22 citations · 22 across the 7 of their papers we have counts for
6 papers · 1 filter
Characterizing the Scalability and Performance of Large-Scale AI Training Under Multi-Tenancy
Jacopo Raffi, Thomas Pasquali, Lorenzo Piarulli +6
Characterising AI workload performance on modern HPC systems requires understanding both their scalability in isolation and their behaviour under concurrent execution. However, the…
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…
The Landscape of GPU-Centric Communication
Didem Unat, Ilyas Turimbetov, Mohammed Kefah Taha Issa +4
In recent years, GPUs have become the preferred accelerators for HPC and ML applications due to their parallelism and fast memory bandwidth. While GPUs boost computation, inter-GPU…
High Performance Unstructured SpMM Computation Using Tensor Cores
Patrik Okanovic, Grzegorz Kwasniewski, Paolo Sylos Labini +3
High-performance sparse matrix-matrix (SpMM) multiplication is paramount for science and industry, as the ever-increasing sizes of data prohibit using dense data structures. Yet, e…
Exploring GPU-to-GPU Communication: Insights into Supercomputer Interconnects
Daniele De Sensi, Lorenzo Pichetti, Flavio Vella +11
Multi-GPU nodes are increasingly common in the rapidly evolving landscape of exascale supercomputers. On these systems, GPUs on the same node are connected through dedicated networ…