translation cache policies for dynamic binary translation

National School of Computer Sciences

Translation cache policies for dynamic binary translation

Saber FERJANI

TIMA Laboratory - SLS Group

18 Avril 2013

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Who I am ?

Academic & Professional Cursus2010-2013 : Student at National School of Computer Sciences - Tunisia.2011/2012 : Robotic team leader, participation to many competitions.June-July 2011 : Intern at Alpha Technology, Design of many PCB layoutincluding QFP, SO, SMT and through hole components.July-August 2012 : Intern at STMicroelectronics : Developing software for aHygrometer and an Altimeter, for STM32F3 microcontroller

http ://about.me/ferjani

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Context

Why ?Hardware design is taking more and more time,Software development should start earlier,Instruction Set Simulators (ISS) handles the simulation of processors, namedtarget, on a machine with a different architecture, named host.

How?Cross Compilation.Interpretive translation.Dynamic Binary Translation.

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Context

Why ?Hardware design is taking more and more time,Software development should start earlier,Instruction Set Simulators (ISS) handles the simulation of processors, namedtarget, on a machine with a different architecture, named host.

How?Cross Compilation.Interpretive translation.Dynamic Binary Translation.

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Terminology

Simulator : just duplicate the behavior of the system.

Emulator : duplicate the inner workings of the system.

TB : Translated Bloc.

IR : Intermediate representation (also called op-code)

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Outline

1 Introduction

2 Cache Algorithms

3 Qemu internals

4 Preliminary Results

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I- Introduction

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Qemu OverviewGeneric and open source machine emulator and virtualizer,Created by Fabrice Bellard in 2003,uses portable dynamic translation,Supported Targets : x86, arm, mips, sh4, cris, sparc, powerpc, nds32...

Qemu FeaturesJust-in-time (JIT) compilation support,Self-modifying code support,Direct block chaining.

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Subject

ProblematicSimulation speed is mainly affected by reuse of TB,Current policy just flush the entire cache when it is full,We need to enhance translation cache policy in order to maximize TB reuse.

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II- Cache Algorithms

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Optimal cache algorithmEvict entry that will not be used for the longest time.Unfeasible in practice, since we cannot really know future !

First In First OutMost simple cache replacement policy,Entry remain in memory a constant duration.

Least Recently UsedEnhancement to FIFO.Each time an entry is referenced, it is moved to the end of the queue.

Least Frequently UsedExploit the overall popularity rather than temporal locality.Least referenced entry is always chosen for eviction.

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Optimal cache algorithmEvict entry that will not be used for the longest time.Unfeasible in practice, since we cannot really know future !

First In First OutMost simple cache replacement policy,Entry remain in memory a constant duration.

Least Recently UsedEnhancement to FIFO.Each time an entry is referenced, it is moved to the end of the queue.

Least Frequently UsedExploit the overall popularity rather than temporal locality.Least referenced entry is always chosen for eviction.

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Optimal cache algorithmEvict entry that will not be used for the longest time.Unfeasible in practice, since we cannot really know future !

First In First OutMost simple cache replacement policy,Entry remain in memory a constant duration.

Least Recently UsedEnhancement to FIFO.Each time an entry is referenced, it is moved to the end of the queue.

Least Frequently UsedExploit the overall popularity rather than temporal locality.Least referenced entry is always chosen for eviction.

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Optimal cache algorithmEvict entry that will not be used for the longest time.Unfeasible in practice, since we cannot really know future !

First In First OutMost simple cache replacement policy,Entry remain in memory a constant duration.

Least Recently UsedEnhancement to FIFO.Each time an entry is referenced, it is moved to the end of the queue.

Least Frequently UsedExploit the overall popularity rather than temporal locality.Least referenced entry is always chosen for eviction.

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III- Qemu internals

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Dynamic Binary Translation in Qemu

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Dynamic Binary Translation in Qemu

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Dynamic Binary Translation in Qemu

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Bloc chaining

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Bloc chaining

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lookup tbby target pc

Cached ? Translate onebasic block

execute tbchain itto existedbasic block

Exceptionhandling

no

yes

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lookup tbby target pc

Cached ? Translate onebasic block

execute tbchain itto existedbasic block

Exceptionhandling

no

yes

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Focus on (Translate one basic block)

try to allocatespace for tb

sucess ?Flush entiretranslation

cache

generate op& host code

allocatespace for tb(cannot fail!)

no

yes

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Implementation constraints

Variable TB sizeIn basics cache algorithms, evicting one entry is always sufficient to bring an other,but in our case, TB size is not only variable, but also unknown during allocation.

Self modifying codeWhen the executed code modify it self, the TB is re-translated into differentspace. thus result in many memory allocation while only the last one is needed.

Low overheadWe need to predict if the the replacement cache overhead remain below the costof cache flush, otherwise, we should simply flush the entire cache.

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Implementation constraints

Variable TB sizeIn basics cache algorithms, evicting one entry is always sufficient to bring an other,but in our case, TB size is not only variable, but also unknown during allocation.

Self modifying codeWhen the executed code modify it self, the TB is re-translated into differentspace. thus result in many memory allocation while only the last one is needed.

Low overheadWe need to predict if the the replacement cache overhead remain below the costof cache flush, otherwise, we should simply flush the entire cache.

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Implementation constraints

Variable TB sizeIn basics cache algorithms, evicting one entry is always sufficient to bring an other,but in our case, TB size is not only variable, but also unknown during allocation.

Self modifying codeWhen the executed code modify it self, the TB is re-translated into differentspace. thus result in many memory allocation while only the last one is needed.

Low overheadWe need to predict if the the replacement cache overhead remain below the costof cache flush, otherwise, we should simply flush the entire cache.

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IV- Preliminary Results

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GoalsSimulate LRU & LFU Algorithms,Compare cache hit ratio,Evaluate overhead of each algorithm.

AssumptionsWe ignore TB size & cache size,Quota of retained entries is 1/5,Cache size is just limited by number of TB,

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GoalsSimulate LRU & LFU Algorithms,Compare cache hit ratio,Evaluate overhead of each algorithm.

AssumptionsWe ignore TB size & cache size,Quota of retained entries is 1/5,Cache size is just limited by number of TB,

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Execution ratio = (executions/translation)

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LFU cache hit ratio

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LRU cache hit ratio

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Perspectives

find a suitable cache replacement policy that take care of implementationconstraints.use a dynamically variable quota for retained entries.add small op-code buffer to optimize re-translation of self modifying code.divide translation cache into multiple space to optimize partial cache flush.

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Bibliography

QEMU Just-In-Time Code Generator and System Emulation - cmchao(March 15,2010).QEMU internals - Chad D. Kersey (January 28, 2009).QEMU, a Fast and Portable Dynamic Translator - Fabrice Bellard (USENIX2005 Annual).Performance Evaluation of Traditional Caching Policies on A Large Systemwith Petabytes of Data - 2012 IEEE Seventh International Conference onNetworking, Architecture, and Storage

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Thanks for your attention !

Feel free to ask any question !

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