From cce0fc068e6982253e6a37e10f56f34c04107cad Mon Sep 17 00:00:00 2001
From: Holger Brunst <holger.brunst@tu-dresden.de>
Date: Thu, 1 Sep 2022 12:51:56 +0200
Subject: [PATCH] Added initial complete version of new performance engineering
overview document.
---
.../misc/performance_engineering_cycle.svg | 312 ++++++++++++++++++
.../performance_engineering_overview.md | 268 +++++++++++++++
.../software/software_development_overview.md | 6 +-
doc.zih.tu-dresden.de/mkdocs.yml | 1 +
doc.zih.tu-dresden.de/wordlist.aspell | 5 +-
5 files changed, 586 insertions(+), 6 deletions(-)
create mode 100644 doc.zih.tu-dresden.de/docs/software/misc/performance_engineering_cycle.svg
create mode 100644 doc.zih.tu-dresden.de/docs/software/performance_engineering_overview.md
diff --git a/doc.zih.tu-dresden.de/docs/software/misc/performance_engineering_cycle.svg b/doc.zih.tu-dresden.de/docs/software/misc/performance_engineering_cycle.svg
new file mode 100644
index 000000000..6cbe93112
--- /dev/null
+++ b/doc.zih.tu-dresden.de/docs/software/misc/performance_engineering_cycle.svg
@@ -0,0 +1,312 @@
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diff --git a/doc.zih.tu-dresden.de/docs/software/performance_engineering_overview.md b/doc.zih.tu-dresden.de/docs/software/performance_engineering_overview.md
new file mode 100644
index 000000000..972b31b1b
--- /dev/null
+++ b/doc.zih.tu-dresden.de/docs/software/performance_engineering_overview.md
@@ -0,0 +1,268 @@
+# Performance Engineering Overview
+
+!!! cite "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]](https://en.wikipedia.org/wiki/Performance_engineering).
+
+[^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
+
+## Objectives
+
+??? hint "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
+
+| | Task | Easiness | Details | Overhead | Re-compilation |
+|----------------------|----------------------------------------------|-------------|----------|-----------|-----------------
+| [lo2s](#lo2s) | Create performance [trace](#trace) | easy | medium | low | (no)[^2] |
+| [MUST](#must) | Check MPI correctness | medium | medium | variable | no |
+| [PAPI](#papi) | Read portable CPU counters | advanced | medium | variable | yes |
+| [Perf](#perf-tools) | Produce and visualize [profile](#profile) | easy | medium | low | (no)[^2] |
+| [PIKA](#pika) | Show performance [profile](#profile) and [trace](#trace) | very easy | low | very low | no |
+| [Slurm](#slurm-profiler) | Produce and visualize simple [trace](#trace)| easy | low | low | no |
+| [Score-P](#score-p) | Create performance [trace](#trace) | complex | high | variable | yes |
+| [Vampir](#vampir) | Visualize performance [trace](#trace) | complex | high | n.a. | n.a. |
+
+[^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.
+
+## Approach and Terminology
+
+Performance engineering typically is a cyclic process.
+The following figure shows such a process and its potential stages.
+
+
+
+### Instrumentation
+
+!!! hint "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.
+
+### Measurement
+
+!!! note "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.
+
+#### Profile
+
+!!! hint "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% |
+
+#### Trace
+
+<!-- markdownlint-disable-next-line line-length -->
+!!! hint "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](#profile), 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()` |
+
+<!-- markdownlint-disable-next-line line-length -->
+!!! hint "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.
+
+### Analysis
+
+!!! note "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.
+
+### Presentation
+
+!!! note "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.
+
+### Evaluation
+
+!!! note "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:
+
+### lo2s
+
+!!! hint "Easy to use application and system performance trace recorder supporting Vampir"
+
+[lo2s](lo2s.md) records the status of an application at fixed intervals (statistical sampling).
+It does not require any [instrumentation](#instrumentation).
+The [measurement](#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](#trace) repository at the current directory.
+See [lo2s](lo2s.md) for further details.
+Once the data have been recorded, the tool [Vampir](vampir.md) needs to be invoked to study the data
+graphically.
+
+### MUST
+
+<!-- markdownlint-disable-next-line line-length -->
+!!! hint "Advanced communication error detection for applications using the Message Passing Interface (MPI) standard."
+
+[MUST](mpi_usage_error_detection.md) checks your application for communication errors if the MPI
+library is used.
+It does not require any [instrumentation](#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.
+
+### PAPI
+
+!!! hint "Portable reading of CPU performance metrics like FLOPS"
+
+The [PAPI](papi.md) library allows software developers to read CPU performance counters in a
+platform-independent way.
+Native usage of the library requires to manually [instrument](#instrumentation) an application by
+adding library calls to the source code of the application under investigation.
+Data [measurement](#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](#score-p) have built-in support for PAPI.
+Therefore, native usage of the PAPI library is usually not needed.
+
+### Perf Tools
+
+!!! hint "Easy to use Linux-integrated performance data recording and analysis"
+
+[Linux perf](perf_tools.md) reads and analyses CPU performance counters for any given application.
+It does not require any [instrumentation](#instrumentation).
+The [measurement](#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](#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](perf_tools.md) for further details.
+
+### PIKA
+
+!!! hint "Very easy to use performance visualization of entire batch jobs"
+
+[PIKA](pika.md) allows users to study their active and completed
+[batch jobs](../jobs_and_resources/slurm.md).
+It does not require any [instrumentation](#instrumentation).
+The [measurement](#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](https://selfservice.tu-dresden.de/services/1663599/).
+
+### Score-P
+
+!!! hint "Complex and powerful performance data recording and analysis of parallel applications"
+
+[Score-P](scorep.md) is an advanced tool that measures configurable performance event data.
+It generates both [profiles](#profile) and detailed [traces](#trace) for subsequent analysis.
+It supports automated [instrumentation](#instrumentation) of an application (involves
+re-compilation) prior to the [measurement](#measurement) step.
+The data analysis of the raw performance data can be carried out with the tools `scalasca`
+(advanced MPI metrics), `cube` ([profile](#profile) viewer), `scorep-score` ([profile](#profile)
+command line viewer), or [Vampir](#vampir) ([trace](#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](scorep.md) for further details.
+
+### Slurm Profiler
+
+!!! hint "Easy to use performance visualization of entire batch jobs"
+
+The [Slurm Profiler](../jobs_and_resources/slurm_profiling.md) gathers performance data from every
+task/node of a given [batch job](../jobs_and_resources/slurm.md).
+It records a coarse-grained [trace](#trace) for subsequent analysis.
+[Instrumentation](#instrumentation) of the applications under test is not needed.
+The data analysis of the given set of system metrics needs to be initiated by the user with a
+command line interface.
+The resulting performance metrics are accessible in a simple graphical front-end that provides
+time/performance graphs.
+
+### Vampir
+
+!!! hint "Complex and powerful performance data visualization of parallel applications"
+
+[Vampir](vampir.md) is a graphical analysis tool that provides a large set of different chart
+representations for performance data [traces](#trace) generated by tools such as
+[Score-P](scorep.md) or [lo2s](lo2s.md).
+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.
diff --git a/doc.zih.tu-dresden.de/docs/software/software_development_overview.md b/doc.zih.tu-dresden.de/docs/software/software_development_overview.md
index ed38b2b5d..04471fb13 100644
--- a/doc.zih.tu-dresden.de/docs/software/software_development_overview.md
+++ b/doc.zih.tu-dresden.de/docs/software/software_development_overview.md
@@ -13,11 +13,7 @@ It will tell you:
- How to deal with (or even prevent) bugs
- [Find caveats and hidden errors in MPI application codes](mpi_usage_error_detection.md)
- [Using debuggers](debuggers.md)
-- How to investigate the performance and efficiency of your code
- - [Pika: monitoring of batch jobs](pika.md)
- - [Perf: sampling-based performance analysis](perf_tools.md)
- - [Score-P: event tracing of HPC applications](scorep.md)
- - [Vampir: trace visualization](vampir.md)
+- [How to investigate the performance and efficiency of your code](performance_engineering_overview.md)
Some hints that are helpful:
diff --git a/doc.zih.tu-dresden.de/mkdocs.yml b/doc.zih.tu-dresden.de/mkdocs.yml
index 4b5e8ce98..604067175 100644
--- a/doc.zih.tu-dresden.de/mkdocs.yml
+++ b/doc.zih.tu-dresden.de/mkdocs.yml
@@ -82,6 +82,7 @@ nav:
- Mathematics Libraries: software/math_libraries.md
- Debugging: software/debuggers.md
- Software Engineering Tools:
+ - Overview: software/performance_engineering_overview.md
- MPI Error Detection: software/mpi_usage_error_detection.md
- Score-P: software/scorep.md
- lo2s: software/lo2s.md
diff --git a/doc.zih.tu-dresden.de/wordlist.aspell b/doc.zih.tu-dresden.de/wordlist.aspell
index 868eaafc9..08d177bd1 100644
--- a/doc.zih.tu-dresden.de/wordlist.aspell
+++ b/doc.zih.tu-dresden.de/wordlist.aspell
@@ -1,9 +1,10 @@
-personal_ws-1.1 en 471
+personal_ws-1.1 en 475
Abaqus
ACL
ACLs
Addon
Addons
+AFIPS
ALLREDUCE
Altix
Amber
@@ -75,6 +76,7 @@ Dockerfile
Dockerfiles
DockerHub
dockerized
+Doherty
DOI
don'ts
dotfile
@@ -432,6 +434,7 @@ tracefiles
tracepoints
transferability
Trition
+TSS
TUD
und
undistinguishable
--
GitLab