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CS 584 Lecture 21

CS 584 Lecture 21. Project Proposal Due Friday! In my box in the CS office No class on Friday Paper presentation on Wednesday Tom Abbott. Performance Analysis Remember: In measuring, we change what we are measuring. 3 Basic Steps Data Collection Data Transformation Data Visualization

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CS 584 Lecture 21

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  1. CS 584 Lecture 21 • Project Proposal • Due Friday! • In my box in the CS office • No class on Friday • Paper presentation on Wednesday • Tom Abbott

  2. Performance AnalysisRemember: In measuring, we change what we are measuring. • 3 Basic Steps • Data Collection • Data Transformation • Data Visualization • Many tools are available, but consider • Accuracy, Simplicity, Flexibility • Intrusiveness, and Abstraction

  3. Data Collection • The process by which data about the performance of a program are gathered. • 3 Techniques • Profiles • Counters • Event Traces

  4. Profiles • Shows time spent in portions of the code. • Advantages • Profiles can be obtained automatically • Should be the first technique used to gather and analyze performance data • Disadvantages • Don't generally consider temporal aspects of a parallel program.

  5. Counters • Storage location that counts events • Number of sends/receives • Number of procedure calls, etc. • Can be compiler generated as in a profile • Also includes interval timers • Time spent in a piece of code • Idle time, function time, etc.

  6. Traces • Low level approach to data collection • A log file is generated which records the event and a time stamp, etc. Trace Record Description 11 0 1553 2 0 1078 Timer Data 6 0 2237 2 1 1 8 Receive 11 0 2237 2 0 1078 Timer Data 11 0 2500 2 0 1341 Timer Data 4 0 3186 2 3 2 12 Send 11 0 3186 2 0 1341 Timer Data

  7. Traces • Advantages • Support a broad study of program behavior • Can be post-processed to obtained profiles, etc. • Contains other data (message size, etc.) • Disadvantages • Huge log files (eg. 20 bytes per event) • Perturbs performance • Sophisticated analysis is required

  8. Data Transformation & Visualization • Profiles, counts, and trace data are difficult to directly interpret. • Goal: Present the data to the programmer in such a way that interpretation is simple. • Histogram • Gantt chart • Space-time diagram

  9. Histogram

  10. Gantt Chart

  11. Space-Time Diagram

  12. Tools • XPVM • Instrumented PVM code • Space-time chart based • Upshot • Instrumented MPI code • Gantt chart based I couldn’t get this to work on our machines

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