CN114895459A - Real-time controller for adaptive optical wavefront on surface layer - Google Patents
Real-time controller for adaptive optical wavefront on surface layer Download PDFInfo
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Abstract
The invention discloses a real-time controller of a surface adaptive optics wavefront, which is a parallel processing hardware platform provided for surface adaptive optics with 5-10 angular divisions and an ultra-large field of view. The invention can complete the functions of image acquisition, slope calculation, data synchronization, wavefront restoration, wavefront control, voltage output, UI interface monitoring and the like of a plurality of multi-sight line-related shack-Hartmann wavefront sensors in an oversized view field. The invention adopts a cluster server architecture and takes a general multi-core CPU cluster server as a computing platform. In the real-time controller, an FPGA acquisition card acquires camera image data and sends the camera image data to a cluster computing platform, and the cluster computing platform completes wave front computing and control tasks; the method is suitable for the field of adaptive optics, and has important significance for realizing the surface layer adaptive optics technology with the ultra-large visual field.
Description
Technical Field
The invention belongs to the field of adaptive optics, and particularly relates to a real-time controller for a wavefront of surface adaptive optics.
Background
In order to meet the observation requirements of astronomical observation on larger field of view and higher resolution, the Ground-layer adaptive Optics (GLAO) technology is one of the major research directions in the field of adaptive Optics. Based on the distribution characteristic that atmospheric turbulence is mainly concentrated on the surface layer, the GLAO technology utilizes a single deformable mirror to carry out real-time control and correction on aberration introduced by the surface layer turbulence, so that high-resolution imaging is realized in a large field of view. Subject to the time-coherent nature of atmospheric turbulence, real-time controllers need to achieve correction frequencies of several kilohertz to achieve effective correction of dynamic distortions. Real-time correction of aberrations within a large field of view places high demands on the performance of real-time controllers. The tasks of the real-time controller include: and performing slope extraction on an input image from the wavefront detector, completing wavefront restoration and wavefront control calculation, and finally outputting voltage data to a corrector to realize real-time correction of wavefront aberration. In the calculation process, the real-time controller generates an interrupt every time when receiving one frame of image, and simultaneously, the voltage calculation result of the current frame needs to be calculated before the next frame of image arrives, so that the correction real-time performance of the GLAO system is ensured. Hence, real-time controllers have a significant impact on the GLAO system.
In recent years, with the continuous development of computer technology, the hardware architecture of a multi-core CPU general-purpose computing platform has become one of the main hardware architecture choices of the real-time controller of the adaptive optics system. Wavefront Real-Time controllers (JENKINS, DAVID, RICHARD (2019) A Protopype Adaptive Optics Real-Time Control Architecture for extreme astronomical telescope (ELT) using Man-Core CPUs, Durham's instruments, developed by the University of Dulun, UK for the Adaptive Optics system planned to be carried by the next generation of the night astronomical oversized caliber (30-40m) telescope (ELT). The real-time controller selects an Intel to high-intensity fusion core processor and a CentOS Linux 7.3 operating system. After the technical schemes of kernel pruning, real-time kernel patching, hyper-threading closing, kernel separation and the like are implemented, the real-time correction frequency of the wavefront aberration of the AO system can reach about 1000Hz, and the target correction frequency above 500Hz is met. With the increasing of the aperture of the telescope and the detection field of view, the requirements of the AO system on a real-time controller are increased. In order to break through the performance limit of a single Multi-core CPU server on a real-time controller, in a Multi-Conjugate Adaptive Optics (MCAO) system which is being upgraded by Daniel k. In the MCAO system, a wavefront sensor camera adopts 9 customized PCO cameras to perform wavefront detection, and image data are directly transmitted to a cluster computing platform through a CameraLink interface; in the cluster computing platform, data transmission is realized among computing nodes through Infiniband HDR. The design index of the real-time controller is to realize a real-time correction frequency of about 2000Hz in a field of view of 30-60 angular seconds under an MCAO working mode (Dirk Schmidt, Andrew bead, Andrew Ferayorn, Scott group, Luke Johnson, Jose Marino, Lukas Rimmele, Thomas Rimmele, "Adding multi-joint Adaptive Optics to the Daniel K. Inouye Solar T Optics," Proc. SPIE 11448, Adaptive Optics Systems VII,114480F (8January 2021)).
For the solar surface layer adaptive optical system with the 5-10-angle super-large view field, the detection target surface of the required shack-Hartmann wavefront sensor is more than 5000 × 5000 pixels, and a plurality of cameras are also required to detect synchronously. Meanwhile, the increase of the detection target surface leads to the increase of the sub-aperture, the number of sub-regions and the number of deformable mirror units in the field of view, which brings huge calculation amount and performance challenges to the real-time controller of the GLAO system. In order to realize real-time correction of wavefront aberration by the GLAO system, clustering is one of the development trends of real-time controller hardware architecture. In the DKIST's MCAO system, the customization of the cameras and cluster computing platforms limits the flexibility of real-time controller development. Therefore, in order to more conveniently realize the engineering application of the adaptive optical system, a general clustering hardware architecture is urgently needed for the wavefront real-time controller at present.
Aiming at the problems, the invention provides a general multi-core CPU cluster server hardware architecture suitable for a large-field-of-view adaptive optical wavefront real-time controller, and provides powerful support for realizing a super-large-field-of-view surface layer adaptive optical system.
Disclosure of Invention
The technical problem to be solved by the invention is as follows: aiming at the problem of performance requirements of a large-visual-field adaptive optical wavefront real-time controller, the ground surface layer adaptive optical system wavefront real-time controller based on a general multi-core CPU cluster server framework is provided, functions such as wavefront detection, slope calculation, wavefront restoration, wavefront control, voltage output and UI (user interface) monitoring in a large visual field are completed, and the calculation delay and jitter of the real-time controller are controlled within a required range so as to meet the high bandwidth requirements of the ground surface layer adaptive optical system with the ultra-large visual field.
The technical scheme adopted by the invention for solving the technical problems is as follows:
a real-time controller of an adaptive optical wavefront on a surface of a ground, characterized by: the real-time controller is a control core of a surface layer adaptive optical system with an ultra-large field of view of 5-10 angles, is used for acquiring wavefront information in the field of view in real time, and completes the driving of a correction device after processing the wavefront information in the field of view in real time, so as to finally realize the real-time correction of wavefront aberration; the real-time controller adopts a general multi-core CPU cluster server architecture, and the cluster server architecture mainly comprises the following two parts: an FPGA acquisition card and a cluster server computing platform; the FPGA acquisition card is configured to receive a camera signal, complete preprocessing of image data and send the preprocessed image data to the cluster server computing platform; the cluster server computing platform is configured to receive the preprocessed image data in real time, complete slope extraction, wavefront restoration and control, obtain a control signal of the wavefront corrector, and drive the wavefront corrector to complete real-time correction of wavefront aberrations.
Furthermore, the cluster server architecture is provided with N FPGA acquisition cards in common, and the FPGA acquisition cards are respectively used for correspondingly acquiring signals of N large-view-field shack-Hartmann wavefront sensor cameras; the N cameras are synchronously triggered by one controller, and images obtained by the cameras are transmitted to the FPGA acquisition card through a first high-speed data transmission interface; the FPGA acquisition card is configured to perform dark field reduction and flat field multiplication on each frame of received image and send the processed data to a corresponding server in the cluster computing platform through a second high-speed data transmission interface.
Further, the first high-speed data transmission interface is a CameraLink or an optical fiber.
Further, the second high-speed data transmission interface is PCIe or an optical fiber.
Further, the cluster server computing platform comprises 1 master control computing node and N-1 slave control computing nodes; each slave control computing node is configured to receive image data transmitted by one FPGA acquisition card and complete slope calculation; after the slave control computing node finishes the slope calculation, the obtained slope data is transmitted to the master control computing node through a third high-speed data transmission interface; data synchronization is realized among the computing nodes through a communication protocol; the master control computing node is configured to perform wavefront restoration and wavefront control operation after all slave control computing nodes complete slope data transmission to obtain a corrector control signal, and drive the corrector to realize real-time correction of wavefront aberration based on the corrector control signal.
Furthermore, a single computing node adopts a universal multi-core CPU server, the universal multi-core CPU server adopts a Linux operating system, a non-real-time system is transformed into a real-time system through real-time kernel transformation, multi-thread parallel computing is realized through core separation and thread binding, and the computing speed of the single computing node is improved.
Further, the third high-speed data transmission interface is ethernet, optical fiber or infiniband hdr, and the communication protocol is OpenMP.
Furthermore, each processing core of the multi-core CPU server in a single slave computing node is a real-time processing core; and part of processing cores of the multi-core CPU server in the master control computing node are non-real-time processing cores and are used for finishing UI interface display and interaction of the adaptive optical system, and the rest of processing cores are real-time processing cores.
Compared with the prior art, the invention has the following advantages:
the invention provides a general multi-core CPU cluster server hardware architecture, which can be compatible with various signal transmission interfaces at a camera signal end and various inter-node data transmission modes and communication protocols in a cluster computing platform, so that a real-time controller of a large-view-field adaptive optical system can be flexibly configured according to requirements, and the development difficulty of the real-time controller is reduced. In the real-time controller, the FPGA acquisition card acquires camera image data and sends the camera image data to the cluster computing platform, and the cluster computing platform completes wave-front computation and control tasks. In the cluster architecture, a single multi-core CPU server adopts a Linux operating system, and the computing speed is improved by means of real-time optimization, parallel computing and the like; data interaction is realized among the servers through high-speed data transmission, and data synchronization is realized through a communication protocol. The general cluster server constructs a wavefront real-time controller to complete signal conversion through an FPGA acquisition card so as to be compatible with different types of wavefront sensing cameras; and the calculation performance of the real-time controller is improved through clustering, and the real-time correction of wavefront aberration in the ultra-large view field is realized. The method is suitable for the field of adaptive optics, and has important significance for realizing the surface layer adaptive optics technology with the ultra-large visual field.
Drawings
FIG. 1 is a block diagram of a real-time controller composition of a surface adaptive optical system;
FIG. 2 is a workflow of a master multicore CPU compute node in a cluster architecture;
FIG. 3 is a slave multicore CPU compute node workflow in a cluster architecture;
FIG. 4 is a working example of a real-time controller of a surface adaptive optical system based on a general multi-core CPU cluster server architecture.
Detailed Description
The invention is further elucidated with reference to the drawing.
As shown in fig. 1, a block diagram of a surface layer adaptive optical wavefront real-time controller based on a general multi-core CPU cluster server architecture is formed. The invention needs to complete a series of calculation tasks such as image acquisition, slope calculation, wavefront restoration, wavefront control and the like in a GLAO system in real time, and the frame frequency is required to be processed at thousands of hertz. The real-time controller mainly comprises a computing platform and the like, wherein the computing platform consists of N FPGA image acquisition cards, 1 master control multi-core CPU computing node and N-1 slave control multi-core CPU computing nodes. In order to ensure the real-time performance of the self-adaptive optical system, the optimization work of the real-time controller is mainly completed in a general multi-core CPU cluster computing platform.
Fig. 2 and fig. 3 show workflows of a master multicore CPU compute node and a slave multicore CPU compute node in a cluster architecture. Firstly, image data of a large-field-of-view shack-Hartmann wavefront sensor camera transmits the data to an FPGA acquisition card through a high-speed data transmission interface (including but not limited to CameraLink, optical fiber and the like), flat dark field preprocessing of an image is completed in the FPGA acquisition card, and then the preprocessed image data is sent to a corresponding computing node in a cluster computing platform through the high-speed data transmission interface (including but not limited to PCIe, optical fiber and the like). In the slave multi-Core CPU computing node, the master Core0 is responsible for reading image information, uniformly distributing the image information to each slave Core according to the sub-aperture, and completing slope computation in each slave Core in parallel. After the slave cores complete the slope calculation, the master core is responsible for summarizing the slope data and sending the slope data to the master multi-core CPU computing node through a high-speed data transmission interface (including but not limited to ethernet, optical fiber, Infiniband HDR, etc.). At this moment, the slave control multi-core CPU computing node completes a frame of computing task; in the master control multi-Core CPU computing node, the master Core0 is also responsible for reading the corresponding wavefront detection information and completing slope computation in parallel in each slave Core. After the slave cores complete the slope calculation, the master control calculation node needs to wait for the slope data transmitted by each slave control calculation node, and after the synchronization of the slope data of the current frame is completed, the master core is responsible for distributing the slope data, and completes the subsequent restoration and control calculation in parallel to obtain a voltage signal of the corrector, so that the corrector is driven to realize the real-time correction of the wavefront aberration.
In the general multi-core CPU cluster server architecture, a slave control computing node transmits slope data to a master control computing node through a high-speed data transmission interface, the slope data transmission quantity is 16Mb/s by taking 1024 sub-apertures 2048Hz correction frequency as an example, and low-delay data transmission can be realized by using data transmission protocols such as Ethernet, optical fiber, Infiniband HDR and the like under the data quantity; signal synchronization is realized among various computing nodes through a communication protocol (including but not limited to OpenMP and the like).
In order to realize the monitoring and control of the state of the self-adaptive optical system, a certain non-real-time processing core is separated from a main control multi-core CPU computing node to be responsible for UI interface display and interaction. A shared memory is opened up in a main control multi-core CPU server, a real-time control process frames and transmits data such as a wavefront detector image, a slope, recovery data, driver voltage and the like to the shared memory, and a UI process reads and visualizes the data. Meanwhile, the UI process can also send commands to control the running state of the real-time controller and complete the functions of controlling parameter writing, data storage and the like. In the priority setting of the processing core, the processing core bound by the UI process is given a lower priority, and the influence of the process on the real-time computing process is reduced as much as possible.
Fig. 4 shows a working example of a real-time controller based on a general multi-core CPU cluster server architecture. In the example, the multicore CPU server adopts a Centos Linux operating system, kernel trimming, hyper-threading closing and other settings are respectively carried out on each server, and Xenomai real-time patches are installed to complete kernel real-time transformation; and simultaneously, respectively performing core separation treatment on each server: in the slave control multi-core CPU server, all processing cores are real-time processing cores, a core0 is responsible for real-time task interrupt processing and distributing received image data, a core1-core N is responsible for slope calculation, and a core N +1 is responsible for inter-server communication and data transmission; in the master control multi-core CPU server, a core 0-core P is a real-time processing core, a core0 is also responsible for real-time task interrupt processing and data distribution, and a core1-core P is responsible for completing real-time calculation tasks such as slope calculation, wavefront restoration, wavefront control and the like; the main control multi-core CPU server opens up a shared memory, and data such as images, slopes, recovery results, driver voltages and the like are extracted and transmitted into the shared memory by a core 0; and meanwhile, two non-real-time processing cores are reserved for reading the shared memory data, and UI display and control of the self-adaptive optical system are realized. The slave control multi-core CPU server transmits the slope data to the master control multi-core CPU computer through the Ethernet, and performs communication among different nodes in the cluster architecture through OpenMP to realize data synchronization.
Parts of the invention not described in detail are well known in the art.
Claims (8)
1. A real-time controller of an adaptive optical wavefront on a surface of a ground, characterized by: the real-time controller is a control core of a surface layer adaptive optical system with an ultra-large field of view of 5-10 angles, is used for acquiring wavefront information in the field of view in real time, and completes the driving of a correction device after processing the wavefront information in the field of view in real time, so as to finally realize the real-time correction of wavefront aberration; the real-time controller adopts a general multi-core CPU cluster server architecture, and the cluster server architecture mainly comprises the following two parts: an FPGA acquisition card and a cluster server computing platform; the FPGA acquisition card is configured to receive a camera signal, complete preprocessing of image data and send the preprocessed image data to the cluster server computing platform; the cluster server computing platform is configured to receive the preprocessed image data in real time, complete slope extraction, wavefront restoration and control, obtain a control signal of the wavefront corrector, and drive the wavefront corrector to complete real-time correction of wavefront aberrations.
2. The real-time controller for the adaptive optics wavefront on the surface of the earth of claim 1, wherein: the cluster server framework is provided with N FPGA acquisition cards which are respectively used for correspondingly acquiring signals of N large-view-field shack-Hartmann wavefront sensor cameras; the N cameras are synchronously triggered by one controller, and images obtained by the cameras are transmitted to the FPGA acquisition card through a first high-speed data transmission interface; the FPGA acquisition card is configured to perform dark field reduction and flat field multiplication on each frame of received image and send the processed data to a corresponding server in the cluster computing platform through a second high-speed data transmission interface.
3. The real-time controller for the adaptive optics wavefront of the earth's surface as claimed in claim 2, wherein: the first high-speed data transmission interface is a CameraLink or an optical fiber.
4. The real-time controller for the adaptive optics wavefront of the earth's surface as claimed in claim 2, wherein: the second high-speed data transmission interface is PCIe or an optical fiber.
5. The real-time controller for the adaptive optics wavefront of the earth's surface as claimed in claim 1, wherein: the cluster server computing platform comprises 1 master control computing node and N-1 slave control computing nodes in total; each slave control computing node is configured to receive image data transmitted by one FPGA acquisition card and complete slope calculation; after the slave control computing node finishes the slope calculation, the obtained slope data is transmitted to the master control computing node through a third high-speed data transmission interface; data synchronization is realized among the computing nodes through a communication protocol; the master control computing node is configured to perform wavefront restoration and wavefront control operation after all slave control computing nodes complete slope data transmission to obtain a corrector control signal, and drive the corrector to realize real-time correction of wavefront aberration based on the corrector control signal.
6. The real-time controller of adaptive optics wavefront on the surface of earth as claimed in claim 5, wherein: the single computing node adopts a general multi-core CPU server, the general multi-core CPU server adopts a Linux operating system, a non-real-time system is transformed into a real-time system through real-time kernel transformation, and multi-thread parallel computing is realized through core separation and thread binding, so that the computing speed of the single computing node is improved.
7. The real-time controller of adaptive optics wavefront on the surface of earth as claimed in claim 5, wherein: the third high-speed data transmission interface is an Ethernet, an optical fiber or Infiniband HDR, and the communication protocol is OpenMP.
8. The surface adaptive optics wavefront real-time controller of claim 6, wherein: each processing core of the multi-core CPU server in a single slave computing node is a real-time processing core; and part of processing cores of the multi-core CPU server in the master control computing node are non-real-time processing cores and are used for finishing UI interface display and interaction of the adaptive optical system, and the rest of processing cores are real-time processing cores.
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