Demo Data from EDBT 2026 / reviewers in the wild / expert
Ashwin Venkatram
dblp:373/7186
· DBLP profile ↗
1ranked-venue papers
0as first author
1since 2021 · last 2024
0009-0005-4661-0060ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 since 2021Software engineering, systems software and programming languages · 1 · 1 since 2021
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.
| Software engineering, system software, and programming languages
1 paper |
Compilers and program optimization · 100% | |
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
GPUs and heterogeneous computing · 100% | |
| Artificial intelligence
1 paper |
Learning theory · 100% |
Topics — the 3 heaviest of 3, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
Compilers and program optimization › deep learning compiler
operator fusion |
0.8 | 1 | 2024 | Optimal Kernel Orchestration for Tensor Programs with Korch · ASPLOS (3) 2024 |
GPUs and heterogeneous computing › GPU scheduling
GPU kernel scheduling |
0.8 | 1 | 2024 | Optimal Kernel Orchestration for Tensor Programs with Korch · ASPLOS (3) 2024 |
Machine learning › Learning theory › neural network theory
tensor programs |
0.2 | 1 | 2024 | Optimal Kernel Orchestration for Tensor Programs with Korch · ASPLOS (3) 2024 |
Methods — techniques the papers use, named apart from their topics
kernel orchestration optimization · 2.3
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2024 | Optimal Kernel Orchestration for Tensor Programs with KorchabstractKernel orchestration is the task of mapping the computation defined in different operators of a deep neural network (DNN) to the execution of GPU kernels on modern hardware platforms. Prior approaches optimize kernel orchestration by greedily applying operator fusion, which fuses the computation of multiple operators into a single kernel, and miss a variety of optimization opportunities in kernel orchestration. Muyan Hu, Ashwin Venkatram, Shreyashri Biswas, Balamurugan Marimuthu, Bohan Hou, Gabriele Oliaro, Haojie Wang 0004, Liyan Zheng 0001, Xupeng Miao, Jidong Zhai |
ASPLOS (3) | 2 |