Live memory analysis for garbage collection in embedded systems

Persson, Per‐Olof

doi:10.1145/314403.314440

Cited by 23 publications

(10 citation statements)

References 14 publications

(4 reference statements)

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“…11 done w i t h ( cam_image_status : = stat ) done w i t h ( cam_image : = my_image ) ; 13 donate global Listing 1. The camera scope (= RS cam ).…”

Section: O P T I O N a L S T A R T Now W I T H I N 3msmentioning

confidence: 99%

Resource Scopes: Toward Language Support for Compositional Determinism

Anand¹,

Fischmeister²,

Lee³

2009

2009 IEEE International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing

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Complex real-time embedded systems should be compositional and deterministic in the resource, time, and value domains. Determinism eases the engineering of correct systems and compositionality simplifies the assembly of complex systems out of smaller modules. This paper describes the PEACOD framework that is developed to support deterministic behavior for resource consumption, value passing, and timing. The paper introduces the notions of determinism in the context of the resource, value, and temporal domains, and present the resource-scope language construct that can be used to program such deterministic behaviors. Furthermore, the paper also provides semantics for the resource scope construct and uses these semantics to show that the program behavior is preserved under composition. The paper briefly describes the current implementation of PEACOD. AbstractComplex real-time embedded systems should be compositional and deterministic in the resource, time, and value domains. Determinism eases the engineering of correct systems and compositionality simplifies the assembly of complex systems out of smaller modules. This paper describes the PEACOD framework that is developed to support deterministic behavior for resource consumption, value passing, and timing. The paper introduces the notions of determinism in the context of the resource, value, and temporal domains, and present the resource-scope language construct that can be used to program such deterministic behaviors. Furthermore, the paper also provides semantics for the resource scope construct and uses these semantics to show that the program behavior is preserved under composition. The paper briefly describes the current implementation of PEACOD.

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“…11 done w i t h ( cam_image_status : = stat ) done w i t h ( cam_image : = my_image ) ; 13 donate global Listing 1. The camera scope (= RS cam ).…”

Section: O P T I O N a L S T A R T Now W I T H I N 3msmentioning

confidence: 99%

Resource Scopes: Toward Language Support for Compositional Determinism

Anand¹,

Fischmeister²,

Lee³

2009

2009 IEEE International Symposium on Object/Component/Service-Oriented Real-Time Distributed Computing

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“…Leena, Stoller and Liu, [24], demonstrated an automatic space usage analysis approach for their language named MEMFL which contains many features commonly found in high-level languages. Persson, [22], made an attempt to derive such bounds using annotations, such as specifying the maximum recursion depth. It is generally assumed that developers who execute Java applications know maximum live memory for a given application, [24,13,3,21].…”

Section: Determining Application-dependent Parametersmentioning

confidence: 99%

“…Like Bacon and Goh, we use a granularity just fine enough that the part of the model pertaining to the GC implementation (the GC cost coefficients) is stable across applications and workloads. The application-dependent parameters that characterize the effects of mutator behavior are defined in a way that existing programming analysis techniques [8,21,22] can be directly used to estimate them. The model also accounts for the cost of write barriers required by the incremental collection approach but executed by the mutators.…”

Section: Introductionmentioning

confidence: 99%

Modeling Real-time Garbage Collection Cost

Hauser

2007

13th IEEE International Conference on Embedded and Real-Time Computing Systems and Applications (RTCSA 2007)

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“…Liveness analysis in such situations may not be precise, but is always conservative in that it never declares a live variable to be dead. For object-oriented languages, liveness analysis has been investigated in [26]. Restricting the set of functions a virtual function may call, is possible at compile-time, in many cases, by using techniques such as [10] which use type information to narrow down what functions can be called.…”

Section: Liveness Analysismentioning

confidence: 99%

Memory overflow protection for embedded systems using run-time checks, reuse, and compression

Biswas

Carley

Simpson

et al. 2006

ACM Trans. Embed. Comput. Syst.

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Out-of-memory errors are a serious source of unreliability in most embedded systems. Applications run out of main memory because of the frequent difficulty of estimating the memory requirement before deployment, either because it depends on input data, or because certain language features prevent estimation. The typical lack of disks and virtual memory in embedded systems has two serious consequences when an out-of-memory error occurs. First, there is no swap space for the application to grow into, and the system crashes. Second, since protection from virtual memory is usually absent, the fact that a segment has exceeded its bounds is not even detected and hence no pre-crash remedial action is possible.This work improves system reliability in two ways. First it proposes a low-overhead system of run-time checks by which the outof-memory errors are detected just before they will happen, by using carefully optimized compiler-inserted run-time check code. Such error detection enables the designer to incorporate systemspecific remedial action, such as transfer to manual control, shutting down of non-critical tasks, or other actions. Second, this work proposes five related techniques that can grow the stack or heap segment after it is out of memory, into previously un-utilized space such as dead variables and space freed by compressed live variables. These techniques can avoid the out-of-memory error if the extra space recovered is enough to complete execution.Results from our benchmarks show that the overheads from the system of run-time checks for detecting memory overflow are extremely low: the run-time and code-size overheads are 1.1% and 0.09% on average. When the reuse functionality is included, the run-time and code-size overheads increase to only 3.2% and 2.33%, but the method is able to grow the stack or heap beyond its overflow by an amount that ranges from 0.7% to 93.5% of the combined stack and heap size.

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Live memory analysis for garbage collection in embedded systems

Cited by 23 publications

References 14 publications

Resource Scopes: Toward Language Support for Compositional Determinism

Resource Scopes: Toward Language Support for Compositional Determinism

Modeling Real-time Garbage Collection Cost

Memory overflow protection for embedded systems using run-time checks, reuse, and compression

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