Demo Data from EDBT 2026 / reviewers in the wild / expert
Erwin Hernández
dblp:03/5232
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2015
—ORCID · unresolved
Domains — the database's venue-derived domains; a paper can count in several
Artificial intelligence and machine learning · 1
Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.
| Computer graphics and multimedia
1 paper |
Audio and music processing · 100% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
GPUs and heterogeneous computing · 100% |
Topics — the 4 heaviest of 4, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Audio and music processing › room acoustics › room acoustics simulation
finite-difference time-domain method |
0.2 | 1 | 2015 | A GPU Implementation of an Explicit Compact FDTD Algorithm with a Digital Impedance Filter for Room Acoustics Applications · IEEE ACM Trans. Audio Speech Lang. Process. 2015 |
Audio and music processing
room acoustics |
0.2 | 1 | 2015 | A GPU Implementation of an Explicit Compact FDTD Algorithm with a Digital Impedance Filter for Room Acoustics Applications · IEEE ACM Trans. Audio Speech Lang. Process. 2015 |
Audio and music processing › acoustic simulation
room impulse response synthesis |
0.2 | 1 | 2015 | A GPU Implementation of an Explicit Compact FDTD Algorithm with a Digital Impedance Filter for Room Acoustics Applications · IEEE ACM Trans. Audio Speech Lang. Process. 2015 |
GPUs and heterogeneous computing
GPU computing |
0.1 | 1 | 2015 | A GPU Implementation of an Explicit Compact FDTD Algorithm with a Digital Impedance Filter for Room Acoustics Applications · IEEE ACM Trans. Audio Speech Lang. Process. 2015 |
Methods — techniques the papers use, named apart from their topics
digital impedance filter · 0.4
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2015 | A GPU Implementation of an Explicit Compact FDTD Algorithm with a Digital Impedance Filter for Room Acoustics ApplicationsabstractIn recent years, computational engineering has undergone great changes due to the development of the graphics processing unit (GPU) technology. For example, in room acoustics, the wave-based methods, that formerly were considered too expensive for 3-D impulse response simulations, are now chosen to exploit the parallel nature of GPU devices considerably reducing the execution time of the simulations. There exist contributions related to this topic that have explored the performance of different GPU algorithms; however, the computational analysis of a general explicit model that incorporates algorithms with different neighboring orders and a general frequency dependent impedance boundary model has not been properly developed. In this paper, we present a GPU implementation of a complete room acoustic model based on a family of explicit finite-difference time-domain (FDTD) algorithms. We first develop a strategy for implementing a frequency independent (FI) impedance model which is free from thread divergences and then, we extend the model adding a digital impedance filter (DIF) boundary subroutine able to compute the acoustic pressure of different nodes such as corners or edges without an additional performance penalty. Both implementations are validated and deeply analyzed by performing different 3-D numerical experiments. Finally, we define a performance metric which is able to objectively measure the computing throughput of a FDTD implementation using a simple number. The robustness of this metric allows us to compare algorithms even if these have been run in different GPU cards or have been formulated with other explicit models. Carlos Spa, Antón Rey, Erwin Hernández |
IEEE ACM Trans. Audio Speech Lang. Process. | 3 |