Hostname: page-component-78c5997874-lj6df Total loading time: 0 Render date: 2024-11-19T12:51:45.264Z Has data issue: false hasContentIssue false

Resource Analysis driven by (Conditional) Termination Proofs

Published online by Cambridge University Press:  20 September 2019

ELVIRA ALBERT
Affiliation:
DSIC, Complutense University of Madrid (UCM), E-28040 Madrid, Spain
MIQUEL BOFILL
Affiliation:
IMAE, University of Girona (UdG), E-17003 Girona, Spain
CRISTINA BORRALLERAS
Affiliation:
University of Vic - Central University of Catalonia (UVic-UCC), 08500 Vic (Barcelona), Spain
ENRIQUE MARTIN-MARTIN
Affiliation:
DSIC, Complutense University of Madrid (UCM), E-28040 Madrid, Spain
ALBERT RUBIO
Affiliation:
DSIC, Complutense University of Madrid (UCM), E-28040 Madrid, Spain

Abstract

When programs feature a complex control flow, existing techniques for resource analysis produce cost relation systems (CRS) whose cost functions retain the complex flow of the program and, consequently, might not be solvable into closed-form upper bounds. This paper presents a novel approach to resource analysis that is driven by the result of a termination analysis. The fundamental idea is that the termination proof encapsulates the flows of the program which are relevant for the cost computation so that, by driving the generation of the CRS using the termination proof, we produce a linearly-bounded CRS (LB-CRS). A LB-CRS is composed of cost functions that are guaranteed to be locally bounded by linear ranking functions and thus greatly simplify the process of CRS solving. We have built a new resource analysis tool, named MaxCore, that is guided by the VeryMax termination analyzer and uses CoFloCo and PUBS as CRS solvers. Our experimental results on the set of benchmarks from the Complexity and Termination Competition 2019 for C Integer programs show that MaxCore outperforms all other resource analysis tools.

Type
Original Article
Copyright
© Cambridge University Press 2019 

Access options

Get access to the full version of this content by using one of the access options below. (Log in options will check for institutional or personal access. Content may require purchase if you do not have access.)

Footnotes

*

This work was funded partially by the Spanish MICINN/FEDER, UE projects RTI2018-094403-BC31, RTI2018-094403-B-C33 and RTI2018-095609-B-I00, the MINECO project TIN2015-69175-C4-2-R, the MINECO/FEDER, UE projects TIN2015-69175-C4-3-R and TIN2015-66293-R, and by the CM project S2018/TCS-4314.

References

Albert, E., Arenas, P., Genaim, S., and Puebla, G. 2008. Automatic inference of upper bounds for recurrence relations in cost analysis. In Proc. of SAS 2008. LNCS, vol. 5079. Springer, 221237.Google Scholar
Albert, E., Arenas, P., Genaim, S., Puebla, G., and Zanardini, D. 2007. Cost Analysis of Java Bytecode. In Proc. of ESOP’07. LNCS, vol. 4421. Springer, 157172.Google Scholar
Albert, E., Correas, J., Ka I Pun, E. B. J., and Román-Díez, G. 2018. Parallel Cost Analysis. ACM Trans. Comput. Log. 19, 4, 137.Google Scholar
Borralleras, C., Brockschmidt, M., Larraz, D., Oliveras, A., Rodríguez-Carbonell, E., and Rubio, A. 2017. Proving termination through conditional termination. In Proc. TACAS 2017. LNCS, vol. 10205. Springer, 99117.Google Scholar
Brockschmidt, M., Emmes, F., Falke, S., Fuhs, C., and Giesl, J. 2016. Analyzing runtime and size complexity of integer programs. ACM Trans. Program. Lang. Syst. 38, 4, 13:113:50.Google Scholar
Cousot, P. and Halbwachs, N. 1978. Automatic discovery of linear restraints among variables of a program. In Proc. POPL 1978. ACM, 8496.Google Scholar
Debray, S. K. and Lin, N. 1993. Cost analysis of logic programs. ACM Trans. Program. Lang. Syst. 15, 5, 826875.Google Scholar
Debray, S. K., López-Garca, P., Hermenegildo, M. V., and Lin, N. 1994. Estimating the computational cost of logic programs. In Proc. SAS 1994. LNCS, vol. 864. Springer, 255265.Google Scholar
Flores-Montoya, A. 2017. Cost analysis of programs based on the refinement of cost relations. Ph.D. thesis, Darmstadt University of Technology, Germany.Google Scholar
Flores-Montoya, A. and Hähnle, R. 2014. Resource analysis of complex programs with cost equations. In Proc. APLAS 2014. LNCS, vol. 8858. Springer, 275295.Google Scholar
Garcia, A., Laneve, C., and Lienhardt, M. 2015. Static analysis of cloud elasticity. In Proc. PPDP 2015. ACM, 125136.Google Scholar
Giesl, J., Thiemann, R., Schneider-Kamp, P., and Falke, S. 2004. Automated termination proofs with aprove. In Proc. RTA 2004. LNCS, vol. 3091. Springer, Aachen, Germany, 210220.Google Scholar
Grech, N., Georgiou, K., Pallister, J., Kerrison, S., Morse, J., and Eder, K. 2015. Static analysis of energy consumption for LLVM IR programs. In Proc. SCOPES 2015. ACM, 1221.Google Scholar
Gulwani, S., Jain, S., and Koskinen, E. 2009. Control-flow refinement and progress invariants for bound analysis. In Proc. of PLDI 2009. ACM, 375385.Google Scholar
Kafle, B., Gallagher, J. P., Gange, G., Schachte, P., Søndergaard, H., and Stuckey, P. J. 2018. An iterative approach to precondition inference using constrained horn clauses. Theory Pract. Log. Program. 18, 3-4, 553570.Google Scholar
Liqat, U., Georgiou, K., Kerrison, S., López-Garca, P., Gallagher, J. P., Hermenegildo, M. V., and Eder, K. 2015. Inferring parametric energy consumption functions at different software levels: ISA vs. LLVM IR. In Proc. FOPARA 2015, Selected Papers. LNCS, vol. 9964. Springer, 81100.Google Scholar
Navas, J. A., Mera, E., López-Garca, P., and Hermenegildo, M. V. 2007. User-definable resource bounds analysis for logic programs. In Proc. ICLP 2007. LNCS, vol. 4670. Springer, 348363.Google Scholar
Serrano, A., López-Garca, P., Bueno, F., and Hermenegildo, M. V. 2013. Sized type analysis for logic programs. Theory Pract. Log. Program. 13, 4-5-Online-Supplement.Google Scholar
Sharma, R., Dillig, I., Dillig, T., and Aiken, A. 2011. Simplifying loop invariant generation using splitter predicates. In Proc. of CAV 2011. Springer, 703719.Google Scholar
Sinn, M., Zuleger, F., and Veith, H. 2014. A simple and scalable static analysis for bound analysis and amortized complexity analysis. In Proc. CAV 2014. LNCS, vol. 8559. Springer, 745761.Google Scholar
Spoto, F., Mesnard, F., and Payet, É. 2010. A termination analyzer for java bytecode based on path-length. ACM Trans. Program. Lang. Syst. 32, 3, 8:18:70.Google Scholar
Wegbreit, B. 1975. Mechanical Program Analysis. Communications ACM 18, 9, 528539.Google Scholar