Live heap space analysis for languages with garbage collection

Albert, Elvira; Genaim, Samir; Gil, Miguel Gómez-Zamalloa

doi:10.1145/1542431.1542450

Cited by 36 publications

(72 citation statements)

References 22 publications

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“…Following [1,2] we express the actual code a task executes in a rule-based representation, or RBR for short. Our RBR is essentially a slight simplification of the format used in [2].…”

Section: Rule-based Representationmentioning

confidence: 99%

“…Our RBR is essentially a slight simplification of the format used in [2]. We assume four distinct name-spaces: ordinary variables ranged over by x and y, procedure names ranged over by p and q, as well as constructor names and field names (ranged over by c and f , respectively).…”

Section: Rule-based Representationmentioning

confidence: 99%

“…Inferring size relations, similarly to e.g. [8,1,2] is performed in two steps. The first one is abstract compilation of rules into linear constraints capturing relations between sizes of program variables.…”

Section: Inferring Size Relationsmentioning

confidence: 99%

“…The size value is not accumulated over recursive calls that might take place while executing the tasks. Therefore, for our purpose we do not need cost relations in the form of [1,2], but rather than that we work directly with the size relations introduced in the previous section. Based on the FSM representation of task execution orders and size relations for each task, we set up a system of linear constraints which is essentially an ILP (integer linear programming) problem that can be solved by any standard solver.…”

Section: Upper Boundsmentioning

confidence: 99%

“…We apply a standard variant of abstract interpretation to each task for inferring size relations [11], which capture how each individual task affects an abstract notion of size for every persistent state variable (Section 5). The input to this step is a variant of the rule-based representation (RBR) introduced in [2] for describing sequential imperative code that may involve iterative or recursive computations (Section 4).…”

Section: Introductionmentioning

confidence: 99%

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Live Heap Space Bounds for Real-Time Systems

Kero

Pietrzak

Nordlander

2010

Programming Languages and Systems

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Abstract. Live heap space analyses have so far been concerned with the standard sequential programming model. However, that model is not very well suited for embedded real-time systems, where fragments of code execute concurrently and in orders determined by periodic and sporadic events. Schedulability analysis has shown that the programming model of real-time systems is not fundamentally in conflict with static predictability, but in contrast to accumulative properties like time, live heap space usage exhibits a very state-dependent behavior that renders direct application of schedulability analysis techniques unsuitable. In this paper we propose an analysis of live heap space upper bounds for real-time systems based on an accurate prediction of task execution orders. The key component of our analysis is the construction of a nondeterministic finite state machine capturing all task executions that are legal under given timing assumptions. By adding heap usage information inferred for each sequential task, our analysis finds an upper bound on the inter-task heap demands as the solution to an integer linear programming problem. Values so obtained are suitable inputs to other analyses depending on the size of a system's persistent state, such as running time prediction for a concurrent tracing garbage collector.

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“…Following [1,2] we express the actual code a task executes in a rule-based representation, or RBR for short. Our RBR is essentially a slight simplification of the format used in [2].…”

Section: Rule-based Representationmentioning

confidence: 99%

Section: Rule-based Representationmentioning

confidence: 99%

Section: Inferring Size Relationsmentioning

confidence: 99%

Section: Upper Boundsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Live Heap Space Bounds for Real-Time Systems

Kero

Pietrzak

Nordlander

2010

Programming Languages and Systems

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Quantitative dynamic‐memory analysis for Java

Garbervetsky

Yovine

Braberman

et al. 2010

Concurrency and Computation

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Space-and time-predictability are hard to achieve for object-oriented languages with automated dynamicmemory management. Although there has been significant work to design APIs, such as the Real-Time Specification for Java (RTSJ), and to implement garbage collectors to enable real-time performance, quantitative space analysis is still in its infancy. This work presents the integration of a series of compiletime analysis techniques to help predicting quantitative memory usage. In particular, we focus on providing tool assistance for identifying RTSJ scoped-memory regions, their sizes, and overall memory usage. First, the tool-suite synthesizes a memory organization where regions are associated with methods. Second, it infers their sizes in parametric closed form in terms of relevant program variables. Third, it exhibits a parametric upper bound on the amount of available free memory required to execute a method. The experiments carried out with a RTSJ benchmark, a real-time aircraft collision detector, show that semiautomatic, tool-assisted generation of scoped-based code is both helpful and doable.An interesting approach to gain control on time and space is to change the memory organization model allocating objects in regions [4,5]. The Real-time Specification for Java (RTSJ) [6] supports application-level region-based memory management through ScopedMemory areas. Under suitable conditions, this environment guarantees both predictable-time operations and predictable-space occupancy at the expense of making programming more difficult. Indeed, complying with RTSJ rules complicates reusing legacy code without careful modification [7]. More importantly in practice, it forces the programmer to adopt new coding habits and to reason in a new paradigm quite different from Java. Moreover, ensuring predictability requires providing upper-bounds of region sizes.This paper presents an integration of a series of techniques and tools into a tool-suite to help developers tackling the aforementioned problems. It also explains how to use it to produce sound and predictable scope-memory managed code from conventional Java code. The tool-suite is meant to be used following the scenario depicted in Figure 1. The basic idea is to follow a two-step approach to assist programmers in generating regions. First, infer memory scopes with the aid of escape analysis [8,9]. Second, fine tune the memory layout by resorting to a region edition tool [10] and explicit program annotations.Furthermore, the tool-chain provides two unique key features, which are independent of the way objects are organized into regions. The first is the ability to produce upper bounds of region sizes as functions of program variables [11]. Such functions are used at runtime to determine the actual sizes of RTSJ memory-scopes. The second is the symbolic over-approximation of the dynamic memory footprint of a method (including its callees) [12]. This enables checking whether the method can be safely executed without running out of memory. That is, the resulting express...

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Resource analyses for parallel and distributed coordination

Trinder

Cole

Hammond

et al. 2011

Concurrency and Computation

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SUMMARYPredicting the resources that are consumed by a program component is crucial for many parallel or distributed systems. In this context, the main resources of interest are execution time, space and communication/synchronisation costs. There has recently been significant progress in resource analysis technology, notably in type-based analyses and abstract interpretation. At the same time, parallel and distributed computing are becoming increasingly important.This paper synthesises progress in both areas to survey the state-of-the-art in resource analysis for parallel and distributed computing. We articulate a general model of resource analysis and describe parallel/distributed resource analysis together with the relationship to sequential analysis. We use three parallel or distributed resource analyses as examples and provide a critical evaluation of the analyses. We investigate why the chosen analysis is effective for each application and identify general principles governing why the resource analysis is effective.

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Live heap space analysis for languages with garbage collection

Cited by 36 publications

References 22 publications

Live Heap Space Bounds for Real-Time Systems

Live Heap Space Bounds for Real-Time Systems

Quantitative dynamic‐memory analysis for Java

Resource analyses for parallel and distributed coordination

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