EDBT 2026 Demo / reviewers in the wild / expert
Xavier Garbet
dblp:119/5058
· DBLP profile ↗
1ranked-venue papers
0as first author
0since 2021 · last 2017
—ORCID · none
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 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 architecture, parallel and distributed computing, and storage systems
1 paper |
GPUs and heterogeneous computing · 36% High-performance computing · 28% Hardware accelerators and domain-specific architectures · 28% |
Topics — the 5 heaviest of 5, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Hardware accelerators and domain-specific architectures
accelerator optimization |
0.3 | 1 | 2017 | Optimization of Fusion Kernels on Accelerators with Indirect or Strided Memory Access Patterns · IEEE Trans. Parallel Distributed Syst. 2017 |
GPUs and heterogeneous computing › GPU memory management
GPU memory access optimization |
0.3 | 1 | 2017 | Optimization of Fusion Kernels on Accelerators with Indirect or Strided Memory Access Patterns · IEEE Trans. Parallel Distributed Syst. 2017 |
High-performance computing
stencil computation |
0.3 | 1 | 2017 | Optimization of Fusion Kernels on Accelerators with Indirect or Strided Memory Access Patterns · IEEE Trans. Parallel Distributed Syst. 2017 |
Storage systems
data layout |
0.1 | 1 | 2017 | Optimization of Fusion Kernels on Accelerators with Indirect or Strided Memory Access Patterns · IEEE Trans. Parallel Distributed Syst. 2017 |
GPUs and heterogeneous computing
GPU computing |
0.1 | 1 | 2017 | Optimization of Fusion Kernels on Accelerators with Indirect or Strided Memory Access Patterns · IEEE Trans. Parallel Distributed Syst. 2017 |
Methods — techniques the papers use, named apart from their topics
temporal blocking · 0.3register reuse · 0.3data layout transformation · 0.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2017 | Optimization of Fusion Kernels on Accelerators with Indirect or Strided Memory Access PatternsabstractThis paper describes optimization for high-dimensional stencil computations on accelerators involving complex memory access patterns, which appear in five dimensional fusion plasma turbulence codes, GYSELA and GT5D. They include different types of memory access patterns, the indirect memory access in GYSELA with a Semi-Lagrangian scheme and the strided memory access in GT5D with a Finite-Difference scheme. We focus on the affinity of the memory access patterns to accelerators such as GPGPUs and Xeon Phi coprocessors. On both devices, the Array of Structure of Array (AoSoA) data layout is preferable for contiguous memory accesses. It is shown that the effective local cache usage by improving spatial and temporal data locality is critical on Xeon Phi. On GPGPU, the texture memory usage improves the performance of the indirect memory accesses in the Semi-Lagrangian scheme. The reuse of registers by taking account of the physical symmetry of the Finite-Difference scheme reduces the amount of memory accesses. Through these optimizations, we achieve acceleration of 3.9 (8.1) on Xeon Phi (GPGPU) for the Semi-Lagrangian scheme and of 1.4 (3.9) on Xeon Phi (GPGPU) for the Finite-Different scheme with respect to the fully optimized codes on Sandy Bridge. Yuuichi Asahi, Guillaume Latu, Takuya Ina, Yasuhiro Idomura, Virginie Grandgirard, Xavier Garbet |
IEEE Trans. Parallel Distributed Syst. | 6 |