Markov Model Prediction of Markov Model Prediction of I/O Requests - - PowerPoint PPT Presentation

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Markov Model Prediction of Markov Model Prediction of I/O Requests - - PowerPoint PPT Presentation

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Pablo Research Group UIUC Markov Model Prediction of Markov Model Prediction of I/O Requests for Scientific I/O Requests for Scientific Applications Applications James Oly and Daniel A. Reed James Oly and Daniel A. Reed Pablo Research Group

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Pablo Research Group UIUC

Markov Model Prediction of Markov Model Prediction of I/O Requests for Scientific I/O Requests for Scientific Applications Applications

James Oly and Daniel A. Reed James Oly and Daniel A. Reed Pablo Research Group Pablo Research Group Department of Computer Department of Computer Science Science University of Illinois University of Illinois {jamesoly,reed}@cs.uiuc.edu {jamesoly,reed}@cs.uiuc.edu

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Outline

= = Markov Models for I/O

Markov Models for I/O

= = Description of Scientific Application Traces

Description of Scientific Application Traces

= = Trace

Trace-

  • driven Simulations

driven Simulations – – Prediction accuracy results Prediction accuracy results – – Cache simulation results Cache simulation results

= = Experimental Results

Experimental Results

= = Summary

Summary

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Introduction

= = I/O continues to be a bottleneck in scientific

I/O continues to be a bottleneck in scientific computing computing

= = Knowledge of the I/O request pattern can be

Knowledge of the I/O request pattern can be crucial to improving performance crucial to improving performance

= = Exact descriptions of request patterns:

Exact descriptions of request patterns: – – May require expensive, multilevel May require expensive, multilevel instrumentation to capture complex patterns instrumentation to capture complex patterns – – May vary due to data dependence or user May vary due to data dependence or user interaction interaction

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Markov Models for I/O

= = Probabilistic models can help

Probabilistic models can help

= = Markov property: the probability of encountering a

Markov property: the probability of encountering a future state depends solely on the current state future state depends solely on the current state – – Little history Little history -

  • > compact model

> compact model

= = Each file block is represented by a state in the

Each file block is represented by a state in the model model

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Example Model

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Prediction Algorithms

= = Greedy

Greedy – – Always chooses the most likely transition from Always chooses the most likely transition from last chosen state last chosen state

= = Path

Path-

  • based

based – – Depth Depth-

  • limited search for most likely path

limited search for most likely path

= = Amortized

Amortized – – Finds most likely state for 1, 2, 3... transitions Finds most likely state for 1, 2, 3... transitions from current state from current state – – Generated using state occupancy vectors and Generated using state occupancy vectors and Kolmogorov equation: Kolmogorov equation: π π(t+1) = (t+1) = π π(t) P (t) P

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Application Traces

= = Cactus

Cactus – – Modular environment for numerical relativity Modular environment for numerical relativity – – Small reads (<16 bytes), 65% sequential Small reads (<16 bytes), 65% sequential

= = Dyna3D

Dyna3D – – Explicit finite Explicit finite-

  • element code analyzing transient

element code analyzing transient dynamic response of 3D solids and structures dynamic response of 3D solids and structures – – 2MB worth of one byte reads, 100% sequential 2MB worth of one byte reads, 100% sequential

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Application Traces

= = CONTINUUM

CONTINUUM – – Unstructured mesh continuum mechanics code Unstructured mesh continuum mechanics code – – Widely varying request size, 43% sequential Widely varying request size, 43% sequential

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Application Traces

= = Hartree

Hartree-

  • Fock

Fock – – Calculates interactions among atomic nuclei and Calculates interactions among atomic nuclei and electrons in reaction paths, storing numerical electrons in reaction paths, storing numerical quadrature data for subsequent reuse quadrature data for subsequent reuse – – 80KB reads, 100% sequential; read six times 80KB reads, 100% sequential; read six times

= = SAR

SAR – – Produces surface images from aircraft Produces surface images from aircraft-

  • or
  • r

satellite satellite-

  • mounted radar data

mounted radar data – – 370KB to 2MB requests, 67% sequential 370KB to 2MB requests, 67% sequential

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Application Traces

= = HYDRO

HYDRO – – Block Block-

  • structured mesh hydrodynamics code

structured mesh hydrodynamics code – – Widely varying request size, 67% sequential Widely varying request size, 67% sequential

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Prediction Accuracy

= = Judges how accurately the model represents the

Judges how accurately the model represents the

  • riginal pattern
  • riginal pattern

= = A prediction of length L is created before each

A prediction of length L is created before each block request block request

= = The prediction is compared to the next L blocks

The prediction is compared to the next L blocks actually requested actually requested

= = The percentages of correctly predicted blocks are

The percentages of correctly predicted blocks are averaged to form the overall accuracy rating averaged to form the overall accuracy rating

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Prediction Accuracy

= = Block size and prediction length

Block size and prediction length

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Prediction Accuracy

= = Prediction algorithm

Prediction algorithm – – Most cases showed little difference between the

Most cases showed little difference between the algorithms, with amortized usually performing slightly algorithms, with amortized usually performing slightly better better

– – Amortized performed far better for Hartree

Amortized performed far better for Hartree-

  • Fock

Fock

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Cache Simulation

= = LRU replacement

LRU replacement

= = Prefetched blocks inserted at LRU end of the

Prefetched blocks inserted at LRU end of the replacement queue replacement queue

= = Block sizes ranging from 1 KB to 64 KB

Block sizes ranging from 1 KB to 64 KB

= = Prediction horizon ranging from 1 to 10

Prediction horizon ranging from 1 to 10 blocks blocks

= = Compared hit ratios with N

Compared hit ratios with N-

  • block readahead

block readahead policy policy

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Cache Simulation

= = All strategies yield relatively high hit ratios

All strategies yield relatively high hit ratios

= = Markov model predictions usually have

Markov model predictions usually have slightly higher hit rates, especially for N>1 slightly higher hit rates, especially for N>1

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Experimental Results

= = Cactus

Cactus – – 50x50x50 grid, 2000 iterations

50x50x50 grid, 2000 iterations

– – 2 millions block reads over 2 GB file

2 millions block reads over 2 GB file

= = Synthetic Hartree

Synthetic Hartree-

  • Fock

Fock – – Synthetic application that mimics Hartree

Synthetic application that mimics Hartree-

  • Fock

Fock

– – Sequential 80 KB reads to a 2.64 MB file, repeated

Sequential 80 KB reads to a 2.64 MB file, repeated six times six times

– – ~1 ms compute time between requests

~1 ms compute time between requests

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Experimental Results

= = Applications modified for PPFS 2, our user

Applications modified for PPFS 2, our user-

  • level

level parallel filesystem parallel filesystem

= = Prediction policies compared

Prediction policies compared – – No prefetching No prefetching – – N N-

  • block readahead

block readahead – – Markov model (greedy prediction) Markov model (greedy prediction)

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Experimental Results (Cactus)

= = Execution time decreased by up to 10% compared to

Execution time decreased by up to 10% compared to not prefetching not prefetching

= = N

N-

  • block readahead was not effective

block readahead was not effective

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Experimental Results (H-F)

= = Sequential pattern is ideal for N

Sequential pattern is ideal for N-

  • block readahead

block readahead

= = Markov model prediction matches its performance

Markov model prediction matches its performance

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Summary

= = Markov models can accurately represent the

Markov models can accurately represent the access patterns of scientific applications access patterns of scientific applications – – Usually have minimal loss of accuracy over a wide

Usually have minimal loss of accuracy over a wide range of block sizes range of block sizes

– – More sophisticated prediction policies can reduce

More sophisticated prediction policies can reduce error error

= = Markov models for prefetching

Markov models for prefetching – – Can noticeably reduce the execution time of a

Can noticeably reduce the execution time of a scientific application scientific application

– – Low overhead

Low overhead