GPGPU — General-purpose programming on GPU

48h — 6 CFU

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The course aims to introduce students to the use of graphic cards as high performance computing hardware (GPGPU: General-purpose Programming on GPU). Attendees are required to know at least one programming language, with a preference for C and/or C++.

The course will be held by Dr. Giuseppe Bilotta

Course outline

• C and C++ programming basics
• History of graphic cards and GPGPU
• GPGPU programming basics; introduction to CUDA and OpenCL
• Benchmarking, optimization and debugging
• GPU programming for embarrasingly parallel problems
• parallel scans and reductions

Calendar

Lessons will be held in every monday (5pm to 7pm) in Room 2 and friday (11am to 1pm) in Room 23. The Microsoft Team code for the course is 3icuged.

Lecture notes

• New lecture notes
• Lessons code

Laboratory

The course will have both theory (frontal) lessons and practice (lab) sessions. For the practice sessions, students may use their own laptops as well as the machines in Lab 125.

Students who prefer to use their own laptops should have a C/C++ development environment in addition to the following software (see also the links below):

1. OpenCL SDK (runtime libraries and headers)
2. updated video drivers
3. CUDA toolkit, ROCm, or equivalent for their GPU (runtime library, include files and essential tools: compiler, debugger, profiler)

OpenCL support on Debian is distributed across several packages, to be installed depending on the available hardware:

• ocl-icd-opencl-dev for the OpenCL library and development headers;
• one or more ICD (Installable Client Drivers), such as:
  • beignet-opencl-icd for older Intel integrated GPUs
  • intel-opencl-icd for newer Intel integrated GPUs
  • nvidia-opencl-icd for NVIDIA GPUs
  • mesa-opencl-icd for AMD GPUs
  • pocl-opencl-icd for the CPU

Link

• hgpu.org — high-performance computing on graphics processing units
• GPUOpen Professional Compute, AMD website on GPU computing
• OpenCL:
  • OpenCL (official website from Khronos Group)
  • Portable Computing Language, OpenCL implementation based on Clang and LLVM
  • for Intel hardware, the Intel® SDK for OpenCL™ Applications is available (may require registration; OpenCL-enaabled drivers (without development software) are available from here
  • ROCm, for AMD GPUs
  • NVIDIA OpenCL SDK Code Samples
  • Forum OpenCL — Khronos
  • Forum OpenCL — AMD
  • Forum OpenCL — Intel
• CUDA:
  • Getting started
  • CUDA downloads
  • List of CUDA-enabled GPUs
  • CUDA Toolkit Documentation
  • CUDA Developer zone

Affiliation

The course is offered by the Dipartimento di Matematica e Informatica of the Università di Catania in cooperation with the Osservatorio Etneo of the Istituto Nazionale di Geofisica e Vulcanologia (INGV).