Valgrind

Valgrind is an instrumentation framework for building dynamic analysis tools. There are Valgrind tools that can automatically detect many memory management and threading bugs, and profile your programs in detail. You can also use Valgrind to build new tools. The Valgrind distribution currently includes six production-quality tools: a memory error detector, two thread error detectors, a cache and branch-prediction profiler, a call-graph generating cache and branch-prediction profiler, and a heap profiler. It also includes three experimental tools: a heap/stack/global array overrun detector, a second heap profiler that examines how heap blocks are used, and a SimPoint basic block vector generator. It runs on the following platforms: X86/Linux, AMD64/Linux, ARM/Linux, PPC32/Linux, PPC64/Linux, S390X/Linux, ARM/Android (2.3.x), X86/Darwin and AMD64/Darwin (Mac OS X 10.6 and 10.7).


References in zbMATH (referenced in 55 articles )

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  1. Alpirez Bock, Estuardo; Bos, Joppe W.; Brzuska, Chris; Hubain, Charles; Michiels, Wil; Mune, Cristofaro; Sanfelix Gonzalez, Eloi; Teuwen, Philippe; Treff, Alexander: White-box cryptography: don’t forget about grey-box attacks (2019)
  2. Andrei V. Novikov: PyClustering: Data Mining Library (2019) not zbMATH
  3. Potter, Samuel F.; Cameron, Maria K.: Ordered line integral methods for solving the eikonal equation (2019)
  4. Valentin Niess, Anne Barnoud, Cristina Cârloganu, Olivier Martineau-Huynh: TURTLE: A C library for an optimistic stepping through a topography (2019) arXiv
  5. Banović, Mladen; Mykhaskiv, Orest; Auriemma, Salvatore; Walther, Andrea; Legrand, Herve; Müller, Jens-Dominik: Algorithmic differentiation of the Open CASCADE technology CAD kernel and its coupling with an adjoint CFD solver (2018)
  6. Cosme, Iria C. S.; Fernandes, Isaac F.; de Carvalho, João L.; Xavier-de-Souza, Samuel: Memory-usage advantageous block recursive matrix inverse (2018)
  7. Feyzi, Farid; Parsa, Saeed: A program slicing-based method for effective detection of coincidentally correct test cases (2018)
  8. Gonzaga de Oliveira, Sanderson L.; Nogueira, Jéssica Renata: An evaluation of point-insertion sequences for incremental Delaunay tessellations (2018)
  9. Hück, Alexander; Bischof, Christian; Sagebaum, Max; Gauger, Nicolas R.; Jurgelucks, Benjamin; Larour, Eric; Perez, Gilberto: A usability case study of algorithmic differentiation tools on the ISSM ice sheet model (2018)
  10. Jordan, Charles; Joswig, Michael; Kastner, Lars: Parallel enumeration of triangulations (2018)
  11. Jurgelucks, Benjamin; Claes, Leander; Walther, Andrea; Henning, Bernd: Optimization of triple-ring electrodes on piezoceramic transducers using algorithmic differentiation (2018)
  12. Dowsley, Rafael; Michalas, Antonis; Nagel, Matthias; Paladi, Nicolae: A survey on design and implementation of protected searchable data in the cloud (2017)
  13. Gagie, Travis; Manzini, Giovanni; Sirén, Jouni: Wheeler graphs: a framework for BWT-based data structures (2017)
  14. Harshit Gupta, Amir Vahid Dastjerdi, Soumya K. Ghosh, Rajkumar Buyya: iFogSim: A Toolkit for Modeling and Simulation of Resource Management Techniques in Internet of Things, Edge and Fog Computing Environments (2016) arXiv
  15. Thomas Keck: FastBDT: A speed-optimized and cache-friendly implementation of stochastic gradient-boosted decision trees for multivariate classification (2016) arXiv
  16. Wang, Mu; Gebremedhin, Assefaw; Pothen, Alex: Capitalizing on \textitlivevariables: new algorithms for efficient Hessian computation via automatic differentiation (2016)
  17. Mushtaq, Hamid; Al-Ars, Zaid; Bertels, Koen: Efficent and highly portable deterministic multithreading (DetLock) (2014) ioport
  18. Ribeiro, Pedro; Silva, Fernando: G-Tries: a data structure for storing and finding subgraphs (2014)
  19. Romano, Anthony: Practical floating-point tests with integer code (2014) ioport
  20. Ye, John; Yan, Hui; Hou, Honglun; Chen, Tianzhou: Potential thread-level-parallelism exploration with superblock reordering (2014) ioport

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