Paper Info
Title
Prediction-Based Task Migration On S-Nuca Many-Cores
Abstract
Performance of a task running on a many-core with distributed shared Last-Level Cache (LLC) strongly depends on two factors: the power budget needed to guarantee thermally safe operation and the LLC latency. The task's thread-to-core mapping determines both the factors. Arrival and departure of tasks on a many-core deployed in an open system can change its state significantly in terms of available cores and power budget. Task migrations can thereupon be used as a tool to keep the many-core operating at the peak performance. Furthermore, the relative impacts of power budget and LLC latency on a task's performance can change with its different execution phases mandating its migration on-the-fly.We propose the first run-time algorithm PCMig that increases the performance of a many-core with distributed shared LLC by migrating tasks based on their phases and the many-core's state. PCMig is based on a performance-prediction model that predicts the performance impact of migrations. PCMig results in up to 16 % reduction in the average response time compared to the state-of-the-art.
Year
DOI
Venue
2019
10.23919/DATE.2019.8714974
2019 DESIGN, AUTOMATION & TEST IN EUROPE CONFERENCE & EXHIBITION (DATE)
Keywords
Field
DocType
Cache Memory, Processor Scheduling, Power Dissipation, Thermal Stability
Power budget,Computer science,Cache,Latency (engineering),Response time,Real-time computing,Open system (systems theory)
Conference
ISSN
Citations 
PageRank 
1530-1591
0
0.34
References 
Authors
0
4
Name
Order
Citations
PageRank
Martin Rapp164.83
Anuj Pathania218114.97
Tulika Mitra32714135.99
J. Henkel44471366.50