Where To Go Next?
Overview
Teaching: 5 min
Exercises: 0 minQuestions
What software libraries can I use so that I don’t need to write my own kernels?
Where can I find additional resources?
Objectives
Links to additional resources.
Use of optimized libraries
If the problem that needs to be accelerated, is a known and somewhat known mathematical problem, it is often not necessary to write custom CUDA kernels, because there are a variety of GPU-accelerated libraries. Those usually perform much better than kernels that we could come up with ourselves, because teams of programmers have spent countless hours to optimize them.
| Description | Name | URL |
|---|---|---|
| BLAS (Basic Linear Algebra Subprograms) | cuBLAS | https://docs.nvidia.com/cuda/cublas/ |
| Drop-in BLAS (based on cuBLAS) | NVBLAS | https://docs.nvidia.com/cuda/nvblas/ |
| FFT (Fast Fourier Transform) | cuFFT | https://docs.nvidia.com/cuda/cufft/ |
| Sparse matrices | cuSPARSE | https://docs.nvidia.com/cuda/cusparse/ |
| Solver for dense and sparse matrices | cuSOLVER | https://docs.nvidia.com/cuda/cusolver/ |
| LAPACK (Linear Algebra Package) | MAGMA | https://icl.utk.edu/magma/ |
| matrix, signal, and image processing | ArrayFire | https://arrayfire.org/docs/ |
More GPU accelerated libraries: https://developer.nvidia.com/gpu-accelerated-libraries
Where to go next?
This has been the barest of introductions to CUDA and GPU programming. Don’t forget the CUDA Programming Guide we mentioned earlier:
- https://docs.nvidia.com/cuda/cuda-c-programming-guide/index.html Here are two shorter tutorials, from NVIDIA:
- https://devblogs.nvidia.com/easy-introduction-cuda-c-and-c/
- https://devblogs.nvidia.com/even-easier-introduction-cuda/
As mentioned in episode 1, there are other ways to program GPUs than CUDA. Here are two OpenACC tutorials, including one from the Alliance:
- https://www.openacc.org/get-started (videos)
- https://docs.alliancecan.ca/wiki/OpenACC_Tutorial (text)
If you’re using GPUs, then performance obviously matters to you. A lot. Here’s a great blog post that summarizes the most important performance issues around GPUs. It’s from a perspective of Deep Learning, but the thoughts are quite general:
Key Points
Many software libraries implement highly-optimized solutions for common problems.