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Performance Engineering Overview

Walter J. Doherty, 1970 1

Fundamentally, performance is the degree to which a computing system meets the expectations of the person involved with it.

Performance engineering encompasses the techniques applied during a systems development life cycle to ensure the non-functional requirements for performance (such as throughput, latency, or memory usage) will be met. Often, it is also referred to as systems performance engineering within systems engineering, and software performance engineering or application performance engineering within software engineering [Wikipedia].


Some good reasons to think about performance in HPC
  • Increase research output by ensuring the system can process transactions within the requisite time frame
  • Eliminate system failure requiring scrapping and writing off the system development effort due to performance objective failure
  • Eliminate avoidable system tuning efforts
  • Avoid additional and unnecessary hardware acquisition costs
  • Reduce increased software maintenance costs due to performance problems in production
  • Reduce additional operational overhead for handling system issues due to performance problems
  • Identify future bottlenecks by simulation over prototype

Installed Tools in a Nutshell

Tool Task Easiness Details Overhead Re-compilation
lo2s Create performance trace easy medium low (no)2
MUST Check MPI correctness medium medium variable no
PAPI Read portable CPU counters advanced medium variable yes
Perf Produce and visualize profile easy medium low (no)2
PIKA Show performance profile and trace very easy low very low no
Score-P Create performance trace complex high variable yes
Vampir Visualize performance trace complex high n.a. n.a.

Approach and Terminology

Performance engineering typically is a cyclic process. The following figure shows such a process and its potential stages.

Performance engineering cycle


Instrumentation is a common term for preparing the performance measurement

The engineering process typically begins with the original application in its unmodified state. First, this application needs to be instrumented, i.e. it must be prepared to enable the measurement of the performance properties. There are different ways to do this, including manual instrumentation of the source code by the user, automatic instrumentation by the compiler, linking against pre-instrumented libraries, or interrupt-driven sampling during run time.


During measurement, raw performance data is collected

When an instrumented application is executed, the additional instructions introduced during the instrumentation phase collect and record the data required to evaluate the performance properties of the code. Unfortunately, the measurement itself has a certain influence on the performance of the instrumented code. Whether the perturbations introduced have a significant effect on the behavior depends on the specific structure of the code to be investigated. In many cases, the perturbations will be rather small, so that the overall results can be considered to be a realistic approximation of the corresponding properties of the non-instrumented code. Yet, it is always advisable to compare the runtime of instrumented applications with their original non-instrumented counterpart.


Performance profiles hold aggregated data (e.g. total time spent in function foo())

A performance profile provides aggregated metrics like time or number of calls for a list of functions, loops or similar as depicted in the following table:

Function Total Time Calls Percentage
main() 2 s 1 1%
foo() 80 s 100 40%
bar() 118 s 9000 59%


Traces consist of a sorted list of timed application events/samples (e.g. enter function foo() at 0.11 s).

In contrast to performance profiles, performance traces consist of individual application samples or events that are recorded with a timestamp. A trace that corresponds to the profile recording above could look as follows:

Timestamp Data Type Parameter
0.10 s Enter Function main()
0.11 s Enter Function foo()
0.12 s Enter Function bar()
0.15 s Exit Function bar()
0.16 s Enter Function bar()
0.17 s Exit Function bar()
many more events...
200.00 s Exit Function main()

Traces enable more sophisticated analysis at the cost of potentially very large amounts of raw data.

Apparently, the size of a performance trace depends on the recorded time whereas a profile does not. Likewise, a trace can tell you when a specific action in your application happened whereas a profile will tell you how much time in total a class of actions takes.


Well defined performance metrics are derived from raw performance data during analysis

The collected raw data is typically processed by a analysis tool (profiler, consistency checker, you name it) to derive meaningful, well-defined performance metrics like data rates, data dependencies, performance events of interest, etc. This step is typically hidden to the user and taken care of automatically once the raw data was collected. Some tools, however, provide an independent analysis front-end that allows specifying the type of analysis to carry out on the raw data.


Presenting performance metrics graphically fosters human intuition

After processing the raw performance data, the resulting metrics are usually presented in the form of a report that makes use of tables or charts known from programs like Excel. In this step, the reduction of the data complexity simplifies the evaluation of the data by software developers. Yet, data reductions have the potential to hide important facts or details.


The evaluation of performance metrics requires tools and lots of thinking

During the evaluation phase, the metrics and findings in a performance report are compared to the behavior/performance as expected by software developers. This step typically requires a fair amount of knowledge about the application under test or software performance in general. The application is considered to behave sufficiently well or weaknesses have been identified which potentially can be improved. An application or its configuration is changed in the later case. After evaluating an application's performance, the cyclic engineering process is either completed or restarted from beginning.

Installed Tools Summary

At ZIH, the following performance engineering tools are installed and maintained:


Easy to use application and system performance trace recorder supporting Vampir

lo2s records the status of an application at fixed intervals (statistical sampling). It does not require any instrumentation. The measurement of a given application is done by pre-fixing the application's executable with lo2s. The data analysis of the fixed metrics is fully integrated and does not require any user actions. Performance data is written to a traces repository at the current directory. See lo2s for further details. Once the data have been recorded, the tool Vampir needs to be invoked to study the data graphically.


Advanced communication error detection for applications using the Message Passing Interface (MPI) standard.

MUST checks your application for communication errors if the MPI library is used. It does not require any instrumentation. The checks of a given MPI application are done by simply replacing srun with mustrun when the application is started. The data analysis of the fixed metrics is fully integrated and does not require any user actions. The correctness results are written to an HTML-formatted output file, which can be inspected with a web browser.


Portable reading of CPU performance metrics like FLOPS

The PAPI library allows software developers to read CPU performance counters in a platform-independent way. Native usage of the library requires to manually instrument an application by adding library calls to the source code of the application under investigation. Data measurement happens whenever the PAPI library is called. The data obtained is raw data. Software developers have to process the data by themselves to obtain meaningful metrics. Tools like Score-P have built-in support for PAPI. Therefore, native usage of the PAPI library is usually not needed.

Perf Tools

Easy to use Linux-integrated performance data recording and analysis

Linux perf reads and analyses CPU performance counters for any given application. It does not require any instrumentation. The measurement of a given application is done by simply prefixing the application executable with perf. Perf has two modes of operation (perf stat, perf record), which both record profile raw data. While the first mode is very basic, the second mode records more data. Use perf report to analyze the raw output data of perf record and produce a performance report. See Linux perf for further details.


Very easy to use performance visualization of entire batch jobs

PIKA allows users to study their active and completed batch jobs. It does not require any instrumentation. The measurement of batch jobs happens automatically in the background for all batch jobs. The data analysis of the given set of metrics is fully integrated and does not require any user actions. Performance metrics are accessible via the PIKA web service.


Complex and powerful performance data recording and analysis of parallel applications

Score-P is an advanced tool that measures configurable performance event data. It generates both profiles and detailed traces for subsequent analysis. It supports automated instrumentation of an application (involves re-compilation) prior to the measurement step. The data analysis of the raw performance data can be carried out with the tools scalasca (advanced MPI metrics), cube (profile viewer), scorep-score (profile command line viewer), or Vampir (trace viewer). Many raw data sources are supported by Score-P. It requires some time, training, and practice to fully benefit from the tool's features. See Score-P for further details.


Complex and powerful performance data visualization of parallel applications

Vampir is a graphical analysis tool that provides a large set of different chart representations for performance data traces generated by tools such as Score-P or lo2s. Complex statistics, timelines, and state diagrams can be used by software developers to obtain a better understanding of the inner working of a parallel application. The tool requires some time, training, and practice to fully benefit from its rich set of features.

  1. Scheduling TSS/360 for responsiveness. In: AFIPS '70 (Fall): Proceedings of the November 17-19, 1970, fall joint computer conference, November 1970, Pages 97–111 

  2. Re-compilation is not required. Yet, to obtain more details it is recommended to re-compile with the -g compiler option, which adds debugging information to the executable of an application.