4 citations · 9 across the 9 of their papers we have counts for
9 papers
SpSYRK: Half the Work in Distributed Sparse Matrix Multiplication
Thomas McFarland, Julian Bellavita, Giulia Guidi
The symmetric rank- update (SYRK), , computes the dot product of each pair of rows of , producing the Gram matrix . Its sparse variant underpins similarity se…
Mixed-Precision Communication-Avoiding SGD for Generalized Linear Models on GPUs
Aditya Devarakonda, Irene Simó Muñoz, Giulia Guidi
Distributed stochastic gradient descent (SGD) is limited by communication rather than computation, since each iteration requires an AllReduce across processes. Communication-avoidi…
Ocean: Fast Estimation-Based Sparse General Matrix-Matrix Multiplication on GPU
Yifan Li, Giulia Guidi
In computational science and data analytics, many workloads involve irregular and sparse computations that are inherently difficult to optimize for modern hardware. A key kernel is…
Communication-Avoiding SpGEMM via Trident Partitioning on Hierarchical GPU Interconnects
Julian Bellavita, Lorenzo Pichetti, Thomas Pasquali +2
The multiplication of two sparse matrices, known as SpGEMM, is a key kernel in scientific computing and large-scale data analytics, underpinning graph algorithms, machine learning,…
Communication-Avoiding Linear Algebraic Kernel K-Means on GPUs
Julian Bellavita, Matthew Rubino, Nakul Iyer +4
Clustering is an important tool in data analysis, with K-means being popular for its simplicity and versatility. However, it cannot handle non-linearly separable clusters. Kernel K…
Parallel GPU-Enabled Algorithms for SpGEMM on Arbitrary Semirings with Hybrid Communication
Thomas McFarland, Julian Bellavita, Giulia Guidi
Sparse General Matrix Multiply (SpGEMM) is key for various High-Performance Computing (HPC) applications such as genomics and graph analytics. Using the semiring abstraction, many…
Popcorn: Accelerating Kernel K-means on GPUs through Sparse Linear Algebra
Julian Bellavita, Thomas Pasquali, Laura Del Rio Martin +2
K-means is a popular clustering algorithm with significant applications in numerous scientific and engineering areas. One drawback of K-means is its inability to identify non-linea…
Rapid GPU-Based Pangenome Graph Layout
Jiajie Li, Jan-Niklas Schmelzle, Yixiao Du +6
Computational Pangenomics is an emerging field that studies genetic variation using a graph structure encompassing multiple genomes. Visualizing pangenome graphs is vital for under…
High-Performance Sorting-Based k-mer Counting in Distributed Memory with Flexible Hybrid Parallelism
Yifan Li, Giulia Guidi
In generating large quantities of DNA data, high-throughput sequencing technologies require advanced bioinformatics infrastructures for efficient data analysis. k-mer counting, the…