Tasa

Kosmidis, Leonidas; Vargas, Roberto; Morales, David; Quiñones, Eduardo; Abella, Jaume; Cazorla, Francisco J.

doi:10.1145/2966986.2967078

Cited by 12 publications

(2 citation statements)

References 20 publications

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“…CIL is capable of parsing any C code, including gcc extensions, which makes it capable of working even with the Linux kernel, which is likely the most complex and largest C code base. This makes our TASA reimplementation, which we have open sourced at [19] to achieve the highest TRL on CPU. CIL provides features which are useful for the application of TASA such as inclusion of function prototypes as well as a linker at source code level, which allows to merge all application's source code files in a single source code file which can be randomised with TASA.…”

Section: Use Case Descriptionmentioning

confidence: 99%

Worst Case Execution Time and Power Estimation of Multicore and GPU Software: A Pedestrian Detection Use Case

Rodriguez Ferrandez,

Jover Alvarez,

Maria Trompouki

et al. 2023

Ada Lett.

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Worst Case Execution Time estimation of software running on parallel platforms is a challenging task, due to resource interference of other tasks and the complexity of the underlying CPU and GPU hardware architectures. Similarly, the increased complexity of the hardware, challenges the estimation of worst case power consumption. In this paper, we employ Measurement Based Probabilistic Timing Analysis (MBPTA), which is capable of managing complex architectures such as multicores. We enable its use by software randomisation, which we show for the first time that is also possible on GPUs. We demonstrate our method on a pedestrian detection use case on an embedded multicore and GPU platform for the automotive domain, the NVIDIA Xavier. Moreover, we extend our measurement based probabilistic method in order to predict the worst case power consumption of the software on the same platform.

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Section: Use Case Descriptionmentioning

confidence: 99%

Worst Case Execution Time and Power Estimation of Multicore and GPU Software: A Pedestrian Detection Use Case

Rodriguez Ferrandez,

Jover Alvarez,

Maria Trompouki

et al. 2023

Ada Lett.

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“…Several binary files are produced for the same program, in which memory objects are created with offsets to achieve random effects. [Kosmidis et al 2016] develop a software tool to modify source code of the program, which realizes randomization without modifying existing toolchains.…”

Section: Related Workmentioning

confidence: 99%

An Adaptive Markov Model for the Timing Analysis of Probabilistic Caches

Chen

Beltrame

2017

ACM Trans. Des. Autom. Electron. Syst.

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Accurate timing prediction for real-time embedded software execution is becoming a problem due to the increasing complexity of computer architecture, and the presence of mixed-criticality workloads. Probabilistic caches were proposed to set bounds to Worst Case Execution Time (WCET) estimates and help designers improve real-time embedded system resource use. Static Probabilistic Timing Analysis (SPTA) for probabilistic caches is nevertheless difficult to perform, because cache accesses depend on execution history, and the computational complexity of SPTA makes it intractable for calculation as the number of accesses increases. In this paper, we explore and improve SPTA for caches with evict-on-miss random replacement policy using a state space modeling technique. A nonhomogeneous Markov model is employed for single-path programs in discrete-time finite state space representation. To make this Markov model tractable, we limit the number of states and use an adaptive method for state modification. Experiments show that compared to the state-of-the-art methodology, the proposed adaptive Markov chain approach provides better results at the occurrence probability of 10 −15 : in terms of accuracy, the state-of-the-art SPTA results are more conservative, by 11% more on average. In terms of computation time, our approach is not significantly different from the state-of-the-art SPTA.

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MC2: Multicore and Cache Analysis via Deterministic and Probabilistic Jitter Bounding

Diaz

Fernandez

Kosmidis

et al. 2017

Reliable Software Technologies – Ada-Europe 2017

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Abstract. In critical domains, reliable software execution is increasingly involving aspects related to the timing dimension. This is due to the advent of high-performance (complex) hardware, used to provide the rising levels of guaranteed performance needed in those domains. Caches and multicores are two of the hardware features that have the potential to significantly reduce WCET estimates, yet they pose new challenges on current-practice measurement-based timing analysis (MBTA) approaches. In this paper we propose MC2, a technique for multilevel-cache multicores that combines deterministic and probabilistic jitter-bounding approaches to reliably handle both the variability in execution time generated by caches and the contention in accessing shared hardware resources. We evaluate MC2 on a COTS quad-core LEON-based board and our initial results show how it effectively captures cache and multicore contention in pWCET estimates with respect to actual observed values.

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Tasa

Cited by 12 publications

References 20 publications

Worst Case Execution Time and Power Estimation of Multicore and GPU Software: A Pedestrian Detection Use Case

Worst Case Execution Time and Power Estimation of Multicore and GPU Software: A Pedestrian Detection Use Case

An Adaptive Markov Model for the Timing Analysis of Probabilistic Caches

MC2: Multicore and Cache Analysis via Deterministic and Probabilistic Jitter Bounding

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