Analysing memory resource bounds for low-level programs

Chin, Wei-Ngan; Nguyen, Huu Hai; Popeea, Corneliu; Qin, Shengchao

doi:10.1145/1375634.1375656

Cited by 45 publications

(48 citation statements)

References 30 publications

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“…Base case: Trivially holds by combining (7) and (9). Inductive step: By inductive assumption we know thatX i k−1 ≥ X k−1 , and by the fact that A m k−1 contains only non-negative values we conclude that…”

Section: Proof: Inductive Over Kmentioning

confidence: 97%

“…The corresponding type inference is based on a standard linear programming solver. Chin et al [9] presents a memory resource analysis for low-level assembly programs. They infer both net usage and a high watermark bound for each computation unit based on explicit allocation and deallocation of heap space.…”

Section: Related Workmentioning

confidence: 99%

“…Recent years have seen a respectable development in techniques for analysis of live heap space usage of programs [3,14,9,18]. The common goal of this line of research is to obtain an a priori upper bound on the size of heap memory reachable from various points in a program, expressed as a function of its input data.…”

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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Section: Proof: Inductive Over Kmentioning

confidence: 97%

Section: Related Workmentioning

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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“…Besides, there are some other works that analyse or verify the bound usage or size property of resources [16]- [20], while our analysis concerns about the correct usage of resource (to avoid energy leaks).…”

Section: Implementation and Experimentsmentioning

confidence: 99%

State-taint analysis for detecting resource bugs

Cheng

Qin

2018

Science of Computer Programming

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“…Like Pareto's checker, the system is based on solving systems of Presburger formulae. Their later work considers properties about the values in containers [14], a language with references [15], space bounds for object-oriented languages [16], and applying similar techniques to assembly programs [17]. Vasconcelos has also studied these inference systems in order to produce a sized types analysis and heap and stack space bounds for Hume [18].…”

Section: Related Workmentioning

confidence: 99%

Amortised Memory Analysis Using the Depth of Data Structures

Campbell

2009

Programming Languages and Systems

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Abstract. Hofmann and Jost have presented a heap space analysis [1] that finds linear space bounds for many functional programs. It uses an amortised analysis: assigning hypothetical amounts of free space (called potential) to data structures in proportion to their sizes using type annotations. Constraints on these annotations in the type system ensure that the total potential assigned to the input is an upper bound on the total memory required to satisfy all allocations. We describe a related system for bounding the stack space requirements which uses the depth of data structures, by expressing potential in terms of maxima as well as sums. This is achieved by adding extra structure to typing contexts (inspired by O'Hearn's bunched typing [2]) to describe the form of the bounds. We will also present the extra steps that must be taken to construct a typing during the analysis.Obtaining bounds on the resource requirements of programs can be crucial for ensuring that they enjoy reliability and security properties, particularly for use in constrained systems such as mobile phones, smartcards and embedded systems. Hofmann and Jost have presented a type-based amortised analysis for finding upper bounds on the heap memory required for programs in a simple functional programming language [1]. The form of these bounds is limited to linear functions with respect to the size of the program's input. Fortunately, this is sufficient for a wide variety of interesting programs. Moreover, the analysis was successfully used to certify such bounds in a Proof Carrying Code system [3].However, it is also important to bound the stack space requirements, especially for functional programs where it is easy to cause excessive stack usage by accident. The Hofmann-Jost analysis has previously been adapted to measure stack space [4,5], but the form of the bounds was again limited to linear functions in terms of the total size of the input.In this work we present a similar analysis where bounds are given as maxplus expressions on the depth of data structures. This is far more precise for programs operating on tree-structured data.Like Hofmann-Jost, our analysis consists of two parts: a type-system which certifies that a given bound really is an upper bound on the stack memory

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Analysing memory resource bounds for low-level programs

Cited by 45 publications

References 30 publications

Live Heap Space Bounds for Real-Time Systems

Live Heap Space Bounds for Real-Time Systems

State-taint analysis for detecting resource bugs

Amortised Memory Analysis Using the Depth of Data Structures

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