improving system performance using application-level...

Introduction Implementation Results Future work

Improving system performance usingapplication-level hints

Bjorn Dobel<[email protected]>

24.06.2005

Bjorn Dobel Application-Level Hints 24.06.2005 1 / 18

Contents

1 IntroductionThe problemApplication-level hints

2 Implementation

3 Results

4 Future work

2 Implementation

3 Results

4 Future work

The problem

Scenario: Application startup

cold caches after bootup

disk accesses

interleaving with CPU operationsdistributed all over run timenot independent

solution:

find out which parts of which files are accessedperform prefetching

The problem

Scenario: Application startup

cold caches after bootup

disk accesses

interleaving with CPU operationsdistributed all over run timenot independent

solution:

find out which parts of which files are accessedperform prefetching

The problem

The problem in general

operating system manages resources

operating system cannot predict future resource usage

But we can predict it by using

heuristics

statistics

informed methods

The problem

The problem in general

operating system manages resources

operating system cannot predict future resource usage

But we can predict it by using

heuristics

statistics

informed methods

Application-level hints

Application-Level Hints

Definition

Application-level hints: hints issued by (user-space) applicationsthat help the operating system predict future resource accesses.The operating system decides what to do with this information.

explicit hints (asynchronous I/O)

implicit hints (prefetching)

2 Implementation

3 Results

4 Future work

Prerequisites

What needs to be done?

1 Identify necessary data

file namesaccessed file sections

2 Obtain data

various options (next slide)

3 Data processing

generation of application-level hints

4 Application of hints

prefetch data described by hints

Prerequisites

2 Obtain data

3 Data processing

Prerequisites

2 Obtain data

3 Data processing

Prerequisites

2 Obtain data

3 Data processing

Monitoring options

Obtaining data

strace

restricted to system calls

ld preload

restricted to dynamically linked libraries

kProbes

Linux Tracing Toolkit

huge amount of data

implement own monitor

Monitoring options

Obtaining data

strace

ld preload

kProbes

huge amount of data

Monitoring options

Obtaining data

strace

ld preload

kProbes

huge amount of data

Monitoring options

Obtaining data

strace

ld preload

kProbes

huge amount of data

Monitoring options

Obtaining data

strace

ld preload

kProbes

huge amount of data

Monitoring options

Obtaining data

strace

ld preload

kProbes

huge amount of data

Architecture

Architecture overview

devices

perf_insert_data()

Hint Generators

(e.g., HintGen) Applications

(e.g. ReadPref)

Hinting

Storage

User space

Kernel space

Sensors

Kernel subsystems

HintMod

Kernel enhancements

Linux kernel enhancements

1 active sensors at certain points

sys open()add to page cache()

2 register callback to obtain sensor data3 HintMod kernel module

ring buffers to store sensor datadevice interface for user-space applications

Kernel enhancements

2 register callback to obtain sensor data

3 HintMod kernel module

Kernel enhancements

2 register callback to obtain sensor data3 HintMod kernel module

Hint generation

scenario-specific

for the application startup scenario:

establish a connection between different kinds of sensor datamerge adjacent disk jobsorder disk jobs

Example from the KDE hint file/lib/tls/libcrypt.so.1

/usr/bin/startkde

/usr/lib/libqt-mt.so.3

508 330

861 143

1007 32

Hint generation

scenario-specific

establish a connection between different kinds of sensor data

merge adjacent disk jobsorder disk jobs

/usr/bin/startkde

508 330

861 143

1007 32

Hint generation

scenario-specific

/usr/bin/startkde

508 330

861 143

1007 32

Hint generation

scenario-specific

/usr/bin/startkde

508 330

861 143

1007 32

Application of hints

ReadPref daemon

read(ahead) sections of files as described in hint filelow priority

decreases later application startups by up to 43%

Problem 1: Interference with “real” disk jobs

monitor number of currently active disk jobs

Problem 2: Cache overrun

can be solved in a similar way

ReadPref daemon

2 Implementation

3 Results

4 Future work

Measurements

cold cache prefetched improvementstartup time startup time in %

GDM + X 12.78 s 12.15 s 5.00

KDE 18.90 s 11.81 s 37.50

Mozilla 5.14 s 2.89 s 43.80

Kate 4.10 s 2.91 s 29.00

The Gimp 7.68 s 6.83 s 13.10

Measurements

Measurements ctd.

cold cache startup

warm cache startup

GDM KDE Mozilla Kate TheGIMP

prefetched startup

��

time / s

Future work

Open issues

solve cache overrun problem

further investigation of scenarios

care for parallel prefetching

generate “informed” hints

acquire user-specific information

Future work

Open issues

Future work

Open issues

Future work

Open issues

Future work

Open issues

Future work

The end

Feel free to ask questions.

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