1. Vertical Profiling : Understanding the Behavior of Object-Oriented Applications Sookmyung Women’s Univ. PsLab Sewon,Moon

2. 1. Content Introduction Fundamental concept Implementation Case Studies

3. 1.Introduction : object-oriented programming languages. Benefit  Increased runtime flexibility Reflection, automatic memory management  improved security properties Null pointer and array bounds checks, security policies  portable deployment representation introduces an additional layer of virtualization, between the application and the OS. Defect  Introduces obstacles to understanding application performance.

4. C program  Application  Operating System  Hardware Java /.net program  Application  Framework  Java Library  Virtual Machine  Native Library  Operating System  Hardware 1.Introduction : object-oriented programming languages.

5. 1.Introduction : Veritcal Profiling Verital Profiling  gather more complete profiles, containing information about system behavior on various levels  to further the understanding of system behavior through correlation of profile information from different level

6. 2. fundamental concept Events  atomic occurrence in time that does not have any duration  Usually causes the system to change to a different state. States  captures the values in all of memory Monitors  obseves the behavior of a system  scalar variable with a value that changes over time  hardware / sofeware performance monitors Counters  special kind of performance monitor  usually a count of events

7. 2. fundamental concept Sofeware Performance Monitors  observe the behaviour in the layers above the hardware  cover the following parts of the system Application Vitual Machine Memory Manager Runtime Compilers Synchronization Operating System

8. 3. Implementation : Jikes RVM open source research virtual machine that executes java bytecodes Jikes RVM  is implemented in the Java programming language  uses Java threads to implement several subsystems, such as the garbage collector and the adaptive optimization system

9. 3. Implementation : Jikes RVM

10. 3. Implementation hardware performance monitors  processor cycles, L1 cache misses.. software performance monitors  In native code  In Java code generates a trace file for each Jikes RVM virtual processor, and one meta file  trace file contains a sereies of trace records Virtual Processor ID /Compiled Method IDs Thread ID / Thread Yield status Real Time / Real Time Duration Thread Switch Status / Monitor Values  Meta file is generated in conjunction with benchmark’s trace files.

11. 3. Implementation Performance Analysis  metrics  sample lists  statistics  visualizations Use IBM Performance Explorer  performance visualization and analysis tool

12. 4. Case Studies

13. Benchmarks : SPECjbb2000 VM : Jikes RVM Initialization  1 thread  120000 transactions  50 transactions per time slice

14. 4. Case Studies The changes in application behavior during a transaction are not captured in the signal Thus, they expect tha the gradual increase in IPC is not due to the transaction behavior, but due to some other lower-level behavior In prior work they speculated that jbb’s gradual increase in IPC occurs because as jbb runs, more and more of its code gets optimized.  Optimized code had a higher IPC than unoptimized code  the number of flushes in the load/store unit went down when the IPC increased.

15. 4. Case Studies Exploration  Need to measure the amount of time spent in optimized code and unoptimized code for each thme slice  Use MonitorEnter bytecode instructions  Two JVM performance monitor UnoptMonitorEnter OptMonitorEnter

16. 4. Case Studies