Schedtask a hardware-assisted task scheduler

The execution of workloads such as web servers and database servers typically switches back and forth between different tasks such as user applications, system call handlers, and interrupt handlers. The combined size of the instruction footprints of such tasks typically exceeds that of the i-cache (...

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Bibliographic Details
Published in:MICRO-50 : the 50th annual IEEE/ACM International Symposium on Microarchitecture : proceedings : October 14-18, 2017, Cambridge, MA pp. 612 - 624
Main Authors: Kallurkar, Prathmesh, Sarangi, Smruti R.
Format: Conference Proceeding
Language:English
Published: New York, NY, USA ACM 14.10.2017
Series:ACM Conferences
Subjects:
architectural support for operating system
cache pollution
Computer systems organization > Architectures > Distributed architectures > Cloud computing
Computer systems organization > Architectures > Parallel architectures > Multicore architectures
Hardware
Operating systems
Pollution
Prefetching
Scheduling
Servers
Software and its engineering > Software organization and properties > Contextual software domains > Operating systems > Memory management > Virtual memory
Software and its engineering > Software organization and properties > Contextual software domains > Operating systems > Process management > Scheduling
Task analysis
scheduling
cache pollution
architectural support for operating system
ISBN:1450349528, 9781450349529
ISSN:2379-3155
Online Access:Get full text
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Summary:The execution of workloads such as web servers and database servers typically switches back and forth between different tasks such as user applications, system call handlers, and interrupt handlers. The combined size of the instruction footprints of such tasks typically exceeds that of the i-cache (16--32 KB). This causes a lot of i-cache misses and thereby reduces the application's performance. Hence, we propose SchedTask, a hardware-assisted task scheduler that improves the performance of such workloads by executing tasks with similar instruction footprints on the same core. We start by decomposing the combined execution of the OS and the applications into sequences of instructions called SuperFunctions. We propose a scheme to determine the amount of overlap between the instruction footprints of different SuperFunctions by using Bloom filters. We then use a hierarchical scheduler to execute SuperFunctions with similar instruction footprints on the same core. For a suite of 8 popular OS-intensive workloads, we report an increase in the application's performance of up to 29 percentage points (mean: 11.4 percentage points) over state of the art scheduling techniques.
ISBN:1450349528
9781450349529
ISSN:2379-3155
DOI:10.1145/3123939.3123984