Magnetic diagnostics are essential for the operation and understanding of a magnetic confinement fusion (MCF) device, for example, by providing in real-time , the axisymmetric measurements needed for plasma equilibrium reconstructions or magnetohydrodynamic instabilities control. In this paper, we present a fast and robust implementation on GPU architecture of an accurate algorithm suitable for the computation of synthetic magnetic measurements used both in real time applications and off-line reconstruction procedures. The proposed procedure is first applied to benchmark problems, to validate the compute unified device architecture implementation, and then to cases of practical interests in MCF.

Fast and Efficient Algorithms for Computational Electromagnetics on GPU Architecture

This paper proposes a new real-time Laplacian of Gaussian (RT-LoG) operator for scene text detection. This method takes advantage of the Gaussian kernel distribution in the spatial/scale-space domains and kernel decomposition with the box filtering method. Two levels of optimization are given. The first level of optimization within the spatial domain is obtained by box mutualization. The second level of optimization within the spatial/scale-space domains is performed using a mixed method for box selection. The proposed RT-LoG operator is evaluated on the ICDAR2017 RRC-MLT dataset in terms of robustness and time processing. The results are compared with the state-of-the-art real-time operators for scene text detection. The proposed operator appears as the top performance with the best trade-off between robustness and time processing. The proposed operator can support approximately 30 frames per second (FPS) up to the Quad-HD resolution on a regular CPU architecture with a low-level latency. In addition, the proposed operator can support the full pipeline for scene text detection. Our system is competitive with the top accurate systems of the literature while processing with a difference of two orders of magnitude in term of processing resources.

/pdf/fast-rt-log-operator-for-scene-text-detection-2rdblk0fy4.pdf

Fast RT-LoG operator for scene text detection

Extreme Ultraviolet (EUV) photolithography is seen as the key enabler for increasing transistor density in the next decade. In EUV lithography, 13.5 nm EUV light is illuminated through a reticle, holding a pattern to be printed, onto a silicon wafer. This process is performed about 100 times per wafer, at a rate of over a hundred wafers an hour. During this process, a certain percentage of the light energy is converted into heat in the wafer. In turn, this heat causes the wafer to deform which increases the overlay error, and as a result, reduces the manufacturing yield. To alleviate this, we propose a firm real-time control system that uses a wafer heat feed-forward model to compensate for the wafer deformation. The model calculates the expected wafer deformation, and then, compensates for that by adjusting the light projection and/or the wafer movement. However, the model computational demands are very high. As a result, it needs to be executed on dedicated HW that can perform computations quickly. To this end, we deploy Graphics Processing Units (GPUs) to accelerate the calculations. In order to fit the computations within the required time budgets, we combine in a novel manner multiple techniques, such as compression and mixed-precision arithmetic, with recent advancements in GPUs to build a GPU-based real-time control system. A proof-of-concept implementation using NVIDIA P100 GPUs is able to deliver decompression throughput of 33 GB/s and a sustained 198 GFLOP/s per GPU for mixed-precision dense matrix-vector multiplication.

GPU Acceleration of Real-Time Control Loops.

Reinforcement learning is a promising approach for manufacturing processes. Process knowledge can be gained automatically, and autonomous tuning of control is possible. However, the use of reinforcement learning in a production environment imposes specific requirements that must be met for a successful application. This article defines those requirements and evaluates three reinforcement learning methods to explore their applicability. The results show that convolutional neural networks are computationally heavy and violate the real-time execution requirements. A new architecture is presented and validated that allows using GPU-based hardware acceleration while meeting the real-time execution requirements.

/pdf/reinforcement-learning-methods-based-on-gpu-accelerated-3wb44sl43u.pdf

Reinforcement learning methods based on GPU accelerated industrial control hardware

The real-time control systems industry is moving towards the consolidation of multiple computing systems into fewer and more powerful ones, aiming for a reduction in size, weight, and power. The increasing demand for higher performance in other critical domains like autonomous driving has led the industry to recently include embedded GPUs for the implementation of advanced functionalities. The highly parallel architecture of GPUs could also be leveraged in the control systems industry to develop more advanced, energy-efficient, and scalable control systems. However, the closed-source and non-deterministic nature of GPUs complicates the resource provisioning analysis required for the implementation of critical real-time systems. On the other hand, there is no indication of the integration of GPUs in the traditional development cycle of control systems, which is oriented to the use of a model-based design approach. Recently, some model-based design tools vendors have extended their development frameworks with GPU code generation capabilities targeting hybrid computing platforms, so that the model-based design environment now enables the concurrent analysis of more complex and diverse functions by simulation and automating the deployment to the final target. However, there is no indication whether these tools are well-suited for the design and development of time-sensitive systems.&#x0D; Motivated by these challenges, in this thesis, we contribute to the state of the art of real-time control systems towards the adoption of embedded GPUs by providing tools to facilitate the resource provisioning analysis and the integration in the model-based design development cycle. First, we present a methodology and an automated tool to extract the properties of GPU memory allocators. This tool allows the computation of the real amount of memory used by GPU applications, facilitating a correct resource provisioning analysis. Then, we present a library which allows the characterization of the use of dynamic memory in GPU applications. We use this library to characterize GPU benchmarks and we identify memory allocation patterns that could be modified to improve performance and memory consumption when targeting embedded GPUs. Based on these results, we present a tool to optimize the use of dynamic memory in legacy GPU applications executed on embedded platforms. This tool allows us to minimize the memory consumption and memory management overhead of GPU applications without rewriting them. Afterwards, we analyze the timing of control algorithms executed in embedded GPUs and we identify techniques to achieve an acceptable real-time behavior. Finally, we evaluate model-based design tools in terms of integration with GPU hardware and GPU code generation, and we propose improvements for the model-based generated GPU code. Then, we present a source-to-source transformation tool to automatically apply the proposed improvements. La industria de los sistemas de control en tiempo real avanza hacia la consolidación de múltiples sistemas informáticos en menos y más potentes sistemas, con el objetivo de reducir el tamaño, el peso y el consumo. La creciente demanda de un mayor rendimiento en otros dominios críticos, como la conducción autónoma, ha llevado a la industria a incluir recientemente GPU embebidas para la implementación de funcionalidades avanzadas. La arquitectura altamente paralela de las GPU también podría aprovecharse en la industria de los sistemas de control para desarrollar sistemas de control más avanzados, eficientes energéticamente y escalables. Sin embargo, la naturaleza privativa y no determinista de las GPUs complica el análisis de aprovisionamiento de recursos requerido para la implementación de sistemas críticos en tiempo real. Por otro lado, no hay indicios de la integración de las GPU en el ciclo de desarrollo tradicional de los sistemas de control, que está orientado al uso de un enfoque de diseño basado en modelos. Recientemente, algunos proveedores de herramientas de diseño basado en modelos han ampliado sus entornos de desarrollo con capacidades de generación de código de GPU dirigidas a plataformas informáticas híbridas, de modo que el entorno de diseño basado en modelos ahora permite el análisis simultáneo de funciones más complejas y diversas mediante la simulación y la automatización de la implementación para el objetivo final. Sin embargo, no hay indicación de si estas herramientas son adecuadas para el diseño y desarrollo de sistemas sensibles al tiempo. Motivados por estos desafíos, en esta tesis contribuimos al estado del arte de los sistemas de control en tiempo real hacia la adopción de GPUs integradas al proporcionar herramientas para facilitar el análisis de aprovisionamiento de recursos y la integración en el ciclo de desarrollo de diseño basado en modelos. Primero, presentamos una metodología y una herramienta automatizada para extraer las propiedades de los asignadores de memoria en GPUs. Esta herramienta permite el cómputo de la cantidad real de memoria utilizada por las aplicaciones GPU, facilitando un correcto análisis del aprovisionamiento de recursos. Luego, presentamos una librería que permite la caracterización del uso de memoria dinámica en aplicaciones de GPU. Usamos esta librería para caracterizar una serie de benchmarks GPU e identificamos patrones de asignación de memoria que podrían modificarse para mejorar el rendimiento y el consumo de memoria al utilizar GPUs embebidas. Con base en estos resultados, presentamos también una herramienta para optimizar el uso de la memoria dinámica en aplicaciones de GPU heredadas al ser ejecutadas en plataformas embebidas. Esta herramienta nos permite minimizar el consumo de memoria y la sobrecarga de administración de memoria de las aplicaciones GPU sin necesidad de reescribirlas. Posteriormente, analizamos el tiempo de los algoritmos de control ejecutados en GPUs embebidas e identificamos técnicas para lograr un comportamiento de tiempo real aceptable. Finalmente, evaluamos las herramientas de diseño basadas en modelos en términos de integración con hardware GPU y generación de código GPU, y proponemos mejoras para el código GPU generado por las herramientas basadas en modelos. Luego, presentamos una herramienta de transformación de código fuente para aplicar automáticamente al código generado las mejoras propuestas. 

pdf/real-time-high-performance-computing-for-embedded-control-2o54d5xybo.pdf

Real-time high-performance computing for embedded control systems

The recent advances in GPU technology is offering great prospects in computation. However, the penetration of the GPU technology in real-time control has been somewhat limited due to two main reasons: 1) control algorithms for real-time applications involving highly parallel computation are not very common in practical applications and 2) the excellent performance in computation of GPUs is paid for by a penalty in memory transfer. As a consequence, GPU applications for real-time controls suffer from an often unacceptable latency. We present the factors that affect the performance of GPUs in real-time applications in fusion research in order to provide some hints to designers facing the option of using either a multithreaded, multicore CPU application or a GPU. In particular, we consider GPU usage in two common use cases in real-time applications in fusion research: dense matrix-vector multiplication for large state space-based control and online image analysis for feature extraction in camera-based diagnostics. Two applications mimicking the two use cases have been developed using the Tesla K40 GPU architecture, and the performance results are reported.

Assessment of General Purpose GPU Systems in Real-Time Control

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Assessment of General Purpose GPU Systems in Real-Time Control