341 citations · 451 across the 12 of their papers we have counts for
22 papers
Temporal Vectorization: A Compiler Approach to Automatic Multi-Pumping
Carl-Johannes Johnsen, Tiziano De Matteis, Tal Ben-Nun +2
The multi-pumping resource sharing technique can overcome the limitations commonly found in single-clocked FPGA designs by allowing hardware components to operate at a higher clock…
Pebbles, Graphs, and a Pinch of Combinatorics: Towards Tight I/O Lower Bounds for Statically Analyzable Programs
Grzegorz Kwasniewski, Tal Ben-Nun, Lukas Gianinazzi +5
Determining I/O lower bounds is a crucial step in obtaining communication-efficient parallel algorithms, both across the memory hierarchy and between processors. Current approaches…
Sparsity in Deep Learning: Pruning and growth for efficient inference and training in neural networks
Torsten Hoefler, Dan Alistarh, Tal Ben-Nun +2
The growing energy and performance costs of deep learning have driven the community to reduce the size of neural networks by selectively pruning components. Similarly to their biol…
Clairvoyant Prefetching for Distributed Machine Learning I/O
Nikoli Dryden, Roman Böhringer, Tal Ben-Nun +1
I/O is emerging as a major bottleneck for machine learning training, especially in distributed environments. Indeed, at large scale, I/O takes as much as 85% of training time. Addr…
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…