8 citations · 17 across the 8 of their papers we have counts for
8 papers
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…
VENOM: A Vectorized N:M Format for Unleashing the Power of Sparse Tensor Cores
Roberto L. Castro, Andrei Ivanov, Diego Andrade +3
The increasing success and scaling of Deep Learning models demands higher computational efficiency and power. Sparsification can lead to both smaller models as well as higher compu…
Cached Operator Reordering: A Unified View for Fast GNN Training
Julia Bazinska, Andrei Ivanov, Tal Ben-Nun +4
Graph Neural Networks (GNNs) are a powerful tool for handling structured graph data and addressing tasks such as node classification, graph classification, and clustering. However,…
Maximum Flows in Parametric Graph Templates
Tal Ben-Nun, Lukas Gianinazzi, Torsten Hoefler +1
Execution graphs of parallel loop programs exhibit a nested, repeating structure. We show how such graphs that are the result of nested repetition can be represented by succinct pa…
Bridging Control-Centric and Data-Centric Optimization
Tal Ben-Nun, Berke Ates, Alexandru Calotoiu +1
With the rise of specialized hardware and new programming languages, code optimization has shifted its focus towards promoting data locality. Most production-grade compilers adopt…
Performance Embeddings: A Similarity-based Approach to Automatic Performance Optimization
Lukas Trümper, Tal Ben-Nun, Philipp Schaad +2
Performance optimization is an increasingly challenging but often repetitive task. While each platform has its quirks, the underlying code transformations rely on data movement and…