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Ahmad Hesam

dblp:207/2242 · DBLP profile ↗
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2ranked-venue papers
0as first author
2since 2021 · last 2023
0000-0001-7331-1000ORCID · corroborated

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1 · 1 since 2021Applied, interdisciplinary, general and emerging computing · 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.

Computer architecture, parallel and distributed computing, and storage systems
2 papers
Performance modeling and evaluation · 79% High-performance computing · 21%
Interdisciplinary, comprehensive, and emerging computing
2 papers
Computational social science and digital humanities · 100%

Topics — the 3 heaviest of 3, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Performance modeling and evaluation › simulation
agent-based simulation
0.712023
High-Performance and Scalable Agent-Based Simulation with BioDynaMo · PPoPP 2023
Computational social science and digital humanities
agent-based simulation
0.612022
BioDynaMo: a modular platform for high-performance agent-based simulation · Bioinform. 2022
High-performance computing
scientific computing systems
0.212022
BioDynaMo: a modular platform for high-performance agent-based simulation · Bioinform. 2022

Methods — techniques the papers use, named apart from their topics

agent-based modeling · 1.3
YearPublicationVenuePosition
2023 High-Performance and Scalable Agent-Based Simulation with BioDynaMo
abstract
Agent-based modeling plays an essential role in gaining insights into biology, sociology, economics, and other fields. However, many existing agent-based simulation platforms are not suitable for large-scale studies due to the low performance of the underlying simulation engines. To overcome this limitation, we present a novel high-performance simulation engine.
Lukas Breitwieser, Ahmad Hesam, Fons Rademakers, Juan Gómez-Luna, Onur Mutlu
PPoPP2
2022 BioDynaMo: a modular platform for high-performance agent-based simulation
abstract
MOTIVATION: Agent-based modeling is an indispensable tool for studying complex biological systems. However, existing simulation platforms do not always take full advantage of modern hardware and often have a field-specific software design. RESULTS: We present a novel simulation platform called BioDynaMo that alleviates both of these problems. BioDynaMo features a modular and high-performance simulation engine. We demonstrate that BioDynaMo can be used to simulate use cases in: neuroscience, oncology and epidemiology. For each use case, we validate our findings with experimental data or an analytical solution. Our performance results show that BioDynaMo performs up to three orders of magnitude faster than the state-of-the-art baselines. This improvement makes it feasible to simulate each use case with one billion agents on a single server, showcasing the potential BioDynaMo has for computational biology research. AVAILABILITY AND IMPLEMENTATION: BioDynaMo is an open-source project under the Apache 2.0 license and is available at www.biodynamo.org. Instructions to reproduce the results are available in the supplementary information. SUPPLEMENTARY INFORMATION: Available at https://doi.org/10.5281/zenodo.5121618.
Lukas Breitwieser, Ahmad Hesam, Jean de Montigny, Vasileios Vavourakis, Alexandros Iosif, Jack Jennings, Marcus Kaiser, Marco Manca 0002, Alberto Di Meglio, Zaid Al-Ars, Fons Rademakers, Onur Mutlu, Roman Bauer 0001
Bioinform.2