NDLI: Using idle workstations to implement predictive prefetching

Content Provider	IEEE Xplore Digital Library
Author	Wang, J.Y.Q. Ong, J.S. Coady, Y. Feeley, M.J.
Copyright Year	2000
Description	Author affiliation: Seagate Software Inc., Vancouver, BC, Canada (Wang, J.Y.Q.)
Abstract	The benefits of Markov-based predictive prefetching have been largely overshadowed by the overhead required to produce high-quality predictions. While both theoretical and simulation results for prediction algorithms appear promising, substantial limitations exist in practice. This outcome can be partially attributed to the fact that practical implementations ultimately make compromises in order to reduce overhead. These compromises limit the level of algorithm complexity, the variety of access patterns and the granularity of trace data that the implementation supports. This paper describes the design and implementation of GMS-3P (Global Memory System with Parallel Predictive Prefetching), an operating system kernel extension that offloads prediction overhead to idle network nodes. GMS-3P builds on the GMS global memory system, which pages to and from remote workstation memory. In GMS-3P, the target node sends an online trace of an application's page faults to an idle node that is running a Markov-based prediction algorithm. The prediction node then uses GMS to prefetch pages to the target node from the memory of other workstations in the network. Our preliminary results show that predictive prefetching can reduce the remote-memory page fault time by 60% or more and that, by offloading prediction overhead to an idle node, GMS-3P can reduce this improved latency by between 24% and 44%, depending on the Markov model order.
Starting Page	87
Ending Page	94
File Size	728408
Page Count	8
File Format	PDF
ISBN	0769507832
ISSN	10828907
DOI	10.1109/HPDC.2000.868638
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2000-08-04
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Workstations Prefetching Prediction algorithms Application software Predictive models Collision mitigation Runtime Pattern matching Computer science Computational modeling
Content Type	Text
Resource Type	Article

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

1. Introduction Using Idle Workstations to Implement Predictive Prefetching

A framework for data prefetching using off-line training of Markovian predictors

Using Idle Workstations to Implement Predictive Prefetching (2000)

Machine Learning Prediction andWeb Access Modeling

Pattern matching of time series and its application to trend prediction

Cost-Driven Hybrid Configuration Prefetching for Partial Reconfigurable Coprocessor

Simple alignment constraints for efficient alignment of RNA sequences using family-specific models

The effects of mispredicted-path execution on branch prediction structures

Link Completion using Prediction by Partial Matching

Using idle workstations to implement predictive prefetching

Similar Documents

1. Introduction Using Idle Workstations to Implement Predictive Prefetching

A framework for data prefetching using off-line training of Markovian predictors

Using Idle Workstations to Implement Predictive Prefetching (2000)

Machine Learning Prediction andWeb Access Modeling

Pattern matching of time series and its application to trend prediction

Cost-Driven Hybrid Configuration Prefetching for Partial Reconfigurable Coprocessor

Simple alignment constraints for efficient alignment of RNA sequences using family-specific models

The effects of mispredicted-path execution on branch prediction structures

Link Completion using Prediction by Partial Matching

Using idle workstations to implement predictive prefetching