You are provided with the abstract of a scientific paper and the abstracts of the papers it references. Your task is to write a Related Work section for the main paper. This section should concisely summarize and integrate the key points from the cited abstracts, reflecting how they relate to the main paper's contributions and context. In your summary, use numerical citation markers like [1], [2], etc., to refer to the corresponding papers.

Main Abstract:
Multi-core architectures can be leveraged to allow independent processes to run in parallel. However, due to resources shared across cores, such as caches, distinct processes may interfere with one another, e.g. affecting execution time. Analysing the extent of this interference is difficult due to: (1) the diversity of modern architectures, which may contain different implementations of shared resources, and (2) the complex nature of modern processors, in which interference might arise due to subtle interactions. To address this, we propose a black-box auto-tuning approach that searches for processes that are effective at causing slowdowns for a program when executed in parallel. Such slowdowns provide lower bounds on worst-case execution time; an important metric in systems with real-time constraints. Our approach considers a set of parameterised "enemy" processes and "victim" programs, each targeting a shared resource. The autotuner searches for enemy process parameters that are effective at causing slowdowns in the victim programs. The idea is that victim programs behave as a proxy for shared resource usage of arbitrary programs. We evaluate our approach on: 5 different chips; 3 resources (cache, memory bus, and main memory); and consider several search strategies and slowdown metrics. Using enemy processes tuned per chip, we evaluate the slowdowns on the autobench and coremark benchmark suites and show that our method is able to achieve slowdowns in 98 of benchmark chip combinations and provide similar results to manually written enemy processes.

Cited Abstract(s):
[1]: The development of real-time systems is an essential industrial activity whose importance is increasing. The most important analytical method to assure the quality of real-time systems is dynamic testing. Testing is the only method which examines the actual run-time behaviour of real-time software, based on an execution in the real application environment. Dynamic aspects like the duration of computations, the memory actually needed, or the synchronization of parallel processes are of major importance for the correct function of real-time systems and have to be tested. A comprehensive investigation of existing software test methods shows that they mostly concentrate on testing for functional correctness. They are not suited for an examination of temporal correctness which is essential to real-time systems. Very small systems show a wide range of different execution times. Therefore, existing test procedures must be supplemented by new methods, which concentrate on determining whether the system violates its specified timing constraints. In general, this means that outputs are produced too early or their computation takes too long. The task of the tester is to find the inputs with the longest or shortest execution times to check whether they produce a temporal error. If the search for such inputs is interpreted as a problem of optimization, genetic algorithms can be used to find the inputs with the longest or shortest execution times automatically. The fitness function is the execution time measured in processor cycles. Experiments using genetic algorithms on a number of programs with up to 1511 LOC and 843 integer input parameters have successfully identified new longer and shorter paths than had been found using random testing or systematic testing. Genetic algorithms are able therefore to check large programs and they show considerable promise in establishing the validity of the temporal behaviour of real-time software.

[2]: 

[3]: Establishing Worst Case Execution Times (WCET) using Measurement-Based Timing Analysis (MBTA) is only effective if we have reasonable confidence that we have fed the worst case execution trace into the analysis. Therefore for certification, the quality of these traces is of paramount importance. This paper aims to investigate how search algorithms can be used to automatically, and reliably, generate test cases so that appropriate execution traces are available to support MBTA. The work carried out in this paper uses a standard search algorithm and created a number of fitness functions to target the generation of 'good data'. The results are then input into a commercial measurement-based WCET analysis tool. The new fitness functions focus on achieving a combination of full branch coverage and a high number of loop counts, or partial path coverage, however are shown to achieve reliable approximations of the WCET particularly when combined with an MBTA tool. The code items used for the analysis included off the shelf benchmarks, as well as industrial safety-critical aircraft engine control software.
Tuning strategies have been used to optimise different computational aspects, with [2] showing how such an approach can be used for a variety of domain-specific issues. [1] use genetic algorithms to find the inputs that cause the longest or shortest execution time. To do so, they formulate the search for such inputs as an optimisation problem. [3] use simulated annealing on a single core processor to maximise code coverage and therefore obtain an estimate of the WCET.