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Credits Dept.
7.5 (6.0 ECTS) BSC


Person in charge:  (-)

General goals

The course aims at describing performance analysis techniques and tools and gain practical experience in their application to real codes. We will survey the state of the art in performance data acquisition, processing and presentation as well as performance modeling techniques. Methodological guidelines will be provided to navigate the huge search space of potential performance bottlenecks and forecast the impact of different design and architectural factors on application performance.

Specific goals


  1. To understand the basic functionalities of performance analysis tools.
  2. To gain insight into the techniques to process raw performance data in order to generate useful information.
  3. To understand the issues in doing performance analysis at a very wide dynamic range of scales.


  1. To use some relevant performance tools.
  2. To be able to design the experiments required to achieve deeper and deeper understanding of the causes of an observed behavior.
  3. To be able to suggest ways to improve the performance of an application and quantitatively estimate the potential gain such proposals should achieve.


  1. Open minded attitude towards understanding the performance of a system.
  2. Correlation of knowledge at multiple levels (architecture, programming models, Operating system,...).
  3. Gaining insight on the behavior and structure of an application without having to know/dig beforehand the details of its internals.
  4. Being able to refine and validate such understanding based on measurements and fitted models with information on the internals of the application.


Estimated time (hours):

T P L Alt Ext. L Stu A. time
Theory Problems Laboratory Other activities External Laboratory Study Additional time

1. Introduction to performance analysis.
T      P      L      Alt    Ext. L Stu    A. time Total 
2,0 0 0 0 0 0 0 2,0

2. Data acquisition, processing and presentation.
T      P      L      Alt    Ext. L Stu    A. time Total 
8,0 0 8,0 0 7,0 6,0 0 29,0

3. Models and performance prediction.
T      P      L      Alt    Ext. L Stu    A. time Total 
6,0 0 6,0 0 7,0 6,0 0 25,0

4. Scalability.
T      P      L      Alt    Ext. L Stu    A. time Total 
8,0 0 6,0 0 7,0 6,0 0 27,0

5. Analysis of two major applications.
T      P      L      Alt    Ext. L Stu    A. time Total 
2,0 0 20,0 0 14,0 9,0 0 45,0

Total per kind T      P      L      Alt    Ext. L Stu    A. time Total 
26,0 0 40,0 0 35,0 27,0 0 128,0
Avaluation additional hours 0
Total work hours for student 128,0

Docent Methodolgy

A general introduction of the main techniques and basic features of major tools will be given in the theory lectures. Laboratory classes will start by introducing the usage of the tools on some simple examples but then the student will be faced with a few relatively large codes that will have to be analyzed with different tools.

Evaluation Methodgy

The evaluation of the course will be based on a set of practical works. At least two major applications will have to be evaluated by each student. At least one of the applications will be in an area to which the student has no previous exposure. A detailed analysis report of the performance "problems" of each application will be required, including a detailed quantification of their importance and suggestions of potential ways to overcome them.

Basic Bibliography

  • Major documentation will correspond to manuals of the tools to be used., , .

Complementary Bibliography

(no available informacion)

Web links


Previous capacities

Notions of computer architecture, operating systems, parallel programming models.


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