Accelerating Scientific Computing with GPUs

Last updated on 2024-08-13 | Edit this page

Estimated time: 15 minutes

Overview

Questions

  • What differences are there in handling applications that can use a GPU?

Objectives

  • Learn a little about how GPUs can accelerate some scientific codes.
  • Understand the basics of compiling and running GPU codes.

How to use a GPU to Accelerate Scientific Calculations?

We normally think of a Graphics Processing Unit or GPU in terms of displaying graphics to a computer monitor. GPU cards are designed with many streaming processors that are great at performing lots of similar computations. While this makes them ideal for doing graphics work, it also makes them good for running some scientific codes. GPU cards can accelerate some scientific codes by an order of magnitude while costing much less than what the equivalent CPUs would.

NVIDIA dominates the GPU accelerating market with both 32-bit or consumer grade GPUs like the NVIDIA RTX 4090 that can cost around $1500, but they also produce 64-bit Tesla cards that have no graphics ports at all and are only designed for scientific computing. These include the NVIDIA A100 which can cost as much as $11,000 each or their newest H100 that can cost twice that. Compute nodes can host 1-8 GPU cards each and in some systems the high-end A100s and H100s may be linked together through a fast NVLink connection. Otherwise a multi-GPU run would need to communication across the PCI bus.

AMD GPUs can also be used to accelerate scientific jobs and are increasingly being found in the fastest supercomputers in the world. The code development is still behind that of the NVIDIA CUDA community due to its much later entrance into the scientific computing market. AMD has a Hip compiler that is used to generate code for the AMD GPUs, and it has the benefit of being able to compile the same code for NVIDIA GPUs as well. The AMD GPU line includes 32-bit GPUs such as the Radeon RX 6000 series and the 64-bit Radeon Instinct MI100 cards with costs comparable to the NVIDIA line.

Using GPUs for Machine Learning

Artificial Intelligence applications like Machine Learning can be done on any CPU or GPU. However, GPUs with tensor units can do them faster and less costly since tensor units can do more operations per clock cycle, and the lower precision of the results do not matter for Machine Learning applications. If your system is going to be primarily used for Machine Learning, you’ll want to look at the tensor units in the GPUs while the memory will limit the size of the system you can work with.

AMD and Intel GPUs do not have tensor units. NVIDIA offers tensor units in their 32-bit RTX GPUs providing an inexpensive means of accelerating machine learning algorithms. The 32-bit NVIDIA RTX 3090 for example offers 328 Tensor cores along with 10496 CUDA cores with a maximum of 24 GB of memory. The 64-bit NVIDIA A100 offers 432 Tensor cores along with 6912 CUDA cores for high-end computing and 40 GB or 80 GB of memory. These A100s can also be combined with the NVLink connection so that an 8xA100 GPU cluster can look to the user like a single GPU with 640 GB of memory.

Profiling GPU code

It is more difficult to profile GPU programs since half of the code is running on the CPU and half on the GPU. From outside the program, if a user can ssh into the compute node then running nvidia-smi provides a snapshot of the GPU utilization and GPU memory usage. In the job script the nvprof function can be used in place of the time function to give profile information for various functions of the job.

Compiling and running GPU jobs

Depending on your system you may need to load a module to gain access to the nvcc compiler for CUDA code. nvcc –help can provide you with all the optional parameters, but a basic compile is like nvcc code.cu -o cuda_exec_name. You can then run the executable like any other except that there must be a GPU present. If you are on an HPC system with the Slurm scheduler, you can request a single GPU by adding the –gres=gpu:1 parameter. You can also request a specific type of GPU, but this depends on how each system is set up so you will need to refer to the user documentations. On our HPC cluster at Kansas State University the request for one NVIDIA RTX 3090 would be –gres=gpu:geforce_rtx_3090:1.

Run a simple GPU job if you have access to an HPC system with GPUs

Compile and run the hello_from_gpu.cu CUDA program. You may need to load a module to gain access to the nvcc compiler. There is an sb.hello_from_gpu Slurm batch script to submit the job.

The goal is just to start getting you comfortable with compiling and submitting GPU jobs. Use nvcc hello_gpu.cu -o hello_from_gpu to compile. If you have Slurm on your HPC system you can submit the job with sbatch sb.hello_gpu but you may need to add a partition.

Summary

GPUs are more difficult to program, so for most scientists you will only want to know how to run codes when GPUs are involved and not how to program them yourself. If you are fortunate enough to have an algorithm that someone has accelerated with a GPU, GPUs can often provide an order of magnitude increase in speed over just using CPUs and greatly reduce the cost of doing a calculation.

Key Points

  • Start to become comfortable with using GPUs on an HPC system.