Gpu Computing
A.Y. 2018/2019
Learning objectives
This course mainly focuses on the parallel programming of GPU (Graphics Processing Units) devices.
To this end, the NVIDIA CUDA hw/sw architecture is used together with the CUDA C language so as to develop parallel algorithms for high performance computing.
To this end, the NVIDIA CUDA hw/sw architecture is used together with the CUDA C language so as to develop parallel algorithms for high performance computing.
Expected learning outcomes
Undefined
Lesson period: Second semester
Assessment methods: Esame
Assessment result: voto verbalizzato in trentesimi
Single course
This course cannot be attended as a single course. Please check our list of single courses to find the ones available for enrolment.
Course syllabus and organization
Milan
Responsible
Lesson period
Second semester
ATTENDING STUDENTS
Course syllabus
Program:
- Introduction to heterogeneous system architecture based on CPU and GPU
- The general purpose GPU programming (GPGPU) concept
- Parallel architecture
- The CUDA programming model
- The CUDA execution model
- The CUDA memory model
- Stream, concurrency and performance optimization
- GPU-accelerated CUDA libraries
- Multi-GPU programming
- Parallel design patterns
- Application development on NVIDIA GPUs
- Introduction to heterogeneous system architecture based on CPU and GPU
- The general purpose GPU programming (GPGPU) concept
- Parallel architecture
- The CUDA programming model
- The CUDA execution model
- The CUDA memory model
- Stream, concurrency and performance optimization
- GPU-accelerated CUDA libraries
- Multi-GPU programming
- Parallel design patterns
- Application development on NVIDIA GPUs
Website
NON-ATTENDING STUDENTS
Course syllabus
The same for 'frequentanti'
Professor(s)