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Adaptive Code Unloading for Resource-Constrained JVMs

ACM SIGPLAN, 2004

Presented by

Mithuna Soundararaj

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Outline

Introduction

Motivation and Contributions

Compile-only and Interpreter-only JVM Code Unloading What to Unload? When to Unload? Results

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Introduction

Java Virtual machine extension for adaptive code unloading.

Reduces memory requirement imposed by compile-only JVMs.

The extension feature, an unloader uses execution behavior to adaptively determine *when* and *what* code to unload.

Unloading strategies used reduces the code size, in turn reducing the execution time and memory.

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Motivation

To reduce the overhead of memory consumed by compiled code which increases the cost of memory management.

To adaptively balance not storing any code and caching all generated code according to the dynamic memory availability.

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Compile-only Vs Interpreter JVM

Compile-only JVM

-Uses device resources more efficiently.

-Higher quality code.

-Code reuse and optimization. Interpreter-only JVM

-Simple to interpret.

-No memory overhead

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Code Unloading opportunities

Large amount of executed code used only during program start up( initial 10% of execution time).

Code that remains in the system and is never invoked after start up.(shorter lifetimes).

Long-lived methods executed in-frequently during their lifetime.

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Code Unloading

To exploit the available code unloading opportunities and to relieve the memory pressure imposed by compile-only JVMs, they have developed an extensible framework for the implementation of strategies that decide

-What to unload?-When to unload?-Balance between memory pressure and

recompilation overhead.

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What to Unload?

Predicting methods which are unlikely to be invoked in the future.

Monitor the execution to identify the methods that have not been recently invoked by

-Online eXhaustive profiling (OnX)

-Online Sample-based profiling (OnS)

-Offline exhaustive profiling (Off)

-No profiling (NP).

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What strategies

Online eXhaustive profiling(OnX)

Compiler instruments methods setting a mark bit to one every time a method is invoked.

Online Sample-Based profiling(OnS)

Sets the mark bits of the two methods on the top of the invocation stack.

Offline Exhaustive Profiling(Off) To analyze the behavior of the methods offline No Profiling unload all methods that are not currently on the run

time stack when unloading occurs.

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When to unload?

Timer Triggered Approach Garbage Collection triggered Strategy. Maximum Call Times Triggered(MCT). Code Cache Size Triggered(CS).

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When Strategies

Timer Triggered Approach. Unloads code at regular intervals Does not account for the dynamically

changing memory availability. Approximates time using thread switch

count which occurs every 10 ms. Increments count according to time

spent in GC.

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When Strategies

Adaptive Garbage Collection Triggered Strategy -Unloading trigger of GC invocation count. -Frequency of code unloading dynamically adapts to

resource behavior. -Unloading more frequently when memory is highly

constrained but less frequent otherwise. -Memory Usage behavior is got from heap residency. -At the end of each GC cycle, resource monitor

forwards the percentage of execution time that is spent in GC to the unloader so as to adjust the frequency of the unloading sessions.

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When Strategies

Maximum Call Times Triggered(MCT)

-unloads a method after its final compilation.

Code Cache Size Triggered

-Stores native code bodies in a fixed size code cache, when cache becomes full it is unloaded.

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Unloading Optimized Code

Recompiling an unloaded,previously optimized method affects the balance of re-compilation overhead and performance benefits.

Strategies to handle optimized code -RO(Reload Optimized methods using

the optimization hint). -EO(Exclude unloading of optimized

methods) -DO(Delay unloading of optimized

methods)

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RESULTS

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Impact On Memory Footprint

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Impact On Execution Performance

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Conclusion

Opportunity for dynamically unloading compiled code in JIT-based JVMs for mobile and embedded devices.

Adaptively unload dead and infrequently used code to reduce memory consumed and improve performance.

When memory is highly constrained,reduction in code size by 61% and execution time of 23% on an average across benchmark programs and JVM configurations.

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Thank You