(24-02-2025)
Preface: Is linear algebra used in real life? An example of where there is a lot of research on these things is in sparse matrix analysis, which comes up a lot in real world applications of linear algebra. For some buzzwords, popular topics like machine learning, neural networks, and computer graphics all use huge amounts of linear algebra.
Since a box’s length is independent of its width and breadth, space has three dimensions. Since any point in space may be described by a linear combination of three independent vectors, space is considered to be three-dimensional in the technical language of linear algebra.
In Einstein’s special relativity theory we live in 4 dimensional spacetime. Though the way we normally “imagine” the world, we tend to believe that we live in a 3 dimensional Newtonian space with a separate absolute time dimension.
Introduction: AI calculations often rely on various mathematical techniques, including linear algebra, Fourier transforms, and sparse matrix operations.
Some of the key math libraries in ROCm include:
- rocBLAS: A library for basic linear algebra subprograms.
- rocFFT: A library for fast Fourier transforms.
- rocRAND: A library for random number generation.
- rocSOLVER: A library for solving linear algebra problems.
- rocSPARSE: A library for sparse matrix operations
These libraries are optimized for AMD hardware and provide similar functionality to NVIDIA’s cuBLAS, cuFFT, cuRAND, etc., making it easier for developers to port their applications between different hardware platforms.
What does ROCm stand for? ROCm initially stood for Radeon Open Compute platform; however, due to Open Compute being a registered trademark, ROCm is no longer an acronym — it is simply AMD’s open-source stack designed for GPU compute.
Official reference: If you are interested in ROCm, please refer to the following link – https://rocm.docs.amd.com/en/docs-5.7.1/reference/gpu_libraries/math.html