TaLPas will provide a solution to fast and robust simulation of many, inter-dependent particle systems in peta- and exascale supercomputing environments. This will be beneficial for a wide range of applications, including sampling in molecular dynamics (rare event sampling, determination of equations of state, etc.), uncertainty quantification (sensitivity investigation of parameters on actual simulation results), or parameter identification (identification of optimal parameter sets to fit numerical model and experiment). For this purpose, TaLPas targets 1. the development of innovative auto-tuning based particle simulation software in form of an open-source library to leverage optimal node-level performance. This will guarantee an optimal time-to-solution for small- to mid-sized particle simulations, 2. the development of a scalable task scheduler to optimally distribute inter-dependent particle simulation tasks on available HPC compute resources, 3. the investigation of performance prediction methods for particle simulations to support auto-tuning and to feed the scheduler with accurate runtime predictions, 4. the integration of auto-tuning based particle simulation, scalable task scheduler and performance prediction, augmented by visualisation of the sampling (parameter space exploration) and an approach to resilience. The latter will guarantee robustness at peta- and exascale. Work presented at ISC will focus on steps 1-3. The integration of all components (step 4) is anticipated for the year 2019. To reach its goals, TaLPas bundles interdisciplinary expert knowledge on high-performance computing, visualisation and resilience, performance modeling, and particle applications.

@misc{TTLBAAIPSN18,
	author	 = {Philipp Neumann},
	title	 = {{TaLPas: Task-Based Load Balancing and Auto-Tuning in Particle Simulations}},
	year	 = {2018},
	month	 = {06},
	location	 = {Frankfurt, Germany},
	activity	 = {ISC Project Poster Session},
	abstract	 = {TaLPas will provide a solution to fast and robust simulation of many, inter-dependent particle systems in peta- and exascale supercomputing environments. This will be beneficial for a wide range of applications, including sampling in molecular dynamics (rare event sampling, determination of equations of state, etc.), uncertainty quantification (sensitivity investigation of parameters on actual simulation results), or parameter identification (identification of optimal parameter sets to fit numerical model and experiment).
For this purpose, TaLPas targets
1. the development of innovative auto-tuning based particle simulation software in form of an open-source library to leverage optimal node-level performance. This will guarantee an optimal time-to-solution for small- to mid-sized particle simulations,
2. the development of a scalable task scheduler to optimally distribute inter-dependent particle simulation tasks on available HPC compute resources,
3. the investigation of performance prediction methods for particle simulations to support auto-tuning and to feed the scheduler with accurate runtime predictions,
4. the integration of auto-tuning based particle simulation, scalable task scheduler and performance prediction, augmented by visualisation of the sampling (parameter space exploration) and an approach to resilience. The latter will guarantee robustness at peta- and exascale.
Work presented at ISC will focus on steps 1-3. The integration of all components (step 4) is anticipated for the year 2019.
To reach its goals, TaLPas bundles interdisciplinary expert knowledge on high-performance computing, visualisation and resilience, performance modeling, and particle applications.},
}