Language virtual machines are now everywhere, from server to mobile applications through web pages. The main ones are developed and maintained by large industrial groups (Oracle and the JVM, Microsoft and .Net, Google and V8, etc.). The talk introduces the different components of a virtual machine (Interpreter, JIT compiler, Memory Manager) and the key challenges to build a good one. Then, the talk details the current and incoming research going-on I am doing performing this topic.

1. Challenges of High-
performance Language
Virtual Machines
Clément Béra
Pharo Consortium engineer
RMoD - Inria Lille Nord Europe

2. About Me
Working on Language Virtual Machines for 5+ years
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Before 2012 Master Mines de Douai
2012 Inria engineer
2014 Ph.D
2017 Pharo consortium engineer
2018 + Post-Doc & Applying for CR position

3. Language Virtual Machines?
Challenge 1: Fast execution speed
Challenge 2: Efficient memory management
Challenge 3: Stability
Future research
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4. 4
Language Virtual Machines?
Challenge 1: Fast execution speed
Challenge 2: Efficient memory management
Challenge 3: Stability
Future research

5. What?
VM: Virtual Machine
Simulation of a specific machine
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OS VM Language VM

6. Where?

7. Servers: Java, C# applications
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8. Mobile: Android applications
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9. Web: Javascript
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10. Sciences: R, MathLab, Maple
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11. Army: planes
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12. Where?
Basically everywhere

13. Why?
“Write once, run anywhere"
Application cross-platform
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OS Processor

14. Who?
+ a few research teams around the world
but… not really in France
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Java VM Oracle, IBM
.Net (C# VM) Microsoft
Javascript VM Google, Mozilla, Apple, Microsoft
Android VM Google

15. Architecture
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Ch. 1: Fast execution speed
Interpreter & JIT compiler
Execution Stack Management
Ch. 2: Efficient memory
management
Garbage Collector
Foreign Function Interface
Call to dynamic libraries
Platform
OS integration
File Management
Native UIs
Ch. 3: Stability

16. 16
Language Virtual Machines?
Challenge 1: Fast execution speed
Challenge 2: Efficient memory management
Challenge 3: Stability
Future research

17. Challenge 1: Fast
execution speed
Language VM executes the Java, Smalltalk code
Execution is done by interpreter, quite slow
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Speed-up: Just-In-Time compiler (JIT)
Detects frequently used code & recompiles
them at runtime to efficient machine code

18. Speculative JIT compilation
Deoptimization
guard
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elseif elseif
Not used so far

19. 19
Deoptimization
Optimized State Deoptimized State
Deoptimized State
JIT generates single optimized function
from multiple non-optimized functions
Execution Stack recovery

20. Research: Time to reach
peak performance
2020
Executing code using low speed interpreter
Compiling at runtime
Deoptimizing & reoptimizing

21. Persist optimized code
2121
Java, Smalltalk code to execute
Persistent intermediate representation
compact, platform-independent
Machine code
JIT phase 1: Speculative optimizations
JIT phase 2: Low-level optimizations

22. 22
Language Virtual Machines?
Challenge 1: Fast execution speed
Challenge 2: Efficient memory management
Challenge 3: Stability
Future research

23. Challenge 2: Efficient
Memory Management
Garbage collection
Find & Free unused memory
Challenges
Low memory consumption
Responsiveness (Concurrent?)
Fragmentation
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24. Advanced debugging features
Changing memory layout
Add a field to object 2
Not enough space locally
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Object 1
Object 2
Object 3
Pointer from
another object
Pointer from
an interpreter
variable

25. Forwarding objects
Forwarding object
Problem: read-barrier
Solution: partial read-barrier
(Maintain invariant, avoids 99% of checks)
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Object 1
Fwd
Object 3
Pointer from
another object
Pointer from
an interpreter
variable
Object 2

26. 26
Language Virtual Machines?
Challenge 1: Fast execution speed
Challenge 2: Efficient memory management
Challenge 3: Stability
Future research

27. Challenge 3: Stability
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Cross-platform
Fast execution speedHigh responsiveness
Low memory footprint

28. Challenge 3: Stability
Software engineering stability techniques
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Continuous integration
Automatic build
Automatic testing
Benchmarks

29. Research: static analysis
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Deoptimization uncommon
Symbolic execution of the same code
at different optimization levels
Symbolic
non
optimised
state
Symbolic
optimised
state
Symbolic
deoptimised
state
Deoptimization Symbolic
values
comparison

30. 30
Language Virtual Machines?
Challenge 1: Fast execution speed
Challenge 2: Efficient memory management
Challenge 3: Stability
Future research

31. Future research
Debugging
distributed applications

32. Distributed Server app
(Micro-services, AWS)
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Virtual Machine
Physical Machine

33. Internet of Things
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Room / House
IoT: Sensor, ... Local micro-server

34. High performance computing
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DataCenter
GPU CPUs TPUs
FPGAs

35. Distributed Applications
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DataCenter
GPU CPUs TPUs
FPGAs
Room / House
IoT: Sensor, ... Local micro-serverVirtual Machine
Physical Machine

36. Future: Debugging
distributed applications
VM with advanced debugging features
Monitor the asynchronous communication,
Remote debugging
Challenge: Recreate the execution flow
Different hardware, execution models,
life-time, vocabulary
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37. Conclusion & Questions
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Language VMs are used everywhere
Complex architecture:
High execution speed, Low memory consumption,
High responsiveness, Cross-platform
Research on VM performance, stability
memory management, debugging