Olcoz Herrero, Katzalin

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First Name
Last Name
Olcoz Herrero
Universidad Complutense de Madrid
Faculty / Institute
Ciencias Físicas
Arquitectura de Computadores y Automática
Arquitectura y Tecnología de Computadores
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Now showing 1 - 3 of 3
  • Publication
    Gem5-x: a gem5-based system level simulation framework to optimize many-core platforms
    (IEEE, 2019) Mahmood Qureshi, Yasir; Simon, William Andrew; Zapater, Marina; Atienza, David; Olcoz Herrero, Katzalin
    The rapid expansion of online-based services requires novel energy and performance efficient architectures to meet power and latency constraints. Fast architectural exploration has become a key enabler in the proposal of architectural innovation. In this paper, we present gem5-X, a gem5-based system level simulation framework, and a methodology to optimize many-core systems for performance and power. As real-life case studies of many-core server workloads, we use real-time video transcoding and image classification using convolutional neural networks (CNNs). Gem5-X allows us to identify bottlenecks and evaluate the potential benefits of architectural extensions such as in-cache computing and 3D stacked High Bandwidth Memory. For real-time video transcoding, we achieve 15% speed-up using in-order cores with in-cache computing when compared to a baseline in-order system and 76% energy savings when compared to an Out-of-Order system. When using HBM, we further accelerate real-time transcoding and CNNs by up to 7% and 8% respectively.
  • Publication
    A machine learning-based framework for throughput estimation of time-varying applications in multi-core servers
    (IEEE, 2019) Iranfar, Arman; Souza, Wellington Silva de; Zapater, Marina; Olcoz Herrero, Katzalin; Souza, Samuel Xavier de; Atienza, David
    Accurate workload prediction and throughput estimation are keys in efficient proactive power and performance management of multi-core platforms. Although hardware performance counters available on modern platforms contain important information about the application behavior, employing them efficiently is not straightforward when dealing with time-varying applications even if they have iterative structures. In this work, we propose a machine learning-based framework for workload prediction and throughput estimation using hardware events. Our framework enables throughput estimation over various available system configurations, namely, number of parallel threads and operating frequency. In particular, we first employ workload clustering and classification techniques along with Markov chains to predict the next workload for each available system configuration. Then, the predicted workload is used to estimate the next expected throughput through a machine learning-based regression model. The comparison with state of the art demonstrates that our framework is able to improve Quality of Service (QoS) by 3.4x, while consuming 15% less power thanks to the more accurate throughput estimation.
  • Publication
    A QoS and container-based approach for energy saving and performance profiling in multi-core servers
    (IEEE, 2019) Souza, Wellington Silva de; Iranfar, Arman; Silva, Anderson; Zapater, Marina; Souza, Samuel Xavier de; Olcoz Herrero, Katzalin; Atienza, David
    In this work we present ContainEnergy, a new performance evaluation and profiling tool that uses software containers to perform application runtime assessment, providing energy and performance profiling data. It is focused on energy efficiency for next generation workloads and IT infrastructure.