483 citations · 588 across the 24 of their papers we have counts for
7 papers · 1 filter
SpaDA: A Spatial Dataflow Architecture Programming Language
Lukas Gianinazzi, Tal Ben-Nun, Torsten Hoefler
Spatial dataflow architectures like the Cerebras Wafer-Scale Engine deliver exceptional performance in AI and scientific computing by distributing scratchpad memory across hundreds…
Near-Optimal Wafer-Scale Reduce
Piotr Luczynski, Lukas Gianinazzi, Patrick Iff +3
Efficient Reduce and AllReduce communication collectives are a critical cornerstone of high-performance computing (HPC) applications. We present the first systematic investigation…
Low-Depth Spatial Tree Algorithms
Yves Baumann, Tal Ben-Nun, Maciej Besta +3
Contemporary accelerator designs exhibit a high degree of spatial localization, wherein two-dimensional physical distance determines communication costs between processing elements…
Arrow Matrix Decomposition: A Novel Approach for Communication-Efficient Sparse Matrix Multiplication
Lukas Gianinazzi, Alexandros Nikolaos Ziogas, Langwen Huang +9
We propose a novel approach to iterated sparse matrix dense matrix multiplication, a fundamental computational kernel in scientific computing and graph neural network training. In…
Streaming Task Graph Scheduling for Dataflow Architectures
Tiziano De Matteis, Lukas Gianinazzi, Johannes de Fine Licht +1
Dataflow devices represent an avenue towards saving the control and data movement overhead of Load-Store Architectures. Various dataflow accelerators have been proposed, but how to…
GraphMineSuite: Enabling High-Performance and Programmable Graph Mining Algorithms with Set Algebra
Maciej Besta, Zur Vonarburg-Shmaria, Yannick Schaffner +16
We propose GraphMineSuite (GMS): the first benchmarking suite for graph mining that facilitates evaluating and constructing high-performance graph mining algorithms. First, GMS com…
Parallel Minimum Cuts in Near-linear Work and Low Depth
Barbara Geissmann, Lukas Gianinazzi
We present the first near-linear work and poly-logarithmic depth algorithm for computing a minimum cut in a graph, while previous parallel algorithms with poly-logarithmic depth re…