Person:
Sáez Alcaide, Juan Carlos

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First Name
Juan Carlos
Last Name
Sáez Alcaide
URI
https://hdl.handle.net/20.500.14352/78726
Affiliation
Universidad Complutense de Madrid
Faculty / Institute
Informática
Department
Arquitectura de Computadores y Automática
Area
Arquitectura y Tecnología de Computadores
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Start date: 2020 × Item Type: Publication ×

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    LFOC+: A Fair OS-Level Cache-Clustering Policy for Commodity Multicore Systems
    (IEEE Transactions on Computers, 2022) Sáez Alcaide, Juan Carlos; Castro Rodríguez, Fernando; Fanizzi, Graziano; Prieto Matías, Manuel
    Commodity multicore systems are increasingly adopting hardware support that enables the system software to partition the last-level cache (LLC). This support makes it possible for the operating system (OS) or the Virtual Machine Monitor (VMM) to mitigate shared-resource contention effects on multicores by assigning different co-running applications to various cache partitions. Recently cache-clustering (or partition-sharing) strategies have emerged as a way to improve system throughput and fairness on new platforms with cache-partitioning support. As opposed to strict cache-partitioning, which allocates separate cache partitions to each application, cache-clustering allows partitions to be shared by a group of applications. In this article we propose LFOC+, a fairness-aware OS-level cache-clustering policy for commodity multicore systems. LFOC+ tries to mimic the behavior of the optimal cache-clustering solution for fairness, which we could obtain for different workload scenarios by using a simulation tool. Our dynamic cache-clustering strategy continuously gathers data fromperformancemonitoring counters to classify applications at runtime based on the degree of cache sensitivity and contentiousness, and effectively separates cache-sensitive applications fromaggressor programs to improve fairness,while providing acceptable system throughput.We implemented LFOC+ in the Linux kernel and evaluated it on a real systemfeaturing an Intel Skylake processor, wherewe compare its effectiveness to that of four previously proposed cache-clustering policies. Our experimental análisis reveals that LFOC+ constitutes a lightweight OS-level policy and improves fairness relative to two other state-of-the-art fairness-aware strategies –Dunn and LFOC–, by up to 22% and up to 20.6%, respectively, and by9% and 4.9%on average.
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    Enabling performance portability of data-parallel OpenMP applications on asymmetric multicore processors
    (2020) Sáez Alcaide, Juan Carlos; Castro Rodríguez, Fernando; Prieto Matías, Manuel
    Asymmetric multicore processors (AMPs) couple high-performance big cores and low-power small cores with the same instruction-set architecture but different features, such as clock frequency or microarchitecture. Previous work has shown that asymmetric designs may deliver higher energy efficiency than symmetric multicores for diverse workloads. Despite their benefits, AMPs pose significant challenges to runtime systems of parallel programming models. While previous work has mainly explored how to efficiently execute task-based parallel applications on AMPs, via enhancements in the runtime system, improving the performance of unmodified data-parallel applications on these architectures is still a big challenge. In this work we analyze the particular case of loop-based OpenMP applications, which are widely used today in scientific and engineering domains, and constitute the dominant application type in many parallel benchmark suites used for performance evaluation on multicore systems. We observed that conventional loop-scheduling OpenMP approaches are unable to efficiently cope with the load imbalance that naturally stems from the different performance delivered by big and small cores. To address this shortcoming, we propose Asymmetric Iteration Distribution (AID), a set of novel loop-scheduling methods for AMPs that distribute iterations unevenly across worker threads to efficiently deal with performance asymmetry. We implemented AID in libgomp –the GNU OpenMP runtime system–, and evaluated it on two different asymmetric multicore platforms. Our analysis reveals that the AID methods constitute effective replacements of the static and dynamic methods on AMPs, and are capable of improving performance over these conventional strategies by up to 56% and 16.8%, respectively.