Page size aware cache prefetching

The increase in working set sizes of contemporary applications outpaces the growth in cache sizes, resulting in frequent main memory accesses that deteriorate system per- formance due to the disparity between processor and memory speeds. Prefetching data blocks into the cache hierarchy ahead of demand accesses has proven successful at attenuating this bottleneck. However, spatial cache prefetchers operating in the physical address space leave significant performance on the table by limiting their pattern detection within 4KB physical page boundaries when modern systems use page sizes larger than 4KB to mitigate the address translation overheads. This paper exploits the high usage of large pages in modern systems to increase the effectiveness of spatial cache prefetch- ing. We design and propose the Page-size Propagation Module (PPM), a µarchitectural scheme that propagates the page size information to the lower-level cache prefetchers, enabling safe prefetching beyond 4KB physical page boundaries when the accessed blocks reside in large pages, at the cost of augmenting the first-level caches’ Miss Status Holding Register (MSHR) entries with one additional bit. PPM is compatible with any cache prefetcher without implying design modifications. We capitalize on PPM’s benefits by designing a module that consists of two page size aware prefetchers that inherently use different page sizes to drive prefetching. The composite module uses adaptive logic to dynamically enable the most appropriate page size aware prefetcher. Finally, we show that the proposed designs are transparent to which cache prefetcher is used. We apply the proposed page size exploitation techniques to four state-of-the-art spatial cache prefetchers. Our evalua- tion shows that our proposals improve single-core geomean performance by up to 8.1% (2.1% at minimum) over the original implementation of the considered prefetchers, across 80 memory-intensive workloads. In multi-core contexts, we report geomean speedups up to 7.7% across different cache prefetchers and core configurations.This work is supported by the Spanish Ministry of Science and Technology through the PID2019-107255GB project, the Generalitat de Catalunya (contract 2017-SGR-1414), the European Union Horizon 2020 research and innovation program under grant agreement No 955606 (DEEP-SEA EU project), the National Science Foundation through grants CNS-1938064 and CCF-1912617, and the Semiconductor Research Corporation project GRC 2936.001. Georgios Vavouliotis has been supported by the Spanish Ministry of Economy, Industry, and Competitiveness and the European Social Fund under the FPI fellowship No. PRE2018-087046. Marc Casas has been partially supported by the Grant RYC2017-23269 funded by MCIN/AEI/10.13039/501100011033 and ESF ‘Investing in your future’.Peer ReviewedPostprint (author's final draft

Active strategies for coordination of solitary robots

Thesis (PhD)--Stellenbosch University, 2020.ENGLISH ABSTRACT: This thesis considers the problem of search of an unknown environment by multiple solitary robots: self-interested robots without prior knowledge about each other, and with restricted perception and communication capacity. When solitary robots accidentally interact with each other, they can leverage each other’s information to work more effectively. In this thesis, we consider three problems related to the treatment of solitary robots: coordination, construction of a view of the network formed when robots interact, and classifier fusion. Coordination is the key focus for search and rescue. The other two problems are related areas inspired by the problems we encountered while developing our coordination method. We propose a coordination strategy based on cellular decomposition of the search environment, which provides sustainable performance when a known available search time (bound) is insufficient to cover the entire search environment. A sustainable performance is achieved when robots that know about each other explore non-overlapping regions. For network construction, we propose modifications to a scalable decentralised method for constructing a model of network topology which reduces the number of messages exchanged between interacting nodes. The method has wider potential application than mobile robotics. For classifier fusion, we propose an iterative method where outputs of classifiers are combined without using any further information about the behaviour of the individual classifiers. Our approaches for each of these problems are compared to state-of-the-art methods.AFRIKAANSE OPSOMMING: Hierdie tesis beskou die probleem van soektog in ’n onbekende omgewing deur ’n aantal alleenstaande robotte: selfbelangstellende robotte sonder voorafgaande kennis van mekaar, en met beperkte persepsie- en kommunikasievermoëns. Wanneer alleenstaande robotte toevallig mekaar raakloop, kan hulle met mekaar inligting uitruil om meer effektief te werk. Hierdie tesis beskou drie probleme wat verband hou met die hantering van alleenstaande robotte: konstruksie van ’n blik van die netwerk gevorm deur interaksie tussen robotte, koördinasie en klassifiseerdersamesmelting. Koördinasie is die hoof fokuspunt vir soek en redding. Die ander twee probleme is uit verwante areas, gemotiveer deur uitdagings wat ons ervaar het tydens die ontwikkeling van ons koördineringsmetode. Ons stel ’n skaleerbare desentraliseerde metode voor om ’n model van netwerktopologie te bou wat minder boodskappe tussen wisselwerkende nodusse hoet te verruil. Die metode het wyer potensiële toepassings as mobiele robotika. Vir koördinasie, stel ons ’n strategie voor gebaseer op sellulêre ontbinding van die soekomgewing, wat volhoubare prestasie toon wanneer ’n bekende soektyd onvoldoende is om die hele soekomgewing te dek. Vir klassifiseerdersamesmelting, stel ons ’n iteratiewe metode voor, waar klassifiseerders se voorspellings gekombineer word sonder om enige verdere inligting oor die gedrag van die individuele klassifiseerders te gebruik. Ons benaderings vir elkeen van hierdie probleme word vergelyk met stand-van-die-kuns metodes.The financial assistance of the African Institute for Mathematical Sciences (AIMS) and CSIR-SU Centre for Artificial Intelligence Research Group (CSIR-SU CAIR) towards this research is hereby acknowledged. Opinions expressed and conclusions arrived at, are those of the author and are not necessarily to be attributed to the AIMS and CSIR-SU CAIR.Doctora

Measuring the impact of COVID-19 on hospital care pathways

Care pathways in hospitals around the world reported significant disruption during the recent COVID-19 pandemic but measuring the actual impact is more problematic. Process mining can be useful for hospital management to measure the conformance of real-life care to what might be considered normal operations. In this study, we aim to demonstrate that process mining can be used to investigate process changes associated with complex disruptive events. We studied perturbations to accident and emergency (A &E) and maternity pathways in a UK public hospital during the COVID-19 pandemic. Co-incidentally the hospital had implemented a Command Centre approach for patient-flow management affording an opportunity to study both the planned improvement and the disruption due to the pandemic. Our study proposes and demonstrates a method for measuring and investigating the impact of such planned and unplanned disruptions affecting hospital care pathways. We found that during the pandemic, both A &E and maternity pathways had measurable reductions in the mean length of stay and a measurable drop in the percentage of pathways conforming to normative models. There were no distinctive patterns of monthly mean values of length of stay nor conformance throughout the phases of the installation of the hospital’s new Command Centre approach. Due to a deficit in the available A &E data, the findings for A &E pathways could not be interpreted