Krylov methods for nonsymmetric linear systems
G Meurant, JD Tebbens - Cham: Springer, 2020 - Springer
Solving systems of algebraic linear equations is among the most frequent problems in
scientific computing. It appears in many areas like physics, engineering, chemistry, biology …
scientific computing. It appears in many areas like physics, engineering, chemistry, biology …
Subspace-orbit randomized decomposition for low-rank matrix approximations
MF Kaloorazi, RC de Lamare - IEEE Transactions on Signal …, 2018 - ieeexplore.ieee.org
An efficient, accurate, and reliable approximation of a matrix by one of lower rank is a
fundamental task in numerical linear algebra and signal processing applications. In this …
fundamental task in numerical linear algebra and signal processing applications. In this …
Flexfloat: A software library for transprecision computing
G Tagliavini, A Marongiu… - IEEE Transactions on …, 2018 - ieeexplore.ieee.org
In recent years approximate computing has been extensively explored as a paradigm to
design hardware and software solutions that save energy by trading off on the quality of the …
design hardware and software solutions that save energy by trading off on the quality of the …
Survey of storage systems for high-performance computing
In current supercomputers, storage is typically provided by parallel distributed file systems
for hot data and tape archives for cold data. These file systems are often compatible with …
for hot data and tape archives for cold data. These file systems are often compatible with …
Power-and cache-aware task mapping with dynamic power budgeting for many-cores
M Rapp, M Sagi, A Pathania… - IEEE Transactions …, 2019 - ieeexplore.ieee.org
Two factors primarily affect the performance of multi-threaded tasks on many-core
processors with logically-shared and physically-distributed Last-Level Cache (LLC): the LLC …
processors with logically-shared and physically-distributed Last-Level Cache (LLC): the LLC …
Compressed randomized UTV decompositions for low-rank matrix approximations
MF Kaloorazi, RC de Lamare - IEEE Journal of Selected Topics …, 2018 - ieeexplore.ieee.org
Low-rank matrix approximations play a fundamental role in numerical linear algebra and
signal processing applications. This paper introduces a novel rank-revealing matrix …
signal processing applications. This paper introduces a novel rank-revealing matrix …
Multi-phase task-based HPC applications: Quickly learning how to run fast
Parallel applications performance strongly depends on the number of resources. Although
adding new nodes usually reduces execution time, excessive amounts are often detrimental …
adding new nodes usually reduces execution time, excessive amounts are often detrimental …
ARRC: A random ray neutron transport code for nuclear reactor simulation
A massively parallel implementation of a recently developed technique for numerically
integrating the transport equation, The Random Ray Method (TRRM)(Tramm et al., 2017), is …
integrating the transport equation, The Random Ray Method (TRRM)(Tramm et al., 2017), is …
Level-based blocking for sparse matrices: Sparse matrix-power-vector multiplication
The multiplication of a sparse matrix with a dense vector (SpMV) is a key component in
many numerical schemes and its performance is known to be severely limited by main …
many numerical schemes and its performance is known to be severely limited by main …
Emerging applications of 3D integration and approximate computing in high-performance computing systems: unique security vulnerabilities
P Yellu, Z Zhang, MMR Monjur… - 2019 IEEE High …, 2019 - ieeexplore.ieee.org
High-performance computing (HPC) systems rely on new technologies such as emerging
devices, advanced integration techniques, and computing architecture to continue …
devices, advanced integration techniques, and computing architecture to continue …