Effect of Compiler Optimizations on the Error Resilience of Soft Computing Applications

Anna Thomas, Jacques Clapauch, Karthik Pattabiraman

University of British Columbia (UBC)

Soft Computing Applications

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 Applications in AI, multimedia processing  Expected to dominate future workloads [Dubey’07]  Tolerate many kinds of faults in data – Error Resilient

Original image (left) versus faulty image from JPEG decoder

Egregious Data Corruptions (EDCs)

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 Large or unacceptable deviation in output  Based on fidelity metric (e.g., PSNR < 30)

EDC image (PSNR of 11.37) of JPEG vs Non-EDC image (PSNR of 44.79)

Goal

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 Detect EDC causing faults

Non-EDC

EDC

Detector

Benign

Pre-emptive

Selective

Application Execution

Preliminary Fault Injection Study

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Execute Application

Choose dynamic data instance at

random

Inject Random Single bit flip

Compare faulty &

fault-free outcome

Crash Fidelity Metric

Benign

EDC

Non- EDC

No Change

Exception Value Change

Low Deviation

High deviation

Why focus on EDC Causing Faults?

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Blindly detecting all faults is wasteful !

  92% : tolerable outcomes

 Media-bench programs: Soft-computing applications

Background

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Static analysis algorithm to identify detector locations for EDCs [DSN 2013 – Anna Thomas]

Compiler EDC

Ranking Algorithm

Selection Algorithm

IR

Application Source Code

Performance Overhead

Data Variables or Locations to Protect

Execution Profile

Backward slice replication

Background: EDC Coverage of technique

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Average EDC Coverage of 85% at 20% performance overhead

This Paper

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Compiler Optimizations

Improve Performance! Resilience

End Goal: Identify optimizations that guarantee error resilience

Performance - resilience trade off space

Outline

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  Background

  Problem and Approach

  Experimental Setup and Results

  Conclusion

Problem

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Optimizations affect error resilience

Affect Baseline

Resilience (EDC Rate)

Affect EDC Coverage of

our technique

How do optimizations affect the error resilience of soft computing applications, with our technique ?

Approach

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 Identified common compiler optimizations

 Performed fault injections on unoptimized and optimized versions of soft computing apps

 Studied the effect of optimizations:  Baseline resilience  EDC coverage of our technique

Compiler Optimizations

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 Identified four optimizations in LLVM compiler (of 15)   InstCombine   LICM   Loop Unroll   SCCP

Compiler Optimizations

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 Identified four optimizations in LLVM compiler (of 15)   InstCombine  LICM   Loop Unroll   SCCP

 Why these optimizations ?   Have conflicting effects on EDC rate

Compiler Optimizations

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Combine Redundant Instructions: Inst-Combine

Redundant Instructions

Uninteresting Code

Uninteresting Code

Combined Instructions

IC Opt

High EDC Likelihood

Lower Probability

of fault strike

But…

Conflicting effect on EDC rate!

Compiler Optimizations

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Loop Invariant Code Motion: LICM

Invariant Code

Loop Body

Loop Header

LICM

Loop Body

Loop Header

Invariant Code

High EDC Likelihood

Lower Probability

of fault strike

But…

Conflicting effect on EDC rate!

Outline

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  Background

  Problem and Approach

  Experimental Setup and Results

  Conclusion

Experimental Setup

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 Six Benchmarks from MediaBench and Parsec Suite   Fidelity Metric: PSNR, scaled distortion [Misailovic12]   EDC thresholds based on visual perception or 30% of SDCs

 Performed fault injections using LLFI [Thomas 13]   5000 fault injections per benchmark per optimization   Error Bars: (± 0.8% at 95% CI)

 Measured coverage under 20% performance overhead   Coverage = (No of EDCs detected / total no of EDCs)*100

Baseline Resilience: EDC Percentages

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Bench-mark

Inst- Combine LICM

Loop- Unroll SCCP UnOpt

Blackscholes 9.9 9.48 8.48 9.38 10

X264 2.48 2.96 2.82 2.1 2.24

Canneal 4.56 3.26 3.26 3.94 3.32

Swaptions 2.5 3.12 2.44 2.98 2.36

JPEG 3.68 3.56 4.16 4.08 3.76

MPEG2 2 2.1 2.56 1.7 2.3

Average 4.19 4.08 3.95 4.03 3.99

EDC Coverage: InstCombine

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Safe Optimization: Within 5% lower than baseline

EDC Coverage: LICM

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Outline

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  Background

  Problem and Approach

  Experimental Setup and Results

  Conclusion

Conclusion and Future Work

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 Compiler optimizations: Characterize performance-resilience trade off in soft computing applications  Baseline resilience lowered in some benchmarks  Our technique preserves resilience in most cases

Future Work   Include more optimizations – ongoing work   Classify optimizations into resilience packages   Decision tree for applying optimizations

Contact: Anna Thomas

Backup Slides

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Compiler Optimizations

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Loop Unrolling: Loop-Unroll

Loop Termination Condition

Loop Body

Unroll

Loop Termination Condition

Unrolled Loop Body

Low chance of fault strike

Lower EDC rate on Loop-Unrolled Code?

Compiler Optimizations

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Sparse Conditional Constant Propagation: SCCP

Conditional branches & constants

Uninteresting Code

SCCP

Conditionals removed

(Optimized code)

Uninteresting Code

High EDC Likelihood

Higher EDC rate on SCCP optimized Code?

LLVM Fault Injector LLFI

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LLFI: Fault Injector at LLVM compiler’s intermediate code level [Thomas’13]

  easy source code mapping