341 citations · 1.1k across the 43 of their papers we have counts for
9 papers · 1 filter
Deep Data Flow Analysis
Chris Cummins, Hugh Leather, Zacharias Fisches +3
Compiler architects increasingly look to machine learning when building heuristics for compiler optimization. The promise of automatic heuristic design, freeing the compiler engine…
Parametric Graph Templates: Properties and Algorithms
Tal Ben-Nun, Lukas Gianinazzi, Torsten Hoefler +1
Hierarchical structure and repetition are prevalent in graphs originating from nature or engineering. These patterns can be represented by a class of parametric-structure graphs, w…
Substream-Centric Maximum Matchings on FPGA
Maciej Besta, Marc Fischer, Tal Ben-Nun +3
Developing high-performance and energy-efficient algorithms for maximum matchings is becoming increasingly important in social network analysis, computational sciences, scheduling,…
On the Parallel I/O Optimality of Linear Algebra Kernels: Near-Optimal LU Factorization
Grzegorz Kwasniewski, Tal Ben-Nun, Alexandros Nikolaos Ziogas +3
Dense linear algebra kernels, such as linear solvers or tensor contractions, are fundamental components of many scientific computing applications. In this work, we present a novel…
StencilFlow: Mapping Large Stencil Programs to Distributed Spatial Computing Systems
Johannes de Fine Licht, Andreas Kuster, Tiziano De Matteis +3
Spatial computing devices have been shown to significantly accelerate stencil computations, but have so far relied on unrolling the iterative dimension of a single stencil operatio…
Data Movement Is All You Need: A Case Study on Optimizing Transformers
Andrei Ivanov, Nikoli Dryden, Tal Ben-Nun +2
Transformers are one of the most important machine learning workloads today. Training one is a very compute-intensive task, often taking days or weeks, and significant attention ha…
Deep Learning for Post-Processing Ensemble Weather Forecasts
Peter Grönquist, Chengyuan Yao, Tal Ben-Nun +4
Quantifying uncertainty in weather forecasts is critical, especially for predicting extreme weather events. This is typically accomplished with ensemble prediction systems, which c…
Breaking (Global) Barriers in Parallel Stochastic Optimization with Wait-Avoiding Group Averaging
Shigang Li, Tal Ben-Nun, Giorgi Nadiradze +4
Deep learning at scale is dominated by communication time. Distributing samples across nodes usually yields the best performance, but poses scaling challenges due to global informa…
ProGraML: Graph-based Deep Learning for Program Optimization and Analysis
Chris Cummins, Zacharias V. Fisches, Tal Ben-Nun +2
The increasing complexity of computing systems places a tremendous burden on optimizing compilers, requiring ever more accurate and aggressive optimizations. Machine learning offer…