Hardware-accelerated Simulation-based Inference of Stochastic Epidemiology Models for COVID-19

Epidemiology models are central to understanding and controlling large-scale pandemics. Several epidemiology models require simulation-based inference such as Approximate Bayesian Computation (ABC) to fit their parameters to observations. ABC inference is highly amenable to efficient hardware acceleration. In this work, we develop parallel ABC inference of a stochastic epidemiology model for COVID-19. The statistical inference framework is implemented and compared on Intel’s Xeon CPU, NVIDIA’s Tesla V100 GPU, Google’s V2 Tensor Processing Unit (TPU), and the Graphcore’s Mk1 Intelligence Processing Unit (IPU), and the results are discussed in the context of their computational architectures. Results show that TPUs are 3×, GPUs are 4×, and IPUs are 30× faster than Xeon CPUs. Extensive performance analysis indicates that the difference between IPU and GPU can be attributed to higher communication bandwidth, closeness of memory to compute, and higher compute power in the IPU. The proposed framework scales across 16 IPUs, with scaling overhead not exceeding 8% for the experiments performed. We present an example of our framework in practice, performing inference on the epidemiology model across three countries and giving a brief overview of the results.

Development of a hardware-accelerated simulation kernel for ultra-high vacuum with Nvidia RTX GPUs

Molflow+ is a Monte Carlo (MC) simulation software for ultra-high vacuum, mainly used to simulate pressure in particle accelerators. In this article, we present and discuss the design choices arising in a new implementation of its ray-tracing–based simulation unit for Nvidia RTX Graphics Processing Units (GPUs). The GPU simulation kernel was designed with Nvidia’s OptiX 7 API to make use of modern hardware-accelerated ray-tracing units, found in recent RTX series GPUs based on the Turing and Ampere architectures. Even with the challenges posed by switching to 32 bit computations, our kernel runs much faster than on comparable CPUs at the expense of a marginal drop in calculation precision.

Fast Antenna Optimization Using Gradient Monitoring and Variable-Fidelity EM Models

Accelerated simulation-driven design optimization of antenna structures is proposed. Variable-fidelity electromagnetic (EM) analysis is used as well as the trust-region framework with limited sensitivity updates. The latter are controlled by monitoring the changes of the antenna response gradients. Our methodology is verified using three compact wideband antennas. Comprehensive benchmarking demonstrates its superiority over both conventional and surrogate-assisted algorithms.

Accelerated Simulation of Large Reaction Systems Using a Constraint-Based Algorithm

Simulation of reaction systems has been employed along decades for a better understanding of such systems. However, the ever-growing gathering of biological data implied in larger and more complex models that are computationally challenging for current discrete-stochastic simulation methods. In this work, we propose a constraint-based algorithm to simulate such reaction systems, called the Constraint-Based Simulation Algorithm (CBSA). The main advantage of the proposed method is that it is intrinsically parallelizable, thus being able to be implemented in GPGPU architectures. We show through examples that our method can provide valid solutions when compared to the well-known Stochastic Simulation Algorithm (SSA). An analysis of computational efficiency showed that the CBSA tend to outperform other considered methods when dealing with a high number of molecules and reaction channels. Therefore, we believe that the proposed method constitutes an interesting alternative when simulating large chemical reaction systems.