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Gamboa de Carvalho, Fernando Miguel

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  • Deconstructing yield operator to enhance streams processing
    Publication . Poeira, Diogo; Carvalho, Fernando Miguel
    Customizing streams pipelines with new user-defined operations is a well-known pattern regarding streams processing. However, programming languages face two challenges when considering streams extensibility: 1) provide a compact and readable way to express new operations, and 2) keep streams’ laziness behavior. From here, we may find a consensus around the adoption of the generator operator, i.e. yield, as a means to fulfil both requirements, since most state-of-the-art programming languages provide this feature. Yet, what is the performance overhead of interleaving a yield-based operation in streams processing? In this work we present a benchmark based on realistic use cases of two different web APIs, namely: Last.fm and world weather on line, where custom yield-based operations may degrade the streams performance in twofold. We also propose a purely functional and minimalistic design, named tinyield, that can be easily adopted in any programming language and provides a concise way of chaining extension operations fluently, with low overhead in the eval uated benchmarks. The tinyield proposal was deployed in three different libraries, namely for Java (jayield), JavaScript (tinyield4ts) and .Net (tinyield4net).
  • Runtime elision of transactional barriers for captured memory
    Publication . Carvalho, Fernando Miguel; Cachopo, Joao
    In this paper, we propose a new technique that can identify transaction-local memory (i.e. captured memory), in managed environments, while having a low runtime overhead. We implemented our proposal in a well known STM framework (Deuce) and we tested it in STMBench7 with two different STMs: TL2 and LSA. In both STMs the performance improved significantly (4 times and 2.6 times, respectively). Moreover, running the STAMP benchmarks with our approach shows improvements of 7 times in the best case for the Vacation application.