اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدELT-scale Adaptive Optics real-time control with the Intel Xeon Phi Many Integrated Core Architecture
110
0
0.0
(
0
)
اسأل ChatGPT حول البحث
ﻻ يوجد ملخص باللغة العربية
We propose a solution to the increased computational demands of Extremely Large Telescope (ELT) scale adaptive optics (AO) real-time control with the Intel Xeon Phi Knights Landing (KNL) Many Integrated Core (MIC) Architecture. The computational demands of an AO real-time controller (RTC) scale with the fourth power of telescope diameter and so the next generation ELTs require orders of magnitude more processing power for the RTC pipeline than existing systems. The Xeon Phi contains a large number (> 64) of low power x86 CPU cores and high bandwidth memory integrated into a single socketed server CPU package. The increased parallelism and memory bandwidth are crucial to providing the performance for reconstructing wavefronts with the required precision for ELT scale AO. Here, we demonstrate that the Xeon Phi KNL is capable of performing ELT scale single conjugate AO real-time control computation at over 1.0 kHz with less than 20 {mu}s RMS jitter. We have also shown that with a wavefront sensor camera attached the KNL can process the real-time control loop at up to 966 Hz, the maximum frame-rate of the camera, with jitter remaining below 20 {mu}s RMS. Future studies will involve exploring the use of a cluster of Xeon Phis for the real-time control of the MCAO and MOAO regimes of AO. We find that the Xeon Phi is highly suitable for ELT AO real time control.
قيم البحث
اقرأ أيضاً
Cosmic dust particles effectively attenuate starlight. Their absorption of starlight produces emission spectra from the near- to far-infrared, which depends on the sizes and properties of the dust grains, and spectrum of the heating radiation field.
The near- to mid-infrared is dominated by the emissions by very small grains. Modeling the absorption of starlight by these particles is, however, computationally expensive and a significant bottleneck for self-consistent radiation transport codes treating the heating of dust by stars. In this paper, we summarize the formalism for computing the stochastic emissivity of cosmic dust, which was developed in earlier works, and present a new library HEATCODE implementing this formalism for the calculation for arbitrary grain properties and heating radiation fields. Our library is highly optimized for general-purpose processors with multiple cores and vector instructions, with hierarchical memory cache structure. The HEATCODE library also efficiently runs on co-processor cards implementing the Intel Many Integrated Core (Intel MIC) architecture. We discuss in detail the optimization steps that we took in order to optimize for the Intel MIC architecture, which also significantly benefited the performance of the code on general-purpose processors, and provide code samples and performance benchmarks for each step. The HEATCODE library performance on a single Intel Xeon Phi coprocessor (Intel MIC architecture) is approximately 2 times a general-purpose two-socket multicore processor system with approximately the same nominal power consumption. The library supports heterogeneous calculations employing host processors simultaneously with multiple coprocessors, and can be easily incorporated into existing radiation transport codes.
We demonstrate a novel architecture for Adaptive Optics (AO) control based on FPGAs (Field Programmable Gate Arrays), making active use of their configurable parallel processing capability. SPARCs unique capabilities are demonstrated through an imple
mentation on an off-the-shelf inexpensive Xilinx VC-709 development board. The architecture makes SPARC a generic and powerful Real-time Control (RTC) kernel for a broad spectrum of AO scenarios. SPARC is scalable across different numbers of subapertures and pixels per subaperture. The overall concept, objectives, architecture, validation and results from simulation as well as hardware tests are presented here. For Shack-Hartmann wavefront sensors, the total AO reconstruction time ranges from a median of 39.4us (11x11 subapertures) to 1.283 ms (50x50 subapertures) on the development board. For large wavefront sensors, the latency is dominated by access time (~1 ms) of the standard DDR memory available on the board. This paper is divided into two parts. Part 1 is targeted at astronomers interested in the capability of the current hardware. Part 2 explains the FPGA implementation of the wavefront processing unit, the reconstruction algorithm and the hardware interfaces of the platform. Part 2 mainly targets the embedded developers interested in the hardware implementation of SPARC.
The forthcoming Extremely Large Telescopes all require adaptive optics systems for their successful operation. The real-time control for these systems becomes computationally challenging, in part limited by the memory bandwidths required for wavefron
t reconstruction. We investigate new POWER8 processor technologies applied to the problem of real-time control for adaptive optics. These processors have a large memory bandwidth, and we show that they are suitable for operation of first-light ELT instrumentation, and propose some potential real-time control system designs. A CPU-based real-time control system significantly reduces complexity, improves maintainability, and leads to increased longevity for the real-time control system.
The main objective of the present project is to explore the viability of an adaptive optics control system based exclusively on Field Programmable Gate Arrays (FPGAs), making strong use of their parallel processing capability. In an Adaptive Optics (
AO) system, the generation of the Deformable Mirror (DM) control voltages from the Wavefront Sensor (WFS) measurements is usually through the multiplication of the wavefront slopes with a predetermined reconstructor matrix. The ability to access several hundred hard multipliers and memories concurrently in an FPGA allows performance far beyond that of a modern CPU or GPU for tasks with a well defined structure such as Adaptive Optics control. The target of the current project is to generate a signal for a real time wavefront correction, from the signals coming from a Wavefront Sensor, wherein the system would be flexible to accommodate all the current Wavefront Sensing techniques and also the different methods which are used for wavefront compensation. The system should also accommodate for different data transmission protocols (like Ethernet, USB, IEEE 1394 etc.) for transmitting data to and from the FPGA device, thus providing a more flexible platform for Adaptive Optics control. Preliminary simulation results for the formulation of the platform, and a design of a fully scalable slope computer is presented.
We give an overview of QPACE 2, which is a custom-designed supercomputer based on Intel Xeon Phi processors, developed in a collaboration of Regensburg University and Eurotech. We give some general recommendations for how to write high-performance co
de for the Xeon Phi and then discuss our implementation of a domain-decomposition-based solver and present a number of benchmarks.
سجل دخول لتتمكن من نشر تعليقات
التعليقات
جاري جلب التعليقات
سجل دخول لتتمكن من متابعة معايير البحث التي قمت باختيارها
