483 citations · 3k across the 177 of their papers we have counts for
9 papers · 1 filter
Assertion-Based Optimization of Quantum Programs
Thomas Häner, Torsten Hoefler, Matthias Troyer
Quantum computers promise to perform certain computations exponentially faster than any classical device. Precise control over their physical implementation and proper shielding fr…
The Convergence of Sparsified Gradient Methods
Dan Alistarh, Torsten Hoefler, Mikael Johansson +3
Distributed training of massive machine learning models, in particular deep neural networks, via Stochastic Gradient Descent (SGD) is becoming commonplace. Several families of comm…
Neural Code Comprehension: A Learnable Representation of Code Semantics
Tal Ben-Nun, Alice Shoshana Jakobovits, Torsten Hoefler
With the recent success of embeddings in natural language processing, research has been conducted into applying similar methods to code analysis. Most works attempt to process the…
Survey and Taxonomy of Lossless Graph Compression and Space-Efficient Graph Representations
Maciej Besta, Torsten Hoefler
Various graphs such as web or social networks may contain up to trillions of edges. Compressing such datasets can accelerate graph processing by reducing the amount of I/O accesses…
Transformations of High-Level Synthesis Codes for High-Performance Computing
Johannes de Fine Licht, Maciej Besta, Simon Meierhans +1
Spatial computing architectures promise a major stride in performance and energy efficiency over the traditional load/store devices currently employed in large scale computing syst…
μ-cuDNN: Accelerating Deep Learning Frameworks with Micro-Batching
Yosuke Oyama, Tal Ben-Nun, Torsten Hoefler +1
NVIDIA cuDNN is a low-level library that provides GPU kernels frequently used in deep learning. Specifically, cuDNN implements several equivalent convolution algorithms, whose perf…
Reproducible Floating-Point Aggregation in RDBMSs
Ingo Müller, Andrea Arteaga, Torsten Hoefler +1
Industry-grade database systems are expected to produce the same result if the same query is repeatedly run on the same input. However, the numerous sources of non-determinism in m…
Demystifying Parallel and Distributed Deep Learning: An In-Depth Concurrency Analysis
Tal Ben-Nun, Torsten Hoefler
Deep Neural Networks (DNNs) are becoming an important tool in modern computing applications. Accelerating their training is a major challenge and techniques range from distributed…
SparCML: High-Performance Sparse Communication for Machine Learning
Cedric Renggli, Saleh Ashkboos, Mehdi Aghagolzadeh +2
Applying machine learning techniques to the quickly growing data in science and industry requires highly-scalable algorithms. Large datasets are most commonly processed "data paral…