16 citations · 44 across the 7 of their papers we have counts for
7 papers · 1 filter
Benchmarking the Linear Algebra Awareness of TensorFlow and PyTorch
Aravind Sankaran, Navid Akbari Alashti, Christos Psarras +1
Linear algebra operations, which are ubiquitous in machine learning, form major performance bottlenecks. The High-Performance Computing community invests significant effort in the…
Accelerating jackknife resampling for the Canonical Polyadic Decomposition
Christos Psarras, Lars Karlsson, Rasmus Bro +1
The Canonical Polyadic (CP) tensor decomposition is frequently used as a model in applications in a variety of different fields. Using jackknife resampling to estimate parameter un…
The landscape of software for tensor computations
Christos Psarras, Lars Karlsson, Jiajia Li +1
Tensors (also commonly seen as multi-linear operators or as multi-dimensional arrays) are ubiquitous in scientific computing and in data science, and so are the software efforts fo…
Concurrent Alternating Least Squares for multiple simultaneous Canonical Polyadic Decompositions
Christos Psarras, Lars Karlsson, Rasmus Bro +1
Tensor decompositions, such as CANDECOMP/PARAFAC (CP), are widely used in a variety of applications, such as chemometrics, signal processing, and machine learning. A broadly used m…
Linnea: Automatic Generation of Efficient Linear Algebra Programs
Henrik Barthels, Christos Psarras, Paolo Bientinesi
The translation of linear algebra computations into efficient sequences of library calls is a non-trivial task that requires expertise in both linear algebra and high-performance c…
The Linear Algebra Mapping Problem. Current state of linear algebra languages and libraries
Christos Psarras, Henrik Barthels, Paolo Bientinesi
We observe a disconnect between the developers and the end users of linear algebra libraries. On the one hand, the numerical linear algebra and the high-performance communities inv…
Automatic Generation of Efficient Linear Algebra Programs
Henrik Barthels, Christos Psarras, Paolo Bientinesi
The level of abstraction at which application experts reason about linear algebra computations and the level of abstraction used by developers of high-performance numerical linear…