VLDB 2026 Research / reviewers in the wild / expert
Amit Gill
dblp:73/9488
· DBLP profile ↗
2ranked-venue papers
1as first author
1since 2021 · last 2021
0000-0001-6439-3673ORCID · reported
Domains — the database's venue-derived domains; a paper can count in several
Systems, architecture and hardware · 1 · 1 first-author · 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.
| Computer architecture, parallel and distributed computing, and storage systems
1 paper |
High-performance computing · 77% Parallel and multicore computing · 23% |
Topics — the 1 heaviest of 2, each with the papers that count most for it
| Topic | Weight | Papers | Last | Evidence papers |
|---|---|---|---|---|
High-performance computing
large-scale simulation |
0.5 | 1 | 2021 | High-Performance Computing Implementations of Agent-Based Economic Models for Realizing 1: 1 Scale Simulations of Large Economies · IEEE Trans. Parallel Distributed Syst. 2021 |
Methods — techniques the papers use, named apart from their topics
cache-efficient algorithms · 0.5OpenMP · 0.5MPI · 0.5
| Year | Publication | Venue | Position |
|---|---|---|---|
| 2021 | High-Performance Computing Implementations of Agent-Based Economic Models for Realizing 1: 1 Scale Simulations of Large EconomiesabstractWe present a scalable high-performance computing implementation of an agent-based economic model using distributed + shared-memory hybrid parallelization paradigms, capable of simulating 1:1 scale models of large economies like the eurozone. Agent-based economic models consist of millions of agents interacting over several graphs, which are either centralized or scale-free in nature. While most of the interactions are bi-directional, the interaction graphs are dense and random and keep evolving as the simulation progresses. These characteristics cause a very large and unknown number of random communications among MPI processes, posing challenges to developing scalable parallel extensions. Further, random access to large volume of data makes the algorithms highly memory-bound, severely degrading computational performance. Adopting various strategies inspired by the real-world functioning of economies, we reduce the large unknown number of communications to a known handful number. Memory-intensive algorithms are improved to make these cache-efficient, and advanced MPI functions are used to minimize communication overhead, thereby attaining higher performance and scalability. Further, an MPI + OpenMP hybrid model is developed to best utilize modern many-core computing nodes with low per-core memory capacity. It is demonstrated that our implementation can simulate a full fledged economic model with 331 million agents within 108 seconds using 128 CPU cores attaining 70 percent strong scalability. Amit Gill, Lalith Maddegedara, Sebastian Poledna, Muneo Hori, Kohei Fujita, Tsuyoshi Ichimura |
IEEE Trans. Parallel Distributed Syst. | 1 |
| 2010 | Protection through multimedia CAPTCHAsabstractCAPTCHAS which are well known as complete automatic public Turing test to tell computers and humans apart are a modern implementation of the Turing test, which ask a series of questions of two players: a human and computer. But both of the players pretend to be human. On the bases of the answers the judge has to decide which one is human and which one is computer, but the judge itself is a computer. In this article, we review current CAPTCHAs. After analysis of all the current CAPTCHAS we propose a new 3-D AI CATCHA which has all the strengths of existing CAPTCHAS to provide a better security alternative for ecommerce. Wesam Al-Sudani, Amit Gill, Chen Li 0016, Fei Liu 0003 |
MoMM | 2 |