Hardware Overview

HPC Overview

Partitions:

• Standard (no GPUs)
• Largemem (no GPUs)
• GPU (compute node with NVIDIA GPU(s))
• Interactive (compute node with NVIDIA GPU(s))

Visit our documentation on HPC hardware configuration for more information.

GPU General Overview

• Graphics Processing Units (GPUs), originally developed for accelerating graphics rendering, can dramatically speed up any simple but highly parallel computational processes (General Purpose GPU).

CPU vs. GPU:

• Integrated GPU vs Discrete GPUs:
  • Integrated GPUs are used mostly for graphics rendering and gaming
  • Dedicated GPUs are designed for intensive computations

Vendors and Types:

• NVIDIA, AMD, Intel
• Datacenter : K80, P100, V100, A100, H100 (NVIDIA); MI300A, MI300X (AMD)
• Workstations: A6000, Quadro (NVIDIA)
• Gaming: GeForce RTX 20xx, 30xx, 40xx (NVIDA), Radeon (AMD)
• Laptops and desktops: GeForce (NVIDIA), Radeon (AMD), Iris (Intel)

Programming GPUs

Several libraries and programming models have been developed to program GPUs.

• CUDA
  • CUDA is a parallel computation platform, developed by NVIDIA, for general-purpose programming on NVIDIA hardware.
• HIP
  • HIP is a programming interface from AMD that allows developers to target either NVIDIA or AMD hardware.
• OpenCL
  • OpenCL is a more general parallel computing platform, developed by Apple. It allows software to access CPUs, GPUs, FPGAs, and other devices.
• SYCL
  • SYCL started as an outgrowth of OpenCL but is now independent of it.
• Kokkos
  • Kokkos is another programming model that attempts to be device-independent. It can target multicore programming (OpenMP), CUDA, HIP, and SYCL.

Most of these programming paradigms can be used from Python, but nearly all machine learning/deep learning packages are based on CUDA and will only work with NVIDIA GPUs.