High-performance matrix multiplication remains a cornerstone of numerical computing, underpinning a wide array of applications from scientific simulations to machine learning.

By transforming operands into a Montgomery domain, these algorithms enable efficient modular multiplication and exponentiation, which are crucial for public-key cryptosystems.

In the past decade, however, the parallel processing of GPUs was also found to more efficiently run the matrix multiplication algorithms needed to power AI models. AI development has two key phases.

As a key operation in contemporary cryptosystems, modular multiplication occupies non-negligible latency and area. We first show optimizations of the k-term Karatsuba algorithm for AB/rk and ABmodrk ...

Elliptic Curve Cryptography (ECC) is extensively utilized in various security applications due to its high efficiency and security. The core operation in ECC, elliptic curve scalar multiplication, ...

Our Case - The Two-Stage Butterfly Approach: In the butterfly matrix multiplication algorithm, each stage applies a set of 2x2 butterfly transformations to pairs of elements. For L=8, there are three ...

The GraphBLAS, Basic Linear Algebra Subprograms for Graphs, is a community-driven, open programming specification for graph analysis. The specification makes the development of high-performance graph ...

You can create a release to package software, along with release notes and links to binary files, for other people to use. Learn more about releases in our docs ...

A matrix multiplication algorithm was applied to integrate scRNA-seq data of each sample with drug–gene interactions to generate the target-based enrichment profiles of compounds as input for the ...