Enhancing performance prediction robustness by combining analytical modeling and machine learning
Classical approaches to performance prediction rely on two, typically antithetic, techniques:
Machine Learning (ML) and Analytical Modeling (AM). ML takes a black box approach …
Machine Learning (ML) and Analytical Modeling (AM). ML takes a black box approach …
Hardware transactional memory meets memory persistency
Abstract Persistent Memory (PM) and Hardware Transactional Memory (HTM) are two recent
architectural developments whose joint usage promises to drastically accelerate the …
architectural developments whose joint usage promises to drastically accelerate the …
Analysis, classification and comparison of scheduling techniques for software transactional memories
P Di Sanzo - IEEE Transactions on Parallel and Distributed …, 2017 - ieeexplore.ieee.org
Transactional Memory (TM) is a practical programming paradigm for developing concurrent
applications. Performance is a critical factor for TM implementations, and various studies …
applications. Performance is a critical factor for TM implementations, and various studies …
A hybrid machine learning approach for performance modeling of cloud-based big data applications
E Ataie, A Evangelinou, E Gianniti… - The Computer …, 2022 - academic.oup.com
Abstract Nowadays, Apache Hadoop and Apache Spark are two of the most prominent
distributed solutions for processing big data applications on the market. Since in many cases …
distributed solutions for processing big data applications on the market. Since in many cases …
Hybrid machine learning/analytical models for performance prediction: A tutorial
Classical approaches to performance prediction of computer systems rely on two, typically
antithetic, techniques: Machine Learning (ML) and Analytical Modeling (AM). ML undertakes …
antithetic, techniques: Machine Learning (ML) and Analytical Modeling (AM). ML undertakes …
A combined analytical modeling machine learning approach for performance prediction of MapReduce jobs in cloud environment
Nowadays MapReduce and its open source implementation, Apache Hadoop, are the most
widespread solutions for handling massive dataset on clusters of commodity hardware. At …
widespread solutions for handling massive dataset on clusters of commodity hardware. At …
On bootstrapping machine learning performance predictors via analytical models
Performance modeling typically relies on two antithetic methodologies: white box models,
which exploit knowledge on system's internals and capture its dynamics using analytical …
which exploit knowledge on system's internals and capture its dynamics using analytical …
Hybrid swarm intelligent parallel algorithm research based on multi-core clusters
W Li, Y Bi, X Zhu, C Yuan, X Zhang - Microprocessors and Microsystems, 2016 - Elsevier
In order to solve poor fine searching capacity of artificial fish swarm algorithm and artificial
bee colony swarm algorithm in late state to result in insufficient local optimization, hybrid …
bee colony swarm algorithm in late state to result in insufficient local optimization, hybrid …
Adaptive model-based scheduling in software transactional memory
P Di Sanzo, A Pellegrini, M Sannicandro… - IEEE Transactions …, 2019 - ieeexplore.ieee.org
Software Transactional Memory (STM) stands as powerful concurrent programming
paradigm, enabling atomicity, and isolation while accessing shared data. On the downside …
paradigm, enabling atomicity, and isolation while accessing shared data. On the downside …
Automatic tuning of the parallelism degree in hardware transactional memory
Transactional Memory (TM) is an emerging paradigm that promises to ease the
development of parallel applications. Due to its inherently speculative nature, however, TM …
development of parallel applications. Due to its inherently speculative nature, however, TM …