Créditos
3
Tipos
Optativa
Requisitos
Esta asignatura no tiene requisitos
, pero tiene capacidades previas
Departamento
AC
Web
https://torres.ai/HPC4AI-MEI
Mail
jordi.torres@upc.edu
Rather than treating deep learning frameworks and tools as black boxes, the course adopts a system-oriented perspective. It guides students through the complete execution workflow of AI training, from hardware architecture and system software to job scheduling, parallel execution, performance measurement, and scalability analysis. The emphasis is on execution behavior: how computation, memory, communication, and coordination interact, and how these interactions determine performance, efficiency, and cost.
A central premise of the course is that the nature of engineering work in AI is changing. Modern AI tools can generate training scripts, pipelines, and even distributed execution logic with minimal effort. As a result, writing code is no longer the primary challenge. The real difficulty, and the real value, lies in understanding whether that code scales, where bottlenecks appear, when efficiency is lost, and what trade-offs are being made when more resources are used.
For this reason, the course explicitly allows and acknowledges the use of modern AI tools (such as code assistants, agentic systems, or automated code generators). However, the course is not about code authorship or syntax. It is about developing the ability to reason about performance, scalability, efficiency, and cost when training deep learning models on real HPC systems. Students are expected to understand what is being executed, how it behaves at scale, and why performance changes as observed.
Hands-on experimentation is a core component of the course. Through a sequence of laboratory activities, students train deep learning models using single and multiple GPUs, explore parallel and distributed training strategies, and analyze scalability and performance behavior under realistic conditions. All laboratory work and assessments are evaluated based on the quality of experimental setup, the relevance of performance measurements, the interpretation of results, and the soundness of scalability and cost¿benefit reasoning.
The course material is self-contained and based on the official course textbook, which serves as the main reference for both theoretical concepts and practical activities. No prior experience with supercomputers is required, and deep learning concepts are introduced progressively as needed.
Ultimately, HPC4AI is not a course about recipes or fixed solutions. It is a course about developing engineering judgment. As code generation becomes cheaper and more accessible, the ability to measure, reason, and decide becomes essential. This course is designed to develop precisely that ability.
Details specific to the 2026 edition of the course can be found on the course web page:
https://torres.ai/HPC4AI-MEI
Profesorado
Responsable
- Jordi Torres Viñals ( torres@ac.upc.edu )
Horas semanales
Teoría
2
Problemas
0
Laboratorio
2
Aprendizaje dirigido
0
Aprendizaje autónomo
7.1
Competencias
Dirección y gestión
Específicas
Genéricas
Actitud frente al trabajo
Básicas
Objetivos
-
OE1: Foundations of HPC platforms for AI: comprender la arquitectura, los componentes principales y el entorno software de una plataforma de supercomputación moderna orientada a cargas de trabajo de inteligencia artificial.
Competencias relacionadas: CTE6, CG1, CG6, CG7, CG8, -
OE2: Practical use of a supercomputer for AI workloads: adquirir autonomía básica en el uso de un supercomputador real, incluyendo acceso, gestión de recursos y ejecución de trabajos para aplicaciones de inteligencia artificial.
Competencias relacionadas: CB6, CTE6, CG1, CG8, -
OE3: Fundamentals of Deep Learning for HPC users: entender los principios fundamentales del Deep Learning necesarios para entrenar modelos en entornos de supercomputación, sin requerir conocimientos previos avanzados.
Competencias relacionadas: CTE9, CG4, CG8, -
OE4: Parallel training of Deep Learning models: comprender y aplicar técnicas de entrenamiento paralelo de modelos de Deep Learning utilizando múltiples GPUs en uno o varios nodos (servidores) de computación.
Competencias relacionadas: CB6, CB9, CTE6, CTE9, CG1, -
OE5: Performance analysis and optimization of AI training: analizar el rendimiento del entrenamiento de modelos de inteligencia artificial mediante métricas como throughput, speedup y eficiencia, y aplicar técnicas básicas de optimización.
Competencias relacionadas: CTE6, CTE9, CG1, CG4, -
OE6: Experimental evaluation and communication of results: evaluar experimentalmente los resultados obtenidos en un entorno de supercomputación y comunicar conclusiones técnicas de forma clara, estructurada y argumentada.
Competencias relacionadas: CB8, CB9, CTR5, CDG1,
Contenidos
-
C1: HPC platforms and software ecosystem for AI
Arquitectura de supercomputadores modernos, componentes hardware, sistema operativo y stack software para cargas de trabajo de inteligencia artificial. -
C2: Accessing and using a supercomputer for AI workloads
Acceso a un supercomputador, gestión de cuentas, sistemas de colas, SLURM y ejecución de trabajos para aplicaciones de Deep Learning. -
C3: Deep Learning fundamentals for HPC environments
Conceptos básicos de Deep Learning necesarios para entrenar modelos en entornos HPC, incluyendo redes neuronales, entrenamiento y datasets (no podemos suponer conocimientos previos). -
C4: Parallel training of Deep Learning models
Entrenamiento paralelo de modelos de Deep Learning utilizando múltiples GPUs, incluyendo estrategias de paralelismo y frameworks de programación. -
C5: Performance metrics and optimization of AI training
Análisis del rendimiento del entrenamiento de modelos de IA mediante métricas como throughput, speedup y eficiencia, y técnicas básicas de optimización. -
C6: Experimental evaluation and presentation of results
Evaluación experimental de resultados obtenidos en un entorno HPC y comunicación clara de conclusiones mediante informes y presentaciones técnicas.
Actividades
Actividad Acto evaluativo
Teoría
1h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
1h
Teoría
2.5h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
2h
Teoría
2h
Problemas
0h
Laboratorio
1.5h
Aprendizaje dirigido
0h
Aprendizaje autónomo
3h
Teoría
3h
Problemas
0h
Laboratorio
1h
Aprendizaje dirigido
0h
Aprendizaje autónomo
4.5h
Teoría
2.5h
Problemas
0h
Laboratorio
2h
Aprendizaje dirigido
0h
Aprendizaje autónomo
5h
Teoría
0h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
9h
Teoría
0h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
2h
Teoría
0h
Problemas
0h
Laboratorio
0h
Aprendizaje dirigido
0h
Aprendizaje autónomo
0.4h
Metodología docente
The course follows an active learning and continuous assessment approach, combining theoretical lectures, hands-on laboratory work, autonomous learning, and student presentations.Theoretical sessions are delivered through participatory lectures, where the instructor introduces the fundamental concepts related to high-performance computing platforms, deep learning fundamentals, parallel training strategies, and performance analysis for artificial intelligence workloads. Students are expected to actively participate in discussions during these sessions.
Hands-on activities constitute a central component of the course and are based on a learn-by-doing methodology. These activities focus on practical experimentation using a real supercomputing environment (MareNostrum 5). Part of the hands-on work is carried out during regular class sessions, while the remaining work is completed outside the classroom as autonomous learning. All hands-on activities require the submission of corresponding reports and, in some cases, technical presentations through the institutional learning platform (Racó).
Autonomous learning is mainly based on the detailed study of the course textbook, which constitutes the main reference material for the subject. Students are also required to prepare presentations and technical material related to their practical work.
Student presentations play an important role in the course. Individual students or groups are randomly selected to present their work and results in class. Peer evaluation is incorporated as part of the learning process, encouraging critical analysis and constructive feedback.
Regular attendance and active participation are expected. Students are responsible for all material covered in class, including announcements, assignments, and project guidelines, regardless of attendance. It is the student¿s responsibility to obtain any missed material.
Método de evaluación
The evaluation of this course is based on a continuous assessment system, strongly focused on practical work and active participation.The final grade is composed of the following elements:
- Attendance and participation: 20%
Regular attendance and active participation in lectures, discussions, and hands-on sessions.
Attendance is mandatory. To qualify for continuous assessment, students must attend at least 80% of the class sessions.
- Hands-on activities (laboratory work): 60%
Evaluation of the practical laboratory activities carried out throughout the course (LAB 0 to LAB 4).
The instructor will assess the submitted work using a rubric that considers correctness, completeness, experimental results, and technical understanding.
Some students or groups will be randomly selected during the course to present and explain their laboratory work (LAB 0 to LAB 2). This mechanism is intended to ensure that all students prepare and understand their work thoroughly.
- Technical presentations and peer evaluation: 20%
During the final session of the course, all students will present either LAB 3 or LAB 4 (assigned randomly).
Presentations will be evaluated by the instructor and through peer evaluation, which will contribute to the final presentation grade.
Attendance on the presentation day is mandatory. Students who do not attend this session will not receive the presentation grade.
Requirements for continuous assessment: To qualify for continuous assessment, students must meet all the following requirements:
- Attendance: at least 80% of the class sessions.
- Hands-on activities: completion of at least 50% of the laboratory work.
Final exam option
- Students who do not meet the requirements for continuous assessment will have the option to take a final exam.
- This exam will evaluate the entire course content, including theoretical concepts, practical knowledge, and autonomous learning material based on the course book and laboratory activities.
- The final exam will be announced during the course. No documentation (printed or digital) will be allowed during the exam.
Bibliografía
Básico
-
Supercomputing for Artificial Intelligence: Foundations, Architectures, and Scaling Deep Learning
- Torres, Jordi,
WATCH THIS SPACE Book Series - Barcelona. Amazon KDP,
2025.
ISBN: 979-831932835-9
-
Slides of the course
- Torres, J,