* Program re-ordering for improved L2 cache hit rate. * Automatic performance tuning. # Motivations # Matrix multiplications are a key building block of most modern high-performance computing systems.

Abstract: While the Karatsuba algorithm reduces the complexity of large integer multiplication, the extra additions required minimize its benefits for smaller integers of more commonly-used bitwidths.

Discover how nvmath-python leverages NVIDIA CUDA-X math libraries for high-performance matrix operations, optimizing deep learning tasks with epilog fusion, as detailed by Szymon Karpiński.

Implementing 3D shape transformations using matrix multiplication and a basic line scan-conversion algorithm. In order to run the main program, you must have a version of Python that is 3.6+ and have ...

Matrix-vector multiplications form the core of a plethora of scientific computing and machine learning applications that include solving partial differential equations, forward and back propagation in ...

Abstract: In distributed computing system for the master-worker framework, an erasure code is able to mitigate the effects of slow workers, also called stragglers. The distributed computing system ...