21 citations · 191 across the 67 of their papers we have counts for
31 papers · 1 filter
Tensor Algebra Processing Primitives (TAPP): Towards a Standard for Tensor Operations
Jan Brandejs, Niklas Hörnblad, Edward F. Valeev +4
To address the absence of a universal standard interface for tensor operations, we introduce the Tensor Algebra Processing Primitives (TAPP), a C-based interface designed to decoup…
Compilation of Generalized Matrix Chains with Symbolic Sizes
Francisco López, Lars Karlsson, Paolo Bientinesi
Generalized Matrix Chains (GMCs) are products of matrices where each matrix carries features (e.g., general, symmetric, triangular, positive-definite) and is optionally transposed…
Enabling mixed-precision in spectral element codes
Yanxiang Chen, Pablo de Oliveira Castro, Paolo Bientinesi +2
Mixed-precision computing has the potential to significantly reduce the cost of exascale computations, but determining when and how to implement it in programs can be challenging.…
Enabling mixed-precision with the help of tools: A Nekbone case study
Yanxiang Chen, Pablo de Oliveira Castro, Paolo Bientinesi +1
Mixed-precision computing has the potential to significantly reduce the cost of exascale computations, but determining when and how to implement it in programs can be challenging.…
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…
Program Generation for Linear Algebra Using Multiple Layers of DSLs
Daniele G. Spampinato, Diego Fabregat-Traver, Markus Püschel +1
Numerical software in computational science and engineering often relies on highly-optimized building blocks from libraries such as BLAS and LAPACK, and while such libraries provid…
The Generalized Matrix Chain Algorithm
Henrik Barthels, Marcin Copik, Paolo Bientinesi
In this paper, we present a generalized version of the matrix chain algorithm to generate efficient code for linear algebra problems, a task for which human experts often invest da…
Spin Summations: A High-Performance Perspective
Paul Springer, Devin Matthews, Paolo Bientinesi
Besides tensor contractions, one of the most pronounced computational bottlenecks in the non-orthogonally spin-adapted forms of the quantum chemistry methods CCSDT and CCSDTQ, and…
HPTT: A High-Performance Tensor Transposition C++ Library
Paul Springer, Tong Su, Paolo Bientinesi
Recently we presented TTC, a domain-specific compiler for tensor transpositions. Despite the fact that the performance of the generated code is nearly optimal, due to its offline n…
TTC: A Tensor Transposition Compiler for Multiple Architectures
Paul Springer, Aravind Sankaran, Paolo Bientinesi
We consider the problem of transposing tensors of arbitrary dimension and describe TTC, an open source domain-specific parallel compiler. TTC generates optimized parallel C++/CUDA…
Design of a high-performance GEMM-like Tensor-Tensor Multiplication
Paul Springer, Paolo Bientinesi
We present "GEMM-like Tensor-Tensor multiplication" (GETT), a novel approach to tensor contractions that mirrors the design of a high-performance general matrix-matrix multiplicati…
TTC: A high-performance Compiler for Tensor Transpositions
Paul Springer, Jeff R. Hammond, Paolo Bientinesi
We present TTC, an open-source parallel compiler for multidimensional tensor transpositions. In order to generate high-performance C++ code, TTC explores a number of optimizations,…
Algorithm 979: Recursive Algorithms for Dense Linear Algebra -- The ReLAPACK Collection
Elmar Peise, Paolo Bientinesi
To exploit both memory locality and the full performance potential of highly tuned kernels, dense linear algebra libraries such as LAPACK commonly implement operations as blocked a…
Knowledge-Based Automatic Generation of Partitioned Matrix Expressions
Diego Fabregat-Traver, Paolo Bientinesi
In a series of papers it has been shown that for many linear algebra operations it is possible to generate families of algorithms by following a systematic procedure. Although powe…
Automatic Generation of Loop-Invariants for Matrix Operations
Diego Fabregat-Traver, Paolo Bientinesi
In recent years it has been shown that for many linear algebra operations it is possible to create families of algorithms following a very systematic procedure. We do not refer to…
On the Performance Prediction of BLAS-based Tensor Contractions
Elmar Peise, Diego Fabregat-Traver, Paolo Bientinesi
Tensor operations are surging as the computational building blocks for a variety of scientific simulations and the development of high-performance kernels for such operations is kn…
A Study on the Influence of Caching: Sequences of Dense Linear Algebra Kernels
Elmar Peise, Paolo Bientinesi
It is universally known that caching is critical to attain high- performance implementations: In many situations, data locality (in space and time) plays a bigger role than optimiz…
MRRR-based Eigensolvers for Multi-core Processors and Supercomputers
Matthias Petschow
The real symmetric tridiagonal eigenproblem is of outstanding importance in numerical computations; it arises frequently as part of eigensolvers for standard and generalized dense…