Performance Optimization: Simulation and Real Measurement
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1 Performance Optimization: Simulation and Real Measurement KDE Developer Conference,
2 Introduction Agenda Performance Analysis Profiling Tools: Examples & Demo KCachegrind: Visualizing Results What s to come 2
3 Introduction Why Performance Analysis in KDE? Key to useful Optimizations Responsive Applications required for Acceptance Not everybody owns a 3 GHz About Me Supporter of KDE since Beginning ( KAbalone ) Currently at TU Munich, working on Cache Optimization for Numerical Code & Tools 3
4 Introduction Agenda Performance Analysis Basics, Terms and Methods Hardware Support Profiling Tools: Examples & Demo KCachegrind: Visualizing Results What s to come 4
5 Performance Analysis Why to use Locate Code Regions for Optimizations (Calls to time-intensive Library-Functions) Check for Assumptions on Runtime Behavior (same Paint-Operation multiple times?) Best Algorithm from Alternatives for a given Problem Get Knowledge about unknown Code (includes used Libraries like KDE-Libs/QT) 5
6 Performance Analysis (Cont d) How to do At End of (fully tested) Implementation On Compiler-Optimized Release Version With typical/representative Input Data Steps of Optimization Cycle Start Measurement Locate Bottleneck Modify Code Finished Yes No Improvement Satisfying? Check for Improvement (Runtime) 6
7 Performance Analysis (Cont d) Performance Bottlenecks (sequential) Logical Errors: Too often called Functions Algorithms with bad Complexity or Implementation Bad Memory Access Behavior (Bad Layout, Low Locality) Too low-level for GUI Applications? Lots of (conditional) Jumps, Lots of (unnecessary) Data Dependencies,... 7
8 Performance Measurement Wanted: Time Partitioning with Reason for Performance Loss (Stall because of ) Detailed Relation to Source (Code, Data Structure) Runtime Numbers Call Relationships, Call Numbers Loop Iterations, Jump Counts No Perturbation of Results b/o Measurement 8
9 Measurement - Terms Trace: Stream of Time-Stamped Events Enter/Leave of Code Region, Actions, Example: Dynamic Call Tree Huge Amount of Data (Linear to Runtime) Unneeded for Sequential Analysis (?) 9
10 Measurement Terms (Cont d) Profiling (e.g.time Partitioning) Summary over Execution Exclusive, Inclusive Cost / Time, Counters Example: DCT DCG (Dynamic Call Graph) Amount of Data Linear to Code Size 10
11 Methods Precise Measurements Increment Counter (Array) on Event Attribute Counters to Code / Data Data Reduction Possibilities Selection (Event Type, Code/Data Range) Online Processing (Compression, ) Needs Instrumentation (Measurement Code) 11
12 Methods - Instrumentation Manual Source Instrumentation Library Version with Instrumentation Compiler Binary Editing Runtime Instrumentation / Compiler Runtime Injection 12
13 Methods (Cont d) Statistical Measurement ( Sampling ) TBS (Time Based), EBS (Event Based) Assumption: Event Distribution over Code Approximated by checking every N-th Event Similar Way for Iterative Code: Measure only every N-th Iteration Data Reduction Tunable Compromise between Quality/Overhead 13
14 Simulation Methods (Cont d) Events for (not existant) HW Models Results not influenced by Measurement Compromise Quality / Slowdown Rough Model = High Discrepancy to Reality Detailed Model = Best Match to Reality But: Reality (CPU) often unknown Allows for Architecture Parameter Studies 14
15 Hardware Support Monitor Hardware Event Sensors (in CPU, on Board) Event Processing / Collection / Storing Best: Separate HW Comprimise: Use Same Resources after Data Reduction Most CPUs nowadays include Performance Counters 15
16 Performance Counters Multiple Event Sensors ALU Utilization, Branch Prediction, Cache Events (L1/L2/TLB), Bus Utilization Processing Hardware Counter Registers Itanium2: 4, Pentium-4: 18, Opteron: 8 Athlon: 4, Pentium-II/III/M: 2, Alpha 21164: 3 16
17 Performance Counters (Cont d) Two Uses: Read Get Precise Count of Events in Code Regions by Enter/Leave Instrumentation Interrupt on Overflow Allows Statistical Sampling Handler Gets Process State & Restarts Counter Both can have Overhead Often Difficult to Understand 17
18 Introduction Agenda Performance Analysis Profiling Tools: Examples & Demo Callgrind/Calltree OProfile KCachegrind: Visualizing Results What s to come 18
19 Tools - Measurement Read Hardware Performance Counters Specific: PerfCtr (x86), Pfmon (Itanium), perfex (SGI) Portable: PAPI, PCL Statistical Sampling PAPI, Pfmon (Itanium), OProfile (Linux), VTune (commercial - Intel), Prof/GProf (TBS) Instrumentation GProf, Pixie (HP/SGI), VTune (Intel) DynaProf (Using DynInst), Valgrind (x86 Simulation) 19
20 Tools Example 1 GProf (Compiler generated Instr.): Function Entries increment Call Counter for (caller, called)-tupel Combined with Time Based Sampling Compile with gcc pg... Run creates gmon.out Analyse with gprof... Overhead still around 100%! Available with GCC on UNIX 20
21 Tools Example 2 Callgrind/Calltree (Linux/x86), GPL Cache Simulator using Valgrind Builds up Dynamic Call Graph Comfortable Runtime Instrumentation Disadvantages Time Estimation Inaccurate (No Simulation of modern CPU Characteristics!) Only User-Level 21
22 Tools Example 2 (Cont d) Callgrind/Calltree (Linux/x86), GPL Run with callgrind prog Generates callgrind.out.xxx Results with callgrind_annotate or kcachegrind Cope with Slowness of Simulation: Switch of Cache Simulation: --simulate-cache=no Use Fast Forward : --instr-atstart=no / callgrind_control i on DEMO: KHTML Rendering 22
23 Tools Example 3 OProfile Configure (as Root: oprof_start, ~/.oprofile/daemonrc) Start the OProfile daemon (opcontrol -s) Run your code Flush Measurement, Stop daemon (opcontrol d/-h) Use tools to analyze the profiling data opreport: Breakdown of CPU time by procedures (better: opreport gdf op2calltree) DEMO: KHTML Rendering 23
24 Introduction Agenda Performance Analysis Profiling Tools: Examples & Demo KCachegrind: Visualizing Results Data Model, GUI Elements, Basic Usage DEMO What s to come 24
25 KCachegrind Data Model Hierarchy of Cost Items (=Code Relations) Profile Measurement Data Profile Data Dumps Function Groups: Source files, Shared Libs, C++ classes Functions Source Lines Assembler Instructions 25
26 KCachegrind GUI Elements List of Functions / Function Groups Visualizations for an Activated Function DEMO 26
27 Introduction Agenda Performance Analysis Profiling Tools: Examples & Demo KCachegrind: Visualizing Results What s to come Callgrind KCachegrind 27
28 Callgrind What s to come Free definable User Costs ( MyCost += arg1 on Entering MyFunc) Relation of Events to Data Objects/Structures More Optional Simulation (TLB, HW Prefetch) 28
29 What s to come (Cont d) KCachegrind Supplement Sampling Data with Inclusive Cost via Call-Graph from Simulation Comparation of Measurements Plugins for Interactive Control of Profiling Tools Visualizations Visualizations for Data Relation 29
30 Finally THANKS FOR LISTENING 30
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