Parametric prediction of heap memory requirements
This work presents a technique to compute symbolic polynomial approximations of the amount of dynamic memory required to safely execute a method without running out of memory, for Javalike imperative programs. We consider object allocations and deallocations made by the method and the methods it tra...
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Acceso en línea: | http://hdl.handle.net/20.500.12110/paper_97816055_v_n_p141_Braberman |
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todo:paper_97816055_v_n_p141_Braberman2023-10-03T16:44:06Z Parametric prediction of heap memory requirements Braberman, V. Fernández, F. Garbervetsky, D. Yovine, S. Heap consumption Heap space analysis Java Memory regions Bernstein bases Dynamic memories Heap consumption Heap space analysis Imperative programs Initial configurations Java Life-times Memory consumptions Memory managements Memory regions Memory requirements Object allocations Out of memories Polyhedral domains Polynomial optimization problems Polynomial approximation This work presents a technique to compute symbolic polynomial approximations of the amount of dynamic memory required to safely execute a method without running out of memory, for Javalike imperative programs. We consider object allocations and deallocations made by the method and the methods it transitively calls. More precisely, given an initial configuration of the stack and the heap, the peak memory consumption is the maximum space occupied by newly created objects in all states along a run from it. We over-approximate the peak memory consumption using a scoped-memory management where objects are organized in regions associated with the lifetime of methods. We model the problem of computing the maximum memory occupied by any region configuration as a parametric polynomial optimization problem over a polyhedral domain and resort to Bernstein basis to solve it. We apply the developed tool to several benchmarks.Copyright © 2008 ACM. Fil:Braberman, V. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Fernández, F. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. Fil:Garbervetsky, D. Universidad de Buenos Aires. Facultad de Ciencias Exactas y Naturales; Argentina. CONF info:eu-repo/semantics/openAccess http://creativecommons.org/licenses/by/2.5/ar http://hdl.handle.net/20.500.12110/paper_97816055_v_n_p141_Braberman |
institution |
Universidad de Buenos Aires |
institution_str |
I-28 |
repository_str |
R-134 |
collection |
Biblioteca Digital - Facultad de Ciencias Exactas y Naturales (UBA) |
topic |
Heap consumption Heap space analysis Java Memory regions Bernstein bases Dynamic memories Heap consumption Heap space analysis Imperative programs Initial configurations Java Life-times Memory consumptions Memory managements Memory regions Memory requirements Object allocations Out of memories Polyhedral domains Polynomial optimization problems Polynomial approximation |
spellingShingle |
Heap consumption Heap space analysis Java Memory regions Bernstein bases Dynamic memories Heap consumption Heap space analysis Imperative programs Initial configurations Java Life-times Memory consumptions Memory managements Memory regions Memory requirements Object allocations Out of memories Polyhedral domains Polynomial optimization problems Polynomial approximation Braberman, V. Fernández, F. Garbervetsky, D. Yovine, S. Parametric prediction of heap memory requirements |
topic_facet |
Heap consumption Heap space analysis Java Memory regions Bernstein bases Dynamic memories Heap consumption Heap space analysis Imperative programs Initial configurations Java Life-times Memory consumptions Memory managements Memory regions Memory requirements Object allocations Out of memories Polyhedral domains Polynomial optimization problems Polynomial approximation |
description |
This work presents a technique to compute symbolic polynomial approximations of the amount of dynamic memory required to safely execute a method without running out of memory, for Javalike imperative programs. We consider object allocations and deallocations made by the method and the methods it transitively calls. More precisely, given an initial configuration of the stack and the heap, the peak memory consumption is the maximum space occupied by newly created objects in all states along a run from it. We over-approximate the peak memory consumption using a scoped-memory management where objects are organized in regions associated with the lifetime of methods. We model the problem of computing the maximum memory occupied by any region configuration as a parametric polynomial optimization problem over a polyhedral domain and resort to Bernstein basis to solve it. We apply the developed tool to several benchmarks.Copyright © 2008 ACM. |
format |
CONF |
author |
Braberman, V. Fernández, F. Garbervetsky, D. Yovine, S. |
author_facet |
Braberman, V. Fernández, F. Garbervetsky, D. Yovine, S. |
author_sort |
Braberman, V. |
title |
Parametric prediction of heap memory requirements |
title_short |
Parametric prediction of heap memory requirements |
title_full |
Parametric prediction of heap memory requirements |
title_fullStr |
Parametric prediction of heap memory requirements |
title_full_unstemmed |
Parametric prediction of heap memory requirements |
title_sort |
parametric prediction of heap memory requirements |
url |
http://hdl.handle.net/20.500.12110/paper_97816055_v_n_p141_Braberman |
work_keys_str_mv |
AT brabermanv parametricpredictionofheapmemoryrequirements AT fernandezf parametricpredictionofheapmemoryrequirements AT garbervetskyd parametricpredictionofheapmemoryrequirements AT yovines parametricpredictionofheapmemoryrequirements |
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1807316302510948352 |