Cloning | Kalyan Perumalla

EpiClone: Epidemiological Clonable Simulations

Sat, 01 Jan 2022 00:00:00 +0000

Cloned simulations of epidemiological outbreaks like COVID-19 can now rapidly evaluate millions of what-if scenarios at national and world scales, and efficiently exploit 1000s of GPUs.

EpiClone model elements

EpiClone simulating USA scenarios

Overview

EpiClone represents the next advancements in what-if decision evaluation on state-of-the-art accelerated computing platforms including supercomputers containing thousands of GPUs.

EpiClone addresses global epidemiological outbreaks via an incremental simulation-based what-if decision tree evolution. EpiClone offers new scaling capabilities that were previously not possible before for rapidly simulating thousands or millions of epidemiological scenarios.

The EpiClone approach and results in scalable modeling and fast scenario exploration provide a leap in the analyses of epidemics and other complex systems.

Gallery

Organization

Sponsor: Lab Directed Research and Development, Oak Ridge National Laboratory
Period: 2015-2017

Scalable Cloning on Large-Scale GPU Platforms with Application to Time-Stepped Simulations on Grids

Cloning is a technique to efficiently simulate a tree of multiple what-if scenarios that are unraveled during the course of a base simulation. We present the conceptual simulation framework, algorithmic foundations, and runtime interface of CloneX, a new system we designed for scalable simulation cloning.

Srikanth Yoginath, Kalyan Perumalla

PDF Cite DOI

Mesoscopic Modeling and Rapid Simulation of Incremental Changes in Epidemic Scenarios on GPUs: Fast What–If Analyses of Localized and Dynamic Effects

A mesoscopic modeling approach is described that strikes a middle ground between macroscopic models based on coupled differential equations and microscopic models built on fine-grained behaviors at the individual entity level. Execution of our implementation scaled to 8192 GPUs of supercomputing platforms demonstrates the ability to rapidly evaluate what–if scenarios several orders of magnitude faster than the conventional methods.

Kalyan Perumalla, Maksudul Alam

PDF Cite DOI

Discrete Event Modeling and Massively Parallel Execution of Epidemic Outbreak Phenomena

In complex phenomena such as epidemiological outbreaks, the intensity of inherent feedback effects a…

Kalyan Perumalla, Sudip Seal

PDF Cite

Reversible Parallel Discrete-Event Execution of Large-scale Epidemic Outbreak Models

The spatial scale, runtime speed, and behavioral detail of epidemic outbreak simulations altogether …

Kalyan Perumalla, Sudip Seal

PDF Cite Slides

CloneX

Sat, 01 Jan 2022 00:00:00 +0000

Cloned eXecution of simulations is a powerful decision-making system to incrementally evaluate millions of what-if scenarios and scale to 1000s of GPUs.

Illustration of CloneX what-if tree on diffusion processes

CloneX computational and memory savings with increasing tree depth

Overview

CloneX is a novel system we designed for scalable simulation cloning, consisting of a conceptual simulation framework, algorithmic foundations, and a highly scalable runtime interface. It efficiently and dynamically creates whole logical copies of a dynamic tree of simulations across a large parallel system without full physical duplication of computation and memory.

CloneX Software Architecture

Titan Supercomputer with 1000s of GPUs

CloneX efficiently simulates a tree of multiple what-if scenarios unraveled during the course of a normal (base) simulation. Cloned execution is highly challenging to realize on large, distributed memory computing platforms, due to the dynamic nature of the computational load across clones, and due to the complex dependencies spanning the clone tree.

CloneX has been tested on 1000s of GPUs of a supercomputing system and evaluated with multiple benchmarks – such as heat diffusion, forest fire, and disease propagation models – delivering a speed up of over two orders of magnitude compared to replicated runs.

CloneX represents a major leap in ensemble simulations as a significantly faster and scalable way to execute many what-if scenarios of large simulations.

Organization

Sponsors: US Department of Energy (DOE)
- Office: Advanced Scientific Computing Research (ASCR)
  - Program: Early Career Research Program (ECRP)
- Office: ORNL Strategic Planning Office
  - Program: Laboratory-Directed Research and Develoopment (LDRD)
Period: 2015-2017

Gallery

Mesoscopic Modeling and Rapid Simulation of Incremental Changes in Epidemic Scenarios on GPUs: Fast What–If Analyses of Localized and Dynamic Effects

Kalyan Perumalla, Maksudul Alam

PDF Cite DOI

Scalable Cloning on Large-Scale GPU Platforms with Application to Time-Stepped Simulations on Grids

Srikanth Yoginath, Kalyan Perumalla

PDF Cite DOI

Mesoscopic Modeling and Rapid Simulation of Incremental Changes in Epidemic Scenarios on GPUs: Fast What–If Analyses of Localized and Dynamic Effects

Sun, 01 Aug 2021 00:00:00 +0000

https://link.springer.com/article/10.1007/s41745-021-00253-1

COVID-relevant Scalable Computational Research Directions and Tools

Thu, 09 Apr 2020 00:00:00 +0000

This seminar conveys some directions and technical ideas being pursued by the Discrete Computing Systems Group of the Computer Science and Mathematics Division, in collaboration with others at the lab. Some of the scalable computational tools ready for applying to challenging computational problems are presented. Actual working codes ready for customization to COVID-related efforts are described, which are built for scaling to supercomputing platforms such as Summit.

Topics that are covered include:

A high resolution simulator, ExaCorona, that scales from laptops to leadership class supercomputers, is outlined that uses a discrete event model of virus spread, with probabilistically timed state transitions at the individual level across millions of individuals represented with arbitray geography and mobility characteristics.
A clonable simulation framework, CloneX, is introduced that enables millions of “what-if” scenarios to be executed rapidly on thousands of GPUs of Summit and similar supercomputers. An SEIR-based epidemiological model is outlined for numerous what-if simulations of disease spread that can be executed for country-scale populations like India’s, with ‘what-if’ scenarios, each varying in the outbreak points (hotspots), quarantines, vaccinations and hospitalizations.
A machine learning pipeline for the prediction of material structure properties directly from their neutron scattering profiles (development as part of the ExaLearn ECP co-design project). A brief overview of this system is provided, which is being applied for studies of new therapeutic targets and viral protein-structure-assisted drug design studies related to the COVID outbreak.
Network science methods are outlined for detecting information cascades in time varying large-scale social communication networks. We discuss its implications for detecting occurrence/response or epidemic related events from Twitter and similar global interaction systems.

Energy Conservation Through Cloned Execution Of Simulations

Sun, 08 Dec 2019 00:00:00 +0000

https://ieeexplore.ieee.org/abstract/document/9004821

Exact-Differential Simulation: Differential Processing of Large-Scale Discrete Event Simulations

Tue, 18 Jun 2019 00:00:00 +0000

https://dl.acm.org/doi/abs/10.1145/3301499

Scalable Cloning on Large-Scale GPU Platforms with Application to Time-Stepped Simulations on Grids

Wed, 31 Jan 2018 00:00:00 +0000

https://dl.acm.org/doi/abs/10.1145/3158669

Cloning | Kalyan Perumalla

EpiClone: Epidemiological Clonable Simulations

Overview

Gallery

Organization

Related Publications

CloneX

Overview

Organization

Gallery

Related Publications

Mesoscopic Modeling and Rapid Simulation of Incremental Changes in Epidemic Scenarios on GPUs: Fast What–If Analyses of Localized and Dynamic Effects

COVID-relevant Scalable Computational Research Directions and Tools

Energy Conservation Through Cloned Execution Of Simulations

Exact-Differential Simulation: Differential Processing of Large-Scale Discrete Event Simulations

Scalable Cloning on Large-Scale GPU Platforms with Application to Time-Stepped Simulations on Grids