Integrated CPU and l2 cache voltage scaling using machine learning

2007 ◽  
Vol 42 (7) ◽  
pp. 41-50 ◽  
Author(s):  
Nevine AbouGhazaleh ◽  
Alexandre Ferreira ◽  
Cosmin Rusu ◽  
Ruibin Xu ◽  
Frank Liberato ◽  
...  
Keyword(s):  
Machine Learning ◽  
Voltage Scaling ◽  
L2 Cache

Integrated CPU and l2 cache voltage scaling using machine learning

2007 ◽  
Author(s):  
Nevine AbouGhazaleh ◽  
Alexandre Ferreira ◽  
Cosmin Rusu ◽  
Ruibin Xu ◽  
Frank Liberato ◽  
...  
Keyword(s):  
Machine Learning ◽  
Voltage Scaling ◽  
L2 Cache

A Machine Learning Based Meta-Scheduler for Multi-Core Processors

2012 ◽  
pp. 226-238 ◽  
Author(s):  
Jitendra Kumar Rai ◽  
Atul Negi ◽  
Rajeev Wankar ◽  
K. D. Nayak
Keyword(s):  
Machine Learning ◽  
Operating System ◽  
Linux Kernel ◽  
Process Scheduling ◽  
Performance Bottlenecks ◽  
System Process ◽  
L2 Cache ◽  
Overall Performance ◽  
Intelligent Scheduling ◽  
Fair Scheduler

Sharing resources such as caches and memory buses between the cores of multi-core processors may cause performance bottlenecks for running programs. In this paper, the authors describe a meta-scheduler, which adapts the process scheduling decisions for reducing the contention for shared L2 caches on multi-core processors. The meta-scheduler takes into account the multi-core topology as well as the L2 cache related characteristics of the processes. Using the model generated by the process of machine learning, it predicts the L2 cache behavior, i.e., solo-run-L2-cache-stress, of the programs. It runs in user mode and guides the underlying operating system process scheduler in intelligent scheduling of processes to reduce the contention of shared L2 caches. In these experiments, the authors observed up to 12 percent speedup in individual as well as overall performance, while using meta-scheduler as compared to default process scheduler (Completely Fair Scheduler) of Linux kernel.

A Machine Learning Based Meta-Scheduler for Multi-Core Processors

2010 ◽  
Vol 1 (4) ◽  
pp. 46-59 ◽  
Author(s):  
Jitendra Kumar Rai ◽  
Atul Negi ◽  
Rajeev Wankar ◽  
K. D. Nayak
Keyword(s):  
Machine Learning ◽  
Operating System ◽  
Linux Kernel ◽  
Process Scheduling ◽  
Performance Bottlenecks ◽  
System Process ◽  
L2 Cache ◽  
Overall Performance ◽  
Intelligent Scheduling ◽  
Fair Scheduler

Sharing resources such as caches and memory buses between the cores of multi-core processors may cause performance bottlenecks for running programs. In this paper, the authors describe a meta-scheduler, which adapts the process scheduling decisions for reducing the contention for shared L2 caches on multi-core processors. The meta-scheduler takes into account the multi-core topology as well as the L2 cache related characteristics of the processes. Using the model generated by the process of machine learning, it predicts the L2 cache behavior, i.e., solo-run-L2-cache-stress, of the programs. It runs in user mode and guides the underlying operating system process scheduler in intelligent scheduling of processes to reduce the contention of shared L2 caches. In these experiments, the authors observed up to 12 percent speedup in individual as well as overall performance, while using meta-scheduler as compared to default process scheduler (Completely Fair Scheduler) of Linux kernel.

A Machine Learning Based Meta-Scheduler for Multi-Core Processors

2012 ◽  
pp. 522-534
Author(s):  
Jitendra Kumar Rai ◽  
Atul Negi ◽  
Rajeev Wankar ◽  
K. D. Nayak
Keyword(s):  
Machine Learning ◽  
Operating System ◽  
Linux Kernel ◽  
Process Scheduling ◽  
Performance Bottlenecks ◽  
System Process ◽  
L2 Cache ◽  
Overall Performance ◽  
Intelligent Scheduling ◽  
Fair Scheduler

Sharing resources such as caches and memory buses between the cores of multi-core processors may cause performance bottlenecks for running programs. In this paper, the authors describe a meta-scheduler, which adapts the process scheduling decisions for reducing the contention for shared L2 caches on multi-core processors. The meta-scheduler takes into account the multi-core topology as well as the L2 cache related characteristics of the processes. Using the model generated by the process of machine learning, it predicts the L2 cache behavior, i.e., solo-run-L2-cache-stress, of the programs. It runs in user mode and guides the underlying operating system process scheduler in intelligent scheduling of processes to reduce the contention of shared L2 caches. In these experiments, the authors observed up to 12 percent speedup in individual as well as overall performance, while using meta-scheduler as compared to default process scheduler (Completely Fair Scheduler) of Linux kernel.

Mind wandering as data augmentation: How mental travel supports abstraction

2020 ◽  
Vol 43 ◽  
Author(s):  
Myrthe Faber
Keyword(s):  
Machine Learning ◽  
Data Augmentation ◽  
Mental Content ◽  
Mind Wandering ◽  
Theoretical Framework ◽  
Important Addition

Abstract Gilead et al. state that abstraction supports mental travel, and that mental travel critically relies on abstraction. I propose an important addition to this theoretical framework, namely that mental travel might also support abstraction. Specifically, I argue that spontaneous mental travel (mind wandering), much like data augmentation in machine learning, provides variability in mental content and context necessary for abstraction.

Machine Learning for Speaker Recognition

2020 ◽  
Author(s):  
Man-Wai Mak ◽  
Jen-Tzung Chien
Keyword(s):  
Machine Learning ◽  
Speaker Recognition

Data Mining and Machine Learning

2020 ◽  
Author(s):  
Mohammed J. Zaki ◽  
Wagner Meira, Jr
Keyword(s):  
Machine Learning ◽  
Data Mining

Mathematics for Machine Learning

2020 ◽  
Author(s):  
Marc Peter Deisenroth ◽  
A. Aldo Faisal ◽  
Cheng Soon Ong
Keyword(s):  
Machine Learning

Phase Transitions in Machine Learning

2009 ◽  
Author(s):  
Lorenza Saitta ◽  
Attilio Giordana ◽  
Antoine Cornuejols
Keyword(s):  
Machine Learning ◽  
Phase Transitions
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