4 citations · 14 across the 16 of their papers we have counts for
18 papers
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…
Program Generation for Small-Scale Linear Algebra Applications
Daniele G. Spampinato, Diego Fabregat-Traver, Paolo Bientinesi +1
We present SLinGen, a program generation system for linear algebra. The input to SLinGen is an application expressed mathematically in a linear-algebra-inspired language (LA) that…
Accelerating the computation of FLAPW methods on heterogeneous architectures
Davor Davidović, Diego Fabregat-Traver, Markus Höhnerbach +1
Legacy codes in computational science and engineering have been very successful in providing essential functionality to researchers. However, they are not capable of exploiting the…
Hybrid CPU-GPU generation of the Hamiltonian and Overlap matrices in FLAPW methods
Diego Fabregat-Traver, Davor Davidović, Markus Höhnerbach +1
In this paper we focus on the integration of high-performance numerical libraries in ab initio codes and the portability of performance and scalability. The target of our work is F…
Accelerating scientific codes by performance and accuracy modeling
Diego Fabregat-Traver, Ahmed E. Ismail, Paolo Bientinesi
Scientific software is often driven by multiple parameters that affect both accuracy and performance. Since finding the optimal configuration of these parameters is a highly comple…
Large-scale linear regression: Development of high-performance routines
Alvaro Frank, Diego Fabregat-Traver, Paolo Bientinesi
In statistics, series of ordinary least squares problems (OLS) are used to study the linear correlation among sets of variables of interest; in many studies, the number of such var…
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…
Knowledge-Based Automatic Generation of Linear Algebra Algorithms and Code
Diego Fabregat-Traver
This dissertation focuses on the design and the implementation of domain-specific compilers for linear algebra matrix equations. The development of efficient libraries for such equ…
High Performance Solutions for Big-data GWAS
Elmar Peise, Diego Fabregat-Traver, Paolo Bientinesi
In order to associate complex traits with genetic polymorphisms, genome-wide association studies process huge datasets involving tens of thousands of individuals genotyped for mill…
Towards an Efficient Use of the BLAS Library for Multilinear Tensor Contractions
Edoardo Di Napoli, Diego Fabregat-Traver, Gregorio Quintana-Ortì +1
Mathematical operators whose transformation rules constitute the building blocks of a multi-linear algebra are widely used in physics and engineering applications where they are ve…
Algorithms for Large-scale Whole Genome Association Analysis
Elmar Peise, Diego Fabregat, Yurii Aulchenko +1
In order to associate complex traits with genetic polymorphisms, genome-wide association studies process huge datasets involving tens of thousands of individuals genotyped for mill…
Application-tailored Linear Algebra Algorithms: A search-based Approach
Diego Fabregat-Traver, Paolo Bientinesi
In this paper, we tackle the problem of automatically generating algorithms for linear algebra operations by taking advantage of problem-specific knowledge. In most situations, use…
Computing Petaflops over Terabytes of Data: The Case of Genome-Wide Association Studies
Diego Fabregat-Traver, Paolo Bientinesi
In many scientific and engineering applications, one has to solve not one but a sequence of instances of the same problem. Often times, the problems in the sequence are linked in a…
Solving Sequences of Generalized Least-Squares Problems on Multi-threaded Architectures
Diego Fabregat-Traver, Yurii Aulchenko, Paolo Bientinesi
Generalized linear mixed-effects models in the context of genome-wide association studies (GWAS) represent a formidable computational challenge: the solution of millions of correla…
High-throughput Genome-wide Association Analysis for Single and Multiple Phenotypes
Diego Fabregat-Traver, Yurii S. Aulchenko, Paolo Bientinesi
The variance component tests used in genomewide association studies of thousands of individuals become computationally exhaustive when multiple traits are analysed in the context o…
A Domain-Specific Compiler for Linear Algebra Operations
Diego Fabregat-Traver, Paolo Bientinesi
We present a prototypical linear algebra compiler that automatically exploits domain-specific knowledge to generate high-performance algorithms. The input to the compiler is a targ…