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SESE Introduction Background Cache Analysis for Diverse Programs - - PowerPoint PPT Presentation

Nov 14, 2023 264 likes •668 views

Instruction Caches in Static WCET Analysis of Artificially Diversified Software Joachim Fellmuth, Thomas G othel, Sabine Glesner Technische Universit at Berlin Software and Embedded Systems Engineering ECRTS 2018, Barcelona SESE

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SESE

Instruction Caches in Static WCET Analysis

  • f Artificially Diversified Software

Joachim Fellmuth, Thomas G¨

  • thel, Sabine Glesner

Technische Universit¨ at Berlin Software and Embedded Systems Engineering

ECRTS 2018, Barcelona

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Motivation

Cyber-physical systems (CPS) omnipresent safety-critical, hard real-time requirements highly interconnected → large attack surface

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Motivation

Cyber-physical systems (CPS) omnipresent safety-critical, hard real-time requirements highly interconnected → large attack surface Security important development aspect unsafe languages enable code-reuse attacks → use knowledge of system’s memory

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Motivation

Cyber-physical systems (CPS) omnipresent safety-critical, hard real-time requirements highly interconnected → large attack surface Security important development aspect unsafe languages enable code-reuse attacks → use knowledge of system’s memory Artificial software diversity hides memory layout (e.g. randomize instruction addresses) copes with unknown attack types WCET-aware security increase possible [FHPG17]

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Problem

Problem: Uncertainty induces pessimism in WCET analysis timing impact of some randomized diversification techniques unpredictable

[DDNS12, LHBF14, Coh93, FSA97, WMHL12]

WCET hardware analyses rely on full knowledge of the program addresses state-of-the-art cache analyses not able to produce upper bound estimate for all program variants

[HJR11, ZK15, BC08, Cul13, LGR+16]

All miss has to be assumed as worst-case cache behavior

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Proposed Solution

Our goal: Efficient WCET cache analysis for diverse programs powerful diversity approach reuse established analyses, compatible with IPET tight worst-case estimate over all variants Approach: relocation and reordering of code fragments introduce uncertainty into WCET cache analysis aggregate results for all variants per basic block

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Outline

1 Background

Artificial software diversity WCET Analysis

2 Cache Analysis for Diverse Programs

Must Analysis Further Analyses

3 Evaluation 4 Conclusion

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Outline

1 Background

Artificial software diversity WCET Analysis

2 Cache Analysis for Diverse Programs

Must Analysis Further Analyses

3 Evaluation 4 Conclusion

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Artificial software diversity

Semantically equivalent program variants

→ different program layout in memory → exploit compiler decisions to obtain variants

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Artificial software diversity

Semantically equivalent program variants

→ different program layout in memory → exploit compiler decisions to obtain variants

We use relocation and reordering of rearrangeable code parts (fragments)

→ no changes to instructions (code size) and CFG → predictable behavior over all variants → covers the entire instruction memory

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Artificial software diversity

Semantically equivalent program variants

→ different program layout in memory → exploit compiler decisions to obtain variants

We use relocation and reordering of rearrangeable code parts (fragments)

→ no changes to instructions (code size) and CFG → predictable behavior over all variants → covers the entire instruction memory

Different fragment granularities possible

→ segment-, function, block level

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Cache analysis: Must (LRU)

bb1: bb2: bb3: bb4: bb5: bb6: bb7: CFG creation

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Cache analysis: Must (LRU)

bb1: bb2: bb3: bb4: bb5: bb6: bb7: 9 10 10 1 1 2 2 3 6 7 7 3 4 4 5 5 6 8 8 9 11 11 12 set 1 i1 set 2 i1 set 1 i2 cache (A=2): Assign cache blocks and sets

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Cache analysis: Must (LRU)

bb1: bb2: bb3: bb4: bb5: bb6: bb7: 9 10 10 1 1 2 2 3 6 7 7 3 4 4 5 5 6 8 8 9 11 11 12 set 1 i1 set 2 i1 set 1 i2 cache (A=2):

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  • 1

6 7

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6

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  • age 1

age 2 Abstract Cache State (ACS) filled using fix- point algorithm

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Cache analysis: Must (LRU)

bb1: bb2: bb3: bb4: bb5: bb6: bb7: 9 10 10 1 1 2 2 3 6 7 7 3 4 4 5 5 6 8 8 9 11 11 12 set 1 i1 set 2 i1 set 1 i2 cache (A=2):

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10 11 12

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6 3 4 1 2

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  • age 1

age 2 ACS update function

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Cache analysis: Must (LRU)

bb1: bb2: bb3: bb4: bb5: bb6: bb7: 9 10 10 1 1 2 2 3 6 7 7 3 4 4 5 5 6 8 8 9 11 11 12 set 1 i1 set 2 i1 set 1 i2 cache (A=2):

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10 -

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10 11 12

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6 7

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  • age 1

age 2 ACS join func- tion

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Cache analysis: Must (LRU)

bb1: bb2: bb3: bb4: bb5: bb6: bb7: 9 10 10 1 1 2 2 3 6 7 7 3 4 4 5 5 6 8 8 9 11 11 12 set 1 i1 set 2 i1 set 1 i2 cache (A=2): 16 16 17 17 18 18 bb5’: Diversity:

  • Alt. node location -

different number of memory blocks

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Cache analysis: Must (LRU)

bb1: bb2: bb3: bb4: bb5: bb6: bb7: 9 10 10 1 1 2 2 3 6 7 7 3 4 4 5 5 6 8 8 9 11 11 12 set 1 i1 set 2 i1 set 1 i2 cache (A=2): 17 17 18 18 19 19 bb5’: f r a g m e n t a f r a g m e n t b Diversity: Different fragments: Conflicts unknown

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Outline

1 Background

Artificial software diversity WCET Analysis

2 Cache Analysis for Diverse Programs

Must Analysis Further Analyses

3 Evaluation 4 Conclusion

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Cache Analysis for diverse Programs

Uncertainty of relocation and reordering in Abstract Cache State every basic block (BB) belongs to a fragment distances to BB are only known within this fragment → one ”virtual” cache per fragment → regular cache replacement within fragment → cache contents of other fragments subject to worst-case behavior depends on offset within set → cache behavior is equal over all sets → fragment starts at 0 plus offset → one cache representation for each possible offset

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Location-tolerant Must analysis

bb1: bb2: bb3: bb4: bb5: bb6: bb7: regular CFG

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Location-tolerant Must analysis

bb1: bb2: bb3: bb4: bb5: bb6: bb7: f r a g m e n t a f r a g m e n t b BB on two fragments

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Location-tolerant Must analysis

bb1: bb2: bb3: bb4: bb5: bb6: bb7: a.0 a.0 a.0 a.1 a.1 a.1 a.1 a.2 a.2 a.2 a.2 a.3 a.3 a.3 a.3 a.4 a.4 a.4 a.4 a.5 a.5 a.5 set 1 i1 set 2 i1 set 1 i2 cache (fragment a): cache set map- ping for both

  • ffsets, frag-

ment a

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Location-tolerant Must analysis

bb1: bb2: bb3: bb4: bb5: bb6: bb7: a.0 a.0 a.0 a.1 a.1 a.1 a.1 a.2 a.2 a.2 a.2 a.3 a.3 a.3 a.3 a.4 a.4 a.4 a.4 a.5 a.5 a.5 b.3 b.3 b.3 b.4 b.4 b.4 b.0 b.0 b.0 b.1 b.1 b.1 b.1 b.2 b.2 b.2 b.2 b.3 b.4 b.5 b.5 b.5 b.5 b.6 set 1 i1 set 2 i1 set 1 i2 cache (fragment a): cache (fragment b): cache set map- ping for both

  • ffsets, frag-

ment b

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Location-tolerant Must analysis

bb1: bb2: bb3: bb4: bb5: bb6: bb7: a.0 a.0 a.0 a.1 a.1 a.1 a.1 a.2 a.2 a.2 a.2 a.3 a.3 a.3 a.3 a.4 a.4 a.4 a.4 a.5 a.5 a.5 b.3 b.3 b.3 b.4 b.4 b.4 b.0 b.0 b.0 b.1 b.1 b.1 b.1 b.2 b.2 b.2 b.2 b.3 b.4 b.5 b.5 b.5 b.5 b.6 b.3 b.4 a.0 a.1 a.2 b.3 b.4 b.0 b.1 a.0 a.1 a.2 b.3 a.2 a.3 a.4 a.5 a.0 a.1 a.0 a.1 a.2 b.1 b.2 b.3 b.4 b.5

  • ffset 0:

set 1 i1 set 2 i1 set 1 i2 cache (fragment a): cache (fragment b): ACS offset 0

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Location-tolerant Must analysis

bb1: bb2: bb3: bb4: bb5: bb6: bb7: a.0 a.0 a.0 a.1 a.1 a.1 a.1 a.2 a.2 a.2 a.2 a.3 a.3 a.3 a.3 a.4 a.4 a.4 a.4 a.5 a.5 a.5 b.3 b.3 b.3 b.4 b.4 b.4 b.0 b.0 b.0 b.1 b.1 b.1 b.1 b.2 b.2 b.2 b.2 b.3 b.4 b.5 b.5 b.5 b.5 b.6 b.3 b.4 a.0 a.1 a.2 b.3 b.4 b.0 b.1 a.0 a.1 a.2 b.3 a.2 a.3 a.4 a.5 a.0 a.1 a.0 a.1 a.2 b.1 b.2 b.3 b.4 b.5

  • ffset 0:

b.3 b.4 a.0 a.1 a.2 b.3 b.4 b.0 b.1 a.0 a.1 a.2 b.3 a.2 a.3 a.4 a.5 a.0 a.1 a.0 a.1 a.2 b.2 b.3 b.3 b.4 b.5 b.6

  • ffset 1:

set 1 i1 set 2 i1 set 1 i2 cache (fragment a): cache (fragment b): ACS offset 0 & 1

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Location-tolerant Must analysis

bb1: bb2: bb3: bb4: bb5: bb6: bb7: a.0 a.0 a.0 a.1 a.1 a.1 a.1 a.2 a.2 a.2 a.2 a.3 a.3 a.3 a.3 a.4 a.4 a.4 a.4 a.5 a.5 a.5 b.3 b.3 b.3 b.4 b.4 b.4 b.0 b.0 b.0 b.1 b.1 b.1 b.1 b.2 b.2 b.2 b.2 b.3 b.4 b.5 b.5 b.5 b.5 b.6 b.3 b.4 a.0 a.1 a.2 b.3 b.4 b.0 b.1 a.0 a.1 a.2 b.3 a.2 a.3 a.4 a.5 a.0 a.1 a.0 a.1 a.2 b.1 b.2 b.3 b.4 b.5

  • ffset 0:

b.3 b.4 a.0 a.1 a.2 b.3 b.4 b.0 b.1 a.0 a.1 a.2 b.3 a.2 a.3 a.4 a.5 a.0 a.1 a.0 a.1 a.2 b.2 b.3 b.3 b.4 b.5 b.6

  • ffset 1:

set 1 i1 set 2 i1 set 1 i2 cache (fragment a): cache (fragment b): update: con- flict within fragment

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Location-tolerant Must analysis

bb1: bb2: bb3: bb4: bb5: bb6: bb7: a.0 a.0 a.0 a.1 a.1 a.1 a.1 a.2 a.2 a.2 a.2 a.3 a.3 a.3 a.3 a.4 a.4 a.4 a.4 a.5 a.5 a.5 b.3 b.3 b.3 b.4 b.4 b.4 b.0 b.0 b.0 b.1 b.1 b.1 b.1 b.2 b.2 b.2 b.2 b.3 b.4 b.5 b.5 b.5 b.5 b.6 b.3 b.4 a.0 a.1 a.2 b.3 b.4 b.0 b.1 a.0 a.1 a.2 b.3 a.2 a.3 a.4 a.5 a.0 a.1 a.0 a.1 a.2 b.1 b.2 b.3 b.4 b.5

  • ffset 0:

b.3 b.4 a.0 a.1 a.2 b.3 b.4 b.0 b.1 a.0 a.1 a.2 b.3 a.2 a.3 a.4 a.5 a.0 a.1 a.0 a.1 a.2 b.2 b.3 b.3 b.4 b.5 b.6

  • ffset 1:

set 1 i1 set 2 i1 set 1 i2 cache (fragment a): cache (fragment b): update: no conflict within fragment

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Location-tolerant Must analysis

bb1: bb2: bb3: bb4: bb5: bb6: bb7: a.0 a.0 a.0 a.1 a.1 a.1 a.1 a.2 a.2 a.2 a.2 a.3 a.3 a.3 a.3 a.4 a.4 a.4 a.4 a.5 a.5 a.5 b.3 b.3 b.3 b.4 b.4 b.4 b.0 b.0 b.0 b.1 b.1 b.1 b.1 b.2 b.2 b.2 b.2 b.3 b.4 b.5 b.5 b.5 b.5 b.6 b.3 b.4 a.0 a.1 a.2 b.3 b.4 b.0 b.1 a.0 a.1 a.2 b.3 a.2 a.3 a.4 a.5 a.0 a.1 a.0 a.1 a.2 b.1 b.2 b.3 b.4 b.5

  • ffset 0:

b.3 b.4 a.0 a.1 a.2 b.3 b.4 b.0 b.1 a.0 a.1 a.2 b.3 a.2 a.3 a.4 a.5 a.0 a.1 a.0 a.1 a.2 b.2 b.3 b.3 b.4 b.5 b.6

  • ffset 1:

set 1 i1 set 2 i1 set 1 i2 cache (fragment a): cache (fragment b): update: blocks

  • f other frag-

ments

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Location-tolerant Must analysis

bb1: bb2: bb3: bb4: bb5: bb6: bb7: a.0 a.0 a.0 a.1 a.1 a.1 a.1 a.2 a.2 a.2 a.2 a.3 a.3 a.3 a.3 a.4 a.4 a.4 a.4 a.5 a.5 a.5 b.3 b.3 b.3 b.4 b.4 b.4 b.0 b.0 b.0 b.1 b.1 b.1 b.1 b.2 b.2 b.2 b.2 b.3 b.4 b.5 b.5 b.5 b.5 b.6 b.3 b.4 a.0 a.1 a.2 b.3 b.4 b.0 b.1 a.0 a.1 a.2 b.3 a.2 a.3 a.4 a.5 a.0 a.1 a.0 a.1 a.2 b.1 b.2 b.3 b.4 b.5

  • ffset 0:

b.3 b.4 a.0 a.1 a.2 b.3 b.4 b.0 b.1 a.0 a.1 a.2 b.3 a.2 a.3 a.4 a.5 a.0 a.1 a.0 a.1 a.2 b.2 b.3 b.3 b.4 b.5 b.6

  • ffset 1:

set 1 i1 set 2 i1 set 1 i2 cache (fragment a): cache (fragment b): join: oldest possible age for each memory block

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Further analyses

Must analysis update uses most of addressing information within fragment: regular update

  • utside fragment: worst-case aging may not cause eviction

Approach can be applied similarly to may and persistence analyses → Ballabriga’s multi-level persistence analysis [BC08] → Allows for first miss classification with respect to enclosing loops ACS results aggregated to find worst case timing per basic block

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Outline

1 Background

Artificial software diversity WCET Analysis

2 Cache Analysis for Diverse Programs

Must Analysis Further Analyses

3 Evaluation 4 Conclusion

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Experimental Setup

Implemented in OTAWA (I.R.I.T. Labs, Toulouse) analysis entirely static framework intended for extension in research

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Experimental Setup

Implemented in OTAWA (I.R.I.T. Labs, Toulouse) analysis entirely static framework intended for extension in research Tested on M¨ alardalen benchmarks widely used WCET benchmark suite program collection, cover all aspects of WCET analysis Experiments conducted with random benchmark variants cache sizes, associativity and granularity varied diverse WCET estimate compared to non-diverse analysis WCETAM base line of all estimations

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Results: Precision

bs bsort100 cnt crc edn expint fac fdct fibcall fir insertsort jfdctint matmult ndes nsichneu ud 0.2 0.4 0.6 0.8 1 WCET / WCETAM WCETmin WCETmax WCETdiv

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Results: Impact of granularity

b s b s

  • r

t 1 c n t c r c e d n e x p i n t f a c f d c t fi b c a l l fi r i n s e r t s

  • r

t j f d c t i n t m a t m u l t n d e s n s i c h n e u u d 1 1.2 1.4 WCET over-approximation Segment Level Function Level Block Level

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Outline

1 Background

Artificial software diversity WCET Analysis

2 Cache Analysis for Diverse Programs

Must Analysis Further Analyses

3 Evaluation 4 Conclusion

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Conclusion

Contribution: static cache analysis for diversified programs tight estimates with respect to uncertainties of diversity major improvement to assuming all miss acceptable analysis time supports powerful relocation and reordering diversification applicable to other aspects of varying memory layout: e.g. dynamic libraries, redundancy concepts

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Conclusion

Contribution: static cache analysis for diversified programs tight estimates with respect to uncertainties of diversity major improvement to assuming all miss acceptable analysis time supports powerful relocation and reordering diversification applicable to other aspects of varying memory layout: e.g. dynamic libraries, redundancy concepts Outlook: more WCET aspects: multi-level caches, branch prediction investigate architectures with timing anomalies

  • ptimize fragmentation using cache analysis results

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Introduction Background Cache Analysis for Diverse Programs Evaluation Conclusion

Conclusion

Contribution: static cache analysis for diversified programs tight estimates with respect to uncertainties of diversity major improvement to assuming all miss acceptable analysis time supports powerful relocation and reordering diversification applicable to other aspects of varying memory layout: e.g. dynamic libraries, redundancy concepts Outlook: more WCET aspects: multi-level caches, branch prediction investigate architectures with timing anomalies

  • ptimize fragmentation using cache analysis results

Q & A

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