6 papers
MAGNUS: Fast SpGEMM on GPUs for Irregular Matrices via Hierarchical Multisplit
Jordi Wolfson-Pou, Ahmed Helal, Fabrizio Petrini
We present MAGNUS, a novel algorithm for sparse matrix-matrix multiplication (SpGEMM) of irregular matrices on GPUs. Such matrices often contain many heavy rows, those with larg…
ReLATE: Accelerating Tensor Decomposition via Safe and Efficient Learning of Sparse Encodings
Ahmed E. Helal, Fabio Checconi, Jan Laukemann +4
Tensor decomposition (TD) is essential for analyzing high-dimensional sparse data, yet its irregular computations and memory-access patterns pose major performance challenges on mo…
Scaling Intelligence: Designing Data Centers for Next-Gen Language Models
Jesmin Jahan Tithi, Hanjiang Wu, Avishaii Abuhatzera +1
The explosive growth of Large Language Models (LLMs), such as GPT-4 with 1.8 trillion parameters, demands a fundamental rethinking of data center architecture to ensure scalability…
MAGNUS: Generating Data Locality to Accelerate Sparse Matrix-Matrix Multiplication on CPUs
Jordi Wolfson-Pou, Jan Laukemann, Fabrizio Petrini
Sparse general matrix-matrix multiplication (SpGEMM) is a critical operation in many applications. Current multithreaded implementations are based on Gustavson's algorithm and ofte…
Enhancing Scalability and Performance in Influence Maximization with Optimized Parallel Processing
Hanjiang Wu, Huan Xu, Joongun Park +5
Influence Maximization (IM) is vital in viral marketing and biological network analysis for identifying key influencers. Given its NP-hard nature, approximate solutions are employe…
Efficient Parallel Multi-Hop Reasoning: A Scalable Approach for Knowledge Graph Analysis
Jesmin Jahan Tithi, Fabio Checconi, Fabrizio Petrini
Multi-hop reasoning (MHR) is a process in artificial intelligence and natural language processing where a system needs to make multiple inferential steps to arrive at a conclusion…