we find several limitations with the current AspectJ implementation that severely hamper its suitability for profiling. Copyright © 2006 John Wiley & Sons, Ltd.

Static prediction of heap space usage for first-order functional programs

We show how to efficiently obtain linear a priori bounds on the heap space consumption of first-order functional programs.The analysis takes space reuse by explicit deallocation into account and also furnishes an upper bound on the heap usage in the presence of garbage collection. It covers a wide variety of examples including, for instance, the familiar sorting algorithms for lists, including quicksort.The analysis relies on a type system with resource annotations. Linear programming (LP) is used to automatically infer derivations in this enriched type system.We also show that integral solutions to the linear programs derived correspond to programs that can be evaluated without any operating system support for memory management. The particular integer linear programs arising in this way are shown to be feasibly solvable under mild assumptions.

Extending resource-bounded functional programming languages with mutable state and concurrency

Camelot is a resource-bounded functional programming language which compiles to Java byte code to run on the Java Virtual Machine. We extend Camelot to include language support for Camelot-level threads which are compiled to native Java threads. We extend the existing Camelot resource-bounded type system to provide safety guarantees about the heap usage of Camelot threads. We demonstrate the usefulness of our concurrency extensions to the language by implementing a multi-threaded graphical network chat application which could not have been expressed as naturally in the sequential, object-free sublanguage of Camelot which was previously available.

A simple approach to supporting untagged objects in dynamically typed languages

In many modern high-level programming languages, the exact low-level representation of data objects cannot always be predicted at compile-time. Implementations usually get around this problem using descriptors (“tags”) and/or indirect (“boxed”) representations. However, the flexibility so gained can come at the cost of significant performance overheads. The problem is especially acute in dynamically typed languages, where both tagging and boxing are necessary in general. This paper discusses a straightforward approach to using untagged and unboxed values in dynamically typed languages. An implementation of our algorithms allows a dynamically typed language to attain performance close to that of highly optimized C code on a variety of benchmarks (including many floating-point intensive computations) and dramatically reduces heap usage.