PARALLEL PROGRAMMING ON GPU ARCHITECTURES

Knowledge and understanding: acquire the fundamentals of massively parallel computing on modern hardware (GPU, multicore CPU, accelerators) based on the stream computing paradigm.

Applying knowledge and understanding: acquire the competence to apply the knowledge to the development of parallel computing software using the main frameworks (CUDA and OpenCL).

Making judgements: acquire che capacity to identify fundamental coding paradigms (embarrasingly parallel problems, reductions, scans) and the corresponding opportunities to paralelize them.

Communication skills: acquire che capacity to describe with proper language both the theoretical and practical aspects of parallel computing on modern architectures.

Learning skills: develop the ability to understand specialized texts on the topic.

The course is composed of both active and practical lectures, with real-time development of sample code for each topic discussed, to provide the students with the necessary familiarity with both the theoretical and practical aspects of the material.

• History of graphic cards and GPGPU
• GPGPU programming basics; introduction to CUDA
• High-level CUDA programming: the CUDA runtime
• Benchmarking, optimization and debugging
• Low-level CUDA programming: the CUDA driver interface
• OpenCL basics and heterogeneous GPGPU programming
• Introduction to multi-GPU

NVIDIA CUDA Programming Guide
OpenCL specification