Overview of AIMS

Hans Sherburne

UPC Group

HCS Research Laboratory

University of Florida

Color encoding key:

Blue: Information

Red: Negative note

Green: Positive note

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Basic Information Name: Automated Instrumentation and Monitoring

System Developer: NAS (NASA) Current Version:

AIMS V3.7.2 (last updated in ’99)

Website: http://www.nas.nasa.gov/Groups/Tools/Projects/AIMS/

Contact: ?

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AIMS Overview

AIMS was not chosen for a full evaluation Not updated since ’99 Support for MPI and PVM programs written in C and

FORTRAN Unfortunately support for C programs and Linux seems to

have only been in initial stages (According to website and README files)

Some tools are mentioned in various publications, but seem to be absent in latest release and user’s guide Xisk MK – modeling kernel

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AIMS Major Software Components xinstrument

Source code instrumentation monitor

Runtime performance monitoring library Various tools to process and display performance

data View Kernel

Displays performance data, primarily through an interactive Gantt-chart style view

Tally Derives statistics from collected performance data

Sysconfig Maps physical paths of network traffic

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Instrumentation AIMS data collection is facilitated by source code

instrumentation User may select to gather trace data or profile (summary)

data xinstrument – GUI for inserting instrumentation

User selects all subroutines, all communication, all i/o User may also select constructs by name

All functions of loaded files are instrumented Only selected events are “triggered” based on profile Profile is read at the beginning of execution This approach eliminates the need for recompilation after

changing selected constructs for monitoring

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Measurement monitor – library used to record

performance data Events recorded at each node: state

entrance/exit, communication, I/O, barriers, data-structure definitions, markers

The library attempts to correct clock skew and drift of timestamps for recorded events

makefile must me modified to accommodate the modified source files

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Analysis tracesort – sorting utility to improve viewing

performance (mandatory for Intel iPSC/860 and Paragon versions of AIMS)

pc -“intrusion correction” component Uses a simple model to predict message transmission time Accounts for overhead introduced by each even recorded Processes sorted traces, and attempts to maintain:

Causality of communication Correct partial ordering along threads of control No false deadlocks introduced in trace

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More Analysis Tally

Generation of statistical data, organized into several tables Data for each function

Routine name, busy time, global blocking time, send blocking time, receive blocking time, inclusive execution time, percentage of time in communication, index of communication compared to other functions

Aggregated data per node Node number, busy time, global blocking time, send blocking time, receive

blocking time, inclusive execution time, percentage of time in communication, percentage of communication time caused by contention

NCPU “The NCPU for a given subroutine and a given k is the amount of CPU

time used by that subroutine when k processors are busy, divided by k. “[1]

Routine Concurrency “…indicates the amount of time spent by each subroutine when k copies

were executing simultaneously “ [1] Tabular data can be fed into various 3rd party statistical drawing

packages (i.e. Excel) for visualization

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Presentation “View Kernel” (VK) is responsible for visualization Main view is a Gantt-chart timeline (OverVIEW)

Variants with color-coded information embedded are available lseek, io_wait, read, write time lseek, io_wait, read, write sizes Message sizes

Additional functionality, available via pop-up dialogs Source code correlation Construct-tree correlation

Other views Spokes - A view processes displayed arranged in a circle,

messages as lines connecting processes SysConfig - A view showing network interconnections and paths

of messages corresponding to selections in main view

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References

[1] AIMS User Manual: http://www.nas.nasa.gov/Software/AIMS/manual/c1LM.html

[2] J. C. Yan, S. R. Sarukkai, and P. Mehra. "Performance Measurement, Visualization and Modeling of Parallel and Distributed Programs using the AIMS Toolkit". Software Practice & Experience. April 1995. Vol. 25, No. 4, pages 429- 461