1. Adaptive Code Unloading for Resource-Constrained JVMs
ACM SIGPLAN, 2004
Presented by
Mithuna Soundararaj
12/2/2018
University of Nebraska-Lincoln

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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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University of Nebraska-Lincoln

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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.
12/2/2018
University of Nebraska-Lincoln

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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.
12/2/2018
University of Nebraska-Lincoln

5. 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
12/2/2018
University of Nebraska-Lincoln

6. 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.
12/2/2018
University of Nebraska-Lincoln

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12/2/2018
University of Nebraska-Lincoln

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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.
12/2/2018
University of Nebraska-Lincoln

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12/2/2018
University of Nebraska-Lincoln

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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).
12/2/2018
University of Nebraska-Lincoln

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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.
12/2/2018
University of Nebraska-Lincoln

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When to unload?
Timer Triggered Approach
Garbage Collection triggered Strategy.
Maximum Call Times Triggered(MCT).
Code Cache Size Triggered(CS).
12/2/2018
University of Nebraska-Lincoln

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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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University of Nebraska-Lincoln

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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.
12/2/2018
University of Nebraska-Lincoln

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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.
12/2/2018
University of Nebraska-Lincoln

16. 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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University of Nebraska-Lincoln

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RESULTS
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18. Impact On Memory Footprint
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20. 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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University of Nebraska-Lincoln

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Thank You
12/2/2018
University of Nebraska-Lincoln