Degree | Type | Year |
---|---|---|
2504392 Artificial Intelligence | OT | 3 |
2504392 Artificial Intelligence | OT | 4 |
You can view this information at the end of this document.
Although there are no formally established prerequisites and it is the responsibility of the subject itself to provide the students with a means to acquire the knowledge described in the subject's content section, it is recommended: a good knowledge of programming, of the operation of a computer and the operating system at the user programmer level (Fundamentals of Programming I and II, Fundamentals of Computing).
The objective of this subject is to know high performance and parallel computing systems, multiprocessor and multi-computing systems, paradigms of parallel programming, learn to develop applications using message passing or shared memory and analyze the performance of these applications.
The theoretical concepts about programming paradigms, message passing and shared memory are reinforced with lab sessions in which students learn to program using parallel programming languages.
All the components described in this subject must allow the student to understand the operation of high performance and parallel systems and, to some extent, be able to perform a simple design of a parallel application and evaluate its performance.
Topic 1: Introduction to high performance systems.
Advanced C programming and integration of C code with Python. Concurrency: concept, race conditions, critical section and mutual exclusion mechanisms.
Introduction to high performance systems, parallel systems, multiprocessors and multicomputers. Execution of parallel applications in high performance systems.
Topic 2: Classification of parallelism
SIMD (Single Instruction, Multiple Data), MIMD (Multiple Instruction, Multiple Data). Parallel application models.
Topic 3: Parallel Algorithms
Definition of parallel algorithms. Examples of parallel algorithms. Development of parallel algorithms.
Topic 4: Parallel programming
Paradigms of parallel programming. Applications based on message passing. Standard MPI (Message Passing Interface). Applications based on shared memory. Standard OpenMP (Open Multiprocessing), OpenACC (Open Accelerators) and CUDA (Compute Unified Device Architecture). Parallelism in Python. Application development using MPI, OpenMP, OpenACC, Cuda and Python.
Topic 5: Performance analysis
Analysis of performance of parallel systems. Evaluation of performance of parallel systems. Examples of performance evaluation tools.
Title | Hours | ECTS | Learning Outcomes |
---|---|---|---|
Type: Directed | |||
Labs (Development of practical projects in the lab) | 16 | 0.64 | 1, 2, 3, 5, 6, 7, 8, 11, 13 |
Problems (Solving exercices) | 7 | 0.28 | 1, 2, 3, 4, 6, 8, 9, 10, 13 |
Theory | 12 | 0.48 | 1, 3, 4, 5, 7, 10 |
Type: Autonomous | |||
Autonomous study | 45 | 1.8 | 2, 3, 4, 5, 7, 10, 11 |
Preparation of problems and basic exercises | 15 | 0.6 | 2, 3, 4, 5, 6, 7, 10, 13 |
Projects preparation | 40 | 1.6 | 2, 3, 5, 6, 7, 8, 11, 13 |
In the development of the subject, four types of teaching activities can be differentiated:
This approach to work is aimed at promoting active learning and developing competencies of organizational and planning skills, oral and written communication, teamwork and critical reasoning. The quality of the exercises carried out, of its presentation and of its operation will be valued especially.
The management of the subject will be done through the Virtual Campus (https://cv.uab.cat/), which will be used to view the materials, manage the lab groups, make the corresponding deliveries, see the notes, communicate with the professors, etc.
Annotation: Within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.
Title | Weighting | Hours | ECTS | Learning Outcomes |
---|---|---|---|---|
Partial individual exam 1 | 25% | 2 | 0.08 | 2, 3, 5, 10 |
Partial individual exam 2 | 25% | 2 | 0.08 | 2, 3, 5, 10 |
Practical exercices | 20% | 8 | 0.32 | 2, 3, 5, 7, 9, 10, 11, 13 |
Practical project 1 | 10% | 1 | 0.04 | 1, 2, 4, 6, 7, 8, 11, 12, 13 |
Practical project 2 | 10% | 1 | 0.04 | 1, 2, 4, 6, 7, 8, 11, 12, 13 |
Practical project 3 | 10% | 1 | 0.04 | 1, 2, 4, 6, 7, 8, 11, 12, 13 |
The objective of the assessment process is to verify that the student has achieved the knowledge and skills defined in the objectives of the subject, as well as the associated competences.
3 types of activities will be assessed independently, and the weighted sum of them will give the final grade. These activities are:
The Theory (T) part will be assessed with two individual partial exams throughout the course. The final grade of Theory will come from the weighted sum of the two exams (0.5 * Control 1 + 0.5 * Control 2). There will be a second chance to recover that part on the day at June we have assigned to. Parts that have not been passed in the partial theory exams may be recovered separately. The minimum grade to pass this part of Theory is > = 5. In the recovery only the failed exams can be recovered, the grade obtained cannot be raised (in the case of passing the Theory part). The maximum grade that can be obtained in recovery is 7.
The part of Practical projects (PL) will be evaluated by group. There are three deliveries. The final grade will come from the weighted sum of the three deliveries (X0 * Delivery 1 + X1 * Delivery 2 + X2 * Delivery 3, X0 + X1 + X2 = 1). There will be 3 short and written exams for individual validation of the practical projects in the class schedule (the third exam in the schedule established by the coordination for the second partial exams in June). Practical projects (PL) = Lab * Validation. To pass the PL the minimum mark will have to be >= 5. It cannot be recovered. These sessions are mandatory.
The practical exercises (EXER) will be done in groups of 2 people and will consist ofworkingon very specific programming problems, short and related to those found in laboratory practices. We want each student to study the solution of a set of specific problems in isolation in the most general case of the practice. The value of these exercises is 20% of the final grade and given their nature and purpose are not recoverable.
The final grade of the subject will be the weighted sum of the grades of each of the activities: 50% of Theory, 20% Resolution of practical exercises and 30% of Resolution of practical projects. The result will have to be >= 5.
In case a student does not pass the subject due to not reaching the minimum score in any of the mandatory parts (Theory or Practical projects), even though the weighted average is equal or superior to 5, the final grade of the subject will be 4.5.
In the event that the average does not reach 5, the official grade will be the average mark obtained numerically.
If the student delivers any activity, it is understood that he/she is participating in the subject and will be evaluated. If you do not deliver any activity, then it can be considered Non-evaluable.
Granting an honorific matriculation qualification is a decision of the faculty responsible for the subject. The regulations of the UAB indicate that MH can only be awarded to students who have obtained a final grade of 9.00 or more. It can be granted up to 5% of MH of the total number of students enrolled.
The dates of continuous evaluation and assignment delivery will be published on the virtual campus and may be subject to possible changes to adapt to possible incidents; the virtual campus will always inform about these changes since it is understood that the CV is the usual mechanism for exchanging information between professors and students.
For each assessment activity, a place, date and time of revision will be indicated in which the student will be able to review the activity with the professor. In this context, claims can be madeaboutthe activity grade, which will be evaluated by the professors responsible for the subject. If the student does not submit to this review, this activity will not bereviewed later.
SUMMARY
If ((T> = 5) i (PL> = 5)) then
NF = 0.5 * T + 0.2 * EXER + 0.3 * PL
If (NF> = 5) then PASS
else FAIL
else FAIL
Repeating students: repeating students who have passed laboratory exercices in previous editions of the subject may request the validation of this part of the subject. The rest of the assessment activities must be carried out under the same conditions as the other students.
Single Assessment Evaluation: This subject DOES NOT HAVE SINGLE ASSESSMENT EVALUATION.
Note about plagiarism:
Without prejudice to other disciplinary measures deemed appropriate, and in accordance with the current academic regulations, irregularities committed by a student who mayleadto a variation of the qualification in an assessable activity will be graded with zero (0). Assessment activities qualified in this way and by this procedure will not be recoverable. If it is necessary to pass any of these assessment activities to pass the subject, this subject will be suspended directly, without opportunity to recover it in the same course. These irregularities include, among others:
If you do not pass the subject due to the fact that some of the evaluation activities do not reach the minimum grade required, the numerical official grade will be the lowest value between 4.5 and the weighted average of the grades. With the exceptions that the "Non-Appraising" qualification will be awarded to students who do not participate in any of the assessment activities, and that the numerical official grade will be the lowest value between 3.0 and the average Weighted grades in case the student has committed irregularities in an evaluation act (and therefore the subject cannot beapproved by compensation). In future editions of this subject, the student who has committed irregularities in an evaluation act will not be validated any of the assessment activities carried out.
In summary: copy, let copy or plagiarize (or attempt) in any of theassessment activities will lead to a FAIL, not compensable and without validations of parts of the subject in subsequent courses.
OpenMP
MPI
OpenACC/CUDA
queue manager (SLURM)
tools for performance evaluation (perf, TAU, nvcc)
remote connection to the laboratory (e.g. MobaTex, Eclipse, Visual Studio, Visual Studio Code, etc.)
Name | Group | Language | Semester | Turn |
---|---|---|---|---|
(PAUL) Classroom practices | 1 | English | second semester | afternoon |
(PLAB) Practical laboratories | 1 | English | second semester | afternoon |
(TE) Theory | 1 | English | second semester | afternoon |