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The diagram of the F-engine module is shown in Figure 3. The Tianlai cylinder correlator system is an improvement upon the Tianlai dish correlator system, with enhancements including an increased number of input signals and additional new functions. The Tianlai dish correlator is very similar to the PAPER experiment correlator, which also uses the ROACH2 system. Please refer to (Niu et al., 2019) for details. Here, we provide a concise overview of the F-engine’s process and the functionality of the CASPER yellow block.

Each ROACH2 board is connected with two ADC boards through Z-DOK + connectors. The ADC board is the adc16 × 250-8 coax rev2 Q2 2013 version, which uses 4 HMCAD1511 chips and provides a total of 16 inputs. It samples 16 analog signal inputs with 8 bits at a rate of 250 Msps.

The output digital signal of the adc16 × 250 block is Fix_8_7 format, which indicates an 8-bit number with 7 bits after the decimal point. The ADC chip is accompanied by a control program developed by David MacMahon from the University of California, Berkeley. This program is responsible for activating the ADC, selecting the amplification level, calibrating the FPGA input delay, aligning the FPGA SERDES blocks until data is correctly framed, and performing other related tasks. A comprehensive user’s guide for the ADC16 chip is accessible on the CASPER website ⁴ . According to the actual range of input signal power, we conducted linearity tests on the ADC at various gain coefficients and also assessed the linearity of the correlator system. Ultimately, the ADC gain coefficient was set to 2.

The analog-to-digital converted data from the ADC is transmitted to the PFB (Polyphase Filter Bank) function module. PFB is a computationally efficient implementation of a filter bank, constructed by using an FFT (Fast Fourier Transform) preceded by a prototype polyphase FIR filter frontend (Price, 2021). The PFB not only ensures a relatively flat response across the channels but also provides excellent suppression of out-of-band signals. The PFB is implemented using the models pfb_fir and fft_biplex_real_2x from the CASPER module library.

Each pfb_fir ⁵ block (the signal processing blocks mentioned in this article can all be linked to the detailed page from here) processes two signals, configured with parameters including a PFB size of 2 11 , a Hamming window function, four taps, input width of 8 bits, an output width of 18 bits, and other settings. Each block takes two input signals, and a total of 16 pfb_fir blocks are used to process 32 input signals. Each fft_biplex_real_2x block processes four input data streams and outputs two sets of frequency domain data. Configured with parameters including an FFT size of 2 11 , an input width of 18 bits, an output width of 36 bits, and other settings. The parameter settings are based on the scientific requirements of the Tianlai project, which calls for a signal resolution of less than or equal to 0.2 MHz. There are eight fft_biplex_real_2x blocks, with each block taking in four data streams and outputting two sets of frequency domain data. The PFB module is flexible, making it very easy to adjust the parameters according to one’s requirements, such as the FFT size, PFB size, and the number of taps in the CASPER block.

The data output of the PFB module is 36 bits, which essentially represents a complex number with 18 bits for the real part and 18 bits for the imaginary part. Considering factors such as data transmission and hardware resources, the data is usually effectively truncated. In our case, we will truncate the complex number to have a 4-bit real part and a 4-bit imaginary part. Prior to quantizing to 4 bits, the PFB output values pass through a scaling (i.e., gain) stage. Each frequency channel of each input has its own scaling factor. The purpose of the scaling stage is to equalize the passband before quantization, so this stage is often referred to as EQ. The scaling factors are also known as EQ ⁶ coefficients and are stored in shared BRAMs.

The quantized data cannot be sent directly to the X-engine. Before sending it, we divide the frequency band and sort the data in a format that facilitates the relevant calculations. This module is called Transpose, and it is divided into four submodules. Each submodule processes 1/4 of the frequency band, resulting in a total of 256 frequency channels. The number of submodules corresponds to the number of 10 GbE network interface controllers (NICs) on the ROACH2 board, with each NIC used to receive and send data from the output of a transpose submodule. This module performs the data transpose, also known as a “corner turn” to arrange the data in the desired sequence. Additionally, it is responsible for generating the packet headers, which consist of MCNT (master counter), Fid (F-engine id), and Xid (X-engine id). The current parameter configuration of the sub-module is tailored for scenarios with 256 inputs or fewer. However, David MacMahon, the researcher behind the PAPER correlator system, has included sufficient spare bits in the design, enabling the adjustment of model parameters based on specific input conditions and accommodating scalability and additional use cases.

The data is already in a form that is easy for X-engine to compute, we want to send it to X-engine, so the data comes to the Ethernet module. It contains four sub-modules and receives data from four transpose sub-modules. Each submodule has a Ten_GbE_v2 block, where we can set the MAC address, IP address, destination port and other parameters using Python or Ruby script.

The data of the F-engine module is sent out through the ROACH2 network port and transmitted to the network port of the target GPU server through the 10 GbE switch. The network transmission model of the correlator system is dependent on the bandwidth of a single frequency channel and the number of frequency channels calculated by the GPU server. The diagram of data transfer from F-engine to X-engine is shown in Figure 4.

The frequency domain data in F-engine has a total of 1,024 frequency channels. Given the 250 Msps sampling rate, each frequency channel has a width of Δ ν = 125/1,024 MHz = 122.07 kHz. Each GPU node processes 128 frequency channels with a bandwidth of 128 × 122.07 kHz = 15.625 MHz. The number of frequency channels processed by the GPU server is determined by hashpipe.

The analog part of the Tianlai digital signal processing system uses replaceable bandpass filters, with the bandpass set to 700 MHz ∼ 800 MHz. We have chosen to utilize seven GPU nodes to implement the X-engine component. These seven GPU nodes process data for the central 896 frequency channels, covering a bandwidth of approximately 109.375 MHz from 692.8125 MHz to 802.1875 MHz, as shown in Figure 5. The final GPU node is dedicated to receiving data from the first 32 and last 32 frequency channels out of the 896 frequency channels.

The data transfer rate of a single network port of the ROACH2 board is 8.0152 Gbps, so the total data transfer rate of 6 ROACH2 boards is 6 × 4 ports × 8.0152 Gbps = 192.3648 Gbps. The data reception rate of a single network port on the GPU node is 192.3648 Gbps/ ( 8 × 4 ) = 6.0114 Gbps. At present, the number of input signals for the correlator system ranges from 32 to 256. While our correlator system is designed for 192 input signals, we conducted data transmission simulations with 256 input signals. Under these conditions, the data reception rate of a single network port on the GPU node stands at 8.0152 Gbps.

In our system, each GPU server has four 10 GbE ports. For the Tianlai cylinder correlator system, we require a total of 6 ROACH2 boards × 4 ports + 7 GPU servers × 4 ports = 52 ports on a 10 GbE switch. So we selected the Mellanox SX1024 switch which has 48 ports of 10 GbE and 12 ports of 40 GbE. Ports 59 and 60 on the switch can be subdivided into four 10 GbE ports, providing ample capacity for our application.

The transpose module is designed with extra bits reserved in the blocks related to the parameter fid. The number of bits in the fid parameter is directly linked to the maximum number of F-engines in the correlator system. By utilizing these additional bits, the correlator can be configured to accommodate a greater number of input signals. In terms of the F-engine, theoretically, there could be an infinite number of input channels, and the number of ROACH2 boards can be increased based on the input channel number. The capacity of X-engine determines the upper limit of input channels, depending on the processing capacity of the GPU servers for a single frequency point. Since each frequency point should contain all the input channel information, the processing capability of the GPU servers for a single frequency channel affects maximum number of input channels. Currently, a single server theoretically has the capability to handle over 20,000 input channels if it only processes one frequency channel. However, this may need an extremely large-scale switch network.

The relationship between the number of input channels and the output data rate is as shown in Eq. 1: 1 2 × N N + 1 × f _ c h × 2 × f _ b / I n t e g r a t i o n _ t i m e (1) where N represents the number of input channels, f _ c h represents the number of frequency channels, f _ b represents the number of bytes in a single frequency channel. The multiplying factor 2 is because frequency channels are complex numbers.

The primary role of the X-engine is to perform cross-correlation calculations. The X-engine receives the data from the F-engine in packets, which are then delivered to different computing servers, where the conjugate multiplication and accumulation (CMAC) are done. The hardware for this part consists mainly of six Supermicro servers and one Dell server. We list the main equipment of the X-engine in Table 1.

The X-engine part consists of seven GPU nodes. To ensure that they integrate the data at exactly the same time duration, they must be synchronized together. A script has been developed to achieve this, and its basic procedure is as follows. First, initialize the hashpipes of 7 GPU nodes; Second, start the hashpipe program of the first GPU node; Third, read out the MCNT value in the current packet and calculate a future (several seconds later) MCNT value to act as the aligning time point. Finally, all GPU nodes work simultaneously when their hashpipe threads receive a packet contains the calculated aligning MCNT value.

The data operation in the X-engine is managed by the hashpipe software running on CPU and GPU heterogeneous servers. Hashpipe was originally developed as an efficient shared pipe engine for the National Astronomical Observatory, the Universal Green Bank Astrospectrograph (Prestage et al., 2009). It was later adapted by David MacMahon of U.C. Berkeley, it can be used for FX correlators (Parsons et al., 2008), pulsar observations (Pei et al., 2021), Fast Radio Bursts detection (Yu et al., 2022a) and the search for extraterrestrial civilizations (Price et al., 2018). The core of the hashpipe is the flexible ring buffer. It simulates contiguous memory blocks, realizes data transmission and sharing among multiple threads, and uses the central processing unit to control startup and shutdown, etc. The ring buffer is used to temporarily store and deliver the data packets to ensure that the data is captured quickly and distributed in the correct order.

Each hashpipe instance in our system has a total of four threads and three buffers, as shown in Figure 6. To process the four 10 GbE ports data stream, four hashpipe instances are created. In each instance, the basic data process can be concluded as follows. First, net_thread receives the packets from the GPU server’s 10 GbE port. According to the packet format, the valid data is extracted and the packet header is analyzed. Packets are time-stamped, and if they arrive at the GPU server out of order, they can be rearranged into the appropriate time series and written to the input data buffer, which is passed onto the next thread once a consecutive block of data is filled. The fluff thread “fluffing” the data, fluffs 4bit+4bit complex data into 8bit+8bit complex data in the thread. The data is “fluffed” and temporarily stored in the GPU input data buffer until it is fetched by gpu_thread. Then gpu_thread transfers the data to the graphics processor to perform complex calculations and then writes the results to the output data buffer. The CMAC process uses the xGPU ⁷ (Clark et al., 2013), which is written in CUDA-C and is optimized on GPU memory resources by specific thread tasks. The cross-correlation algorithm involves computing the cross-power spectrum at a specific frequency observed by a pair of stations, known as a baseline. By processing a sufficient number of baselines, a detailed power spectrum representation can be derived, enabling the generation of an image of the sky through an inverse Fourier transform in the spatial domain. The algorithm’s implementation on Nvidia’s Fermi architecture sustains high performance by utilizing a software-managed cache, a multi-level tiling strategy, and efficient data streaming over the PCIe bus, showcasing significant advancements over previous GPU implementations. The output thread gets the data from the output data buffer and transmits it to the storage server through the switch. Hashpipe provides a status buffer that extract key-value pairs in each thread. This key value is updated every running cycle. The status can be viewed using a GUI monitor that has been written in both Python and Ruby.

At the beginning of the design, two schemes for data storage were considered. One is that the data is stored on each GPU server, and it is read and combined when used. Due to the large number of GPU servers, this method is too cumbersome. The other is that the data is transmitted from each GPU server to the master computer in real-time, and the data is stored in the master computer. This method is convenient for data use and processing, so the second scheme is adopted.

Each GPU node has 4 hashpipe instances, and the output thread of each hashpipe instance sends data to a dedicated destination port. A total of 28 different UDP ports are used for the 7 GPU servers. The data acquisition script, written in Python, collects data from all 28 UDP ports and combines them. Currently, the integration time is set to approximately 4 s, resulting in a data rate of about 150 Mbps for each network port. The total data rate for all seven servers with 28 ports amounts to approximately 4.2 Gbps. Therefore, a 10 GbE network is capable of handling the data transmission. Finally, the data are saved onto hard drives in the HDF5 format. Additional information such as integration time, observation time, telescope details, and observer information is also automatically saved in the file.

During the drift scan observation of the Tianlai cylinder array, the system needs to be calibrated by a calibrator noise source (CNS). The CNS periodically broadcasts a broadband white noise of stable magnitude from a fixed position, so the system gain can be recovered (Zuo et al., 2021; Zuo et al., 2019). One requirement in the data processing part is to let the CNS’s signal fall exactly in one integration time interval, so it needs to be aligned to the integration time. To achieve this, a logical ON/OFF signal from the cylinder correlator is necessary. In order to meet this requirement, we have introduced a noise source control function to the correlator system. This control function is implemented through the noise_source_control block in the F-engine, as shown in Figure 7A.

First, the script enables counter_en block to initialize the module. Second, the hashpipe instance on the GPU node returns the MCNT value of its current packet. The script uses this value to calculate the CNS MCNT value (an MCNT value at a future time, when the MCNT value in the F-engine is equal to this value, the CNS is turned ON) and sets that CNS MCNT to reg31_0 block and reg47_32 block. Third, the CNS on/off period is converted to the change value of MCNT and set period_mcnt block to this value. Fourth, set the GPIO’s working time to light block, which is on the ROACH2 board. Finally, the GPIO periodically sends out a logical signal to turn the CNS on or off.

We tested the accuracy of the CNS control module and its actual output result, as shown in Figure 7B. The CNS is activated based on a pre-set MCNT value and is aligned precisely with the integration time interval.

The importance of ADCs lies in their quality and performance, as these factors bear a direct impact on the overall functionality of the systems they inhabit. To verify the sampling correctness of the ADC, we input a 15.625 MHz sinusoidal wave signal into the ADC and fit the digitized data. The sampling points and fitting result are shown in Figure 8A. The correlator system requires the ADCs to have linearly sampled output at different signal levels. We plot the logarithm of the standard deviation of the ADC output with three different gain coefficients as a function of different input power levels, and the results are shown in Figure 8B. No obvious nonlinearity is found in the testing power range.

We verify the phase of the visibility (cross-correlation result) by two input signals, whose phase difference is determined by a cable length difference. We use a noise source generator to output the white noise signal and the signal is divided into two ways by a power splitter. Then, the two signals are fed into the ROACH2 board through two radio cables of different lengths. The cable length difference is 15 m. The two signals can be depicted as S 1 = A 1 e i ( 2 π f t + ϕ 0 ) and S 2 = A 2 e i ( 2 π f ( t + τ ) + ϕ 0 ) , where A is the wave amplitude, ϕ 0 is an arbitrary initial phase, f is frequency, τ is the delay incurred by the unequal-length RF cables. The visibility of two signals is V = < S 1 * ⋅ S 2 > = A 1 A 2 e i 2 π f τ (2) The cable length difference of the two input signals is fixed, so the delay τ is constant over time. As Eq. 2 shows, the phase Φ = 2 π τ ⋅ f , Φ is a linear function of frequency, and the slope k = 2 π τ . The delay τ = Δ l / c ̃ , where Δ l is the cable length difference, c ̃ is the propagation speed of RF signal in coaxial cable.

The measured waterfall 2D plot of phase of visibility output by our correlator in this experiment is plotted in Figures 9A, 1D plot (at one integration time) of phase as a function of frequency is shown in Figure 9B. By calculating the curve slope in Figure 9B, we obtain a propagation speed in the coaxial cable of about 0.78 c (0.78 times speed of light in vacuum), which is consistent with the specification of the RF cable.

The linearity of our correlator is verified by comparing the input power levels and the output amplitudes. The results are shown in Figure 10. Considering multiple factors, we have set the ADC gain coefficient for the correlator to 2. We can draw the conclusion that the linear dynamic range of our correlator is between −22 dBm and 0 dBm within the 125 MHz bandpass. In realistic observations, power levels output from the receivers vary 10 dB at most, so the 22 dB dynamic range of our correlator can satisfy our observation requirement.

The whole frequency band of each feed ranging from 692.8125 to 802.1875 MHz, is divided to 28 sub-bands. These sub-band have been sent to different hashpipe instances for correlation calculation. The final spectra are the combination of these 28 sub-bands. Some spectra of feeds (A10X, A19Y, B27X, and C12X) are plotted in Figure 11. The Tianlai cylinder array is aligned in the N-S direction and consists of three adjacent cylinders. They are designated as A, B and C from east to west, and have 31, 32, and 33 feeds respectively. Each dual linear polarization feed generates two signal outputs. We use “X” to denote the output for the polarization along the N-S direction and “Y” along E-W direction. Spectra of the selected feeds in Figure 11 are from three cylinders, and are smooth in adjacent frequency sub-bands. No obvious inconsistent processing amplitude in different sub-bands are found.

In these spectra, a periodic fluctuation of about 6.8 MHz can be seen. They have been confirmed to result from the standing wave in the 15-m feed cable (Li et al., 2021).

We made 4.4 h (16,000 s) of continuous observation since the night of August 7th, 2023, and the data are shown in Figure 12. The fringe of radio bright source Cassiopeia A occurred around 8000th second.

The continuous operation ability of the correlator is tested, and there is no fault in continuous operation for a month. We plot 4 days’ continuous observation data of three baselines as a function of LST (Local sidereal time) and frequency, as shown in Figure 13. The subplots from top to bottom show the baselines for two feeds (a) on the same cylinder, (b) on two adjacent cylinders, and (c) on two non-adjacent cylinders. Each subplot shows the result of four consecutive days starting from September 6th, 2023; each day is a sub-panel from bottom to top.

All devices are powered by PDU (Power Distribution Unit), and the voltage and current usage of the devices can be monitored through the PDU management interface. The entire correlator system uses a total of 3 PDUs. The six ROACH2 boards and the master computer are connected to one PDU. The first 7 GPU servers and the 10 GbE switch are connected to another PDU. The last 7 servers and the 1 GbE Ethernet switch are connected to the third PDU.

The total power of the F-engine is 220 V × 3.5 A = 770 W, including six ROACH2 boards and one master computer. The total power of X-engine is 220 V × 17.5 A = 3850 W, including seven GPU servers, one 10 GbE switch, and one 1 GbE switch. Therefore, the total power of the whole correlator system is 770 W + 3850 W = 4,620 W for 192 inputs. This is very energy-efficient for such a large-scale interferometer system.