Abstract: In many applications, matrix multiplication involves different shapes of matrices. The shape of the matrix can significantly impact the performance of matrix multiplication algorithm. This ...
Abstract: Excessive energy consumption has become one of the major challenges in high performance computing. Reducing the energy consumption of frequently used high performance computing applications ...
Hefei National Laboratory for Physical Sciences at the Microscale, Department of Chemical Physics, and Synergetic Innovation Center of Quantum Information and Quantum Physics, University of Science ...
A high-performance implementation of matrix multiplication using Strassen’s algorithm and OpenMP-based parallelization. Developed as part of a Parallel Computing course to explore recursive algorithms ...
High-performance matrix multiplication remains a cornerstone of numerical computing, underpinning a wide array of applications from scientific simulations to machine learning. Researchers continually ...
The project demonstrates speedup, efficiency, and scalability of parallel computing techniques and applies theoretical models (Amdahl's Law and Gustafson's Law) to understand performance improvements.
Want smarter insights in your inbox? Sign up for our weekly newsletters to get only what matters to enterprise AI, data, and security leaders. Subscribe Now Can artificial intelligence (AI) create its ...
We’re just a few years into the AI revolution, but AI systems are already improving decades-old computer science algorithms. Google’s AlphaEvolve AI, its latest coding agent for algorithm discovery, ...
What do encrypted messages, recognizing speech commands and running simulations to predict the weather have in common? They all rely on matrix multiplication for accurate calculations. DeepMind, an ...
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