Savant: Automatic generation of a parallel scheduling heuristic for map-reduce

Pinel, Frédéric; Dorronsoro, Bernabè

doi:10.3233/his-140200

Cited by 9 publications

(3 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Virtual Savant is a novel paradigm to automatically generate programs that solve optimization problems in a massively parallel fashion [11]. The paradigm is inspired by the savant syndrome, a rare condition in which a person with significant mental disabilities has certain abilities far in excess of what would be considered normal [5].…”

Section: Virtual Savantmentioning

confidence: 99%

See 1 more Smart Citation

Virtual Savant for the Knapsack Problem: learning for automatic resource allocation

Массобрио¹,

Díaz²,

Cánovas³

2019

Proceedings of ISP RAS

View full text Add to dashboard Cite

This article presents the application of Virtual Savant to solve resource allocation problems, a widelystudied area with several real-world applications. Virtual Savant is a novel soft computing method that uses machine learning techniques to compute solutions to a given optimization problem. Virtual Savant aims at learning how to solve a given problem from the solutions computed by a reference algorithm, and its design allows taking advantage of modern parallel computing infrastructures. The proposed approach is evaluated to solve the Knapsack Problem, which models different variant of resource allocation problems, considering a set of instances with varying size and difficulty. The experimental analysis is performed on an Intel Xeon Phi many-core server. Results indicate that Virtual Savant is able to compute accurate solutions while showing good scalability properties when increasing the number of computing resources used.

show abstract

Section: Virtual Savantmentioning

confidence: 99%

“…Improvements of 5% on average over the baseline results were obtained for the problem instances studied. Our previous works were able to obtain promising results when applying Virtual Savant to a task scheduling problem [17,18,7,11]. The application of Virtual Savant to the 0/1 Knapsack Problem has been previously studied in [8,9].…”

Section: Related Workmentioning

confidence: 99%

Virtual Savant for the Knapsack Problem: learning for automatic resource allocation

Массобрио¹,

Díaz²,

Cánovas³

2019

Proceedings of ISP RAS

View full text Add to dashboard Cite

show abstract

“…al. [80], [27] investigates using machine learning to automatically generate desired solutions for a set of problem instances and solve for new problems in a massively parallel manner.…”

Section: Machine Learning For Systemsmentioning

confidence: 99%

Improving heterogeneous system efficiency : architecture, scheduling, and machine learning

Nemirovsky

View full text Add to dashboard Cite

Computer architects are beginning to embrace heterogeneous systems as an effective method to utilize increases in transistor densities for executing a diverse range of workloads under varying performance and energy constraints. As heterogeneous systems become more ubiquitous, architects will need to develop novel CPU scheduling techniques capable of exploiting the diversity of computational resources. In recognizing hardware diversity, state-of-the-art heterogeneous schedulers are able to produce significant performance improvements over their predecessors and enable more flexible system designs. Nearly all of these, however, are unable to efficiently identify the mapping schemes which will result in the highest system performance. Accurately estimating the performance of applications on different heterogeneous resources can provide a significant advantage to heterogeneous schedulers for identifying a performance maximizing mapping scheme to improve system performance. Recent advances in machine learning techniques including artificial neural networks have led to the development of powerful and practical prediction models for a variety of fields. As of yet, however, no significant leaps have been taken towards employing machine learning for heterogeneous scheduling in order to maximize system throughput. The core issue we approach is how to understand and utilize the rise of heterogeneous architectures, benefits of heterogeneous scheduling, and the promise of machine learning techniques with respect to maximizing system performance. We present studies that promote a future computing model capable of supporting massive hardware diversity, discuss the constraints faced by heterogeneous designers, explore the advantages and shortcomings of conventional heterogeneous schedulers, and pioneer applying machine learning to optimize mapping and system throughput. The goal of this thesis is to highlight the importance of efficiently exploiting heterogeneity and to validate the opportunities that machine learning can offer for various areas in computer architecture. Arquitectos de computadores estan empesando a diseñar systemas heterogeneos como una manera efficiente de usar los incrementos en densidades de transistors para ejecutar una gran diversidad de programas corriendo debajo de differentes condiciones y requisitos de energia y rendimiento (performance). En cuanto los sistemas heterogeneos van ganando popularidad de uso, arquitectos van a necesitar a diseñar nuevas formas de hacer el scheduling de las applicaciones en los cores distintos de los CPUs. Schedulers nuevos que tienen en cuenta la heterogeniedad de los recursos en el hardware logran importantes beneficios en terminos de rendimiento en comparacion con schedulers hecho para sistemas homogenios. Pero, casi todos de estos schedulers heterogeneos no son capaz de poder identificar la esquema de mapping que produce el rendimiento maximo dado el estado de los cores y las applicaciones. Estimando con precision el rendimiento de los programas ejecutando sobre diferentes cores de un CPU es un a gran ventaja para poder identificar el mapping para lograr el mejor rendimiento posible para el proximo scheduling quantum. Desarollos nuevos en la area de machine learning, como redes neurales, han producido predictores muy potentes y con gran precision in disciplinas numerosas. Pero en estos momentos, la aplicacion de metodos de machine learning no se han casi explorados para poder mejorar la eficiencia de los CPUs y menos para mejorar los schedulers para sistemas heterogeneos. El tema de enfoque en esta tesis es como poder entender y utilizar los sistemas heterogeneos, los beneficios de scheduling para estos sistemas, y como aprovechar las promesas de los metodos de machine learning con respeto a maximizer el redimiento de el Sistema. Presentamos estudios que dan una esquema para un modelo de computacion para el futuro capaz de dar suporte a recursos heterogeneos en gran escala, discutimos las restricciones enfrentados por diseñadores de sistemas heterogeneos, exploramos las ventajas y desventajas de las ultimas schedulers heterogeneos, y abrimos el camino de usar metodos de machine learning para optimizer el mapping y rendimiento de un sistema heterogeneo. El objetivo de esta tesis es destacar la imporancia de explotando eficientemente la heterogenidad de los recursos y tambien validar las oportunidades para mejorar la eficiencia en diferente areas de arquitectura de computadoras que pueden ser realizadas gracias a machine learning.

show abstract