Decades long monitoring of millisecond pulsars, which exhibit highly stable rotational periods in pulsar timing array experiments is on the threshold of discovering nanohertz stochastic gravitational wave background. This paper describes the Indian pulsar timing array (InPTA) experiment, which employs the upgraded Giant Metrewave Radio Telescope (uGMRT) for timing an ensemble of millisecond pulsars for thispurpose. We highlight InPTA’s observation strategies and analysis methods, which are relevant for a future PTA experiment with the more sensitive Square Kilometer Array (SKA) telescope. We show that the unique multi-sub-array multi-band wide-bandwidth frequency coverage of the InPTA, provides dispersion measureestimates with unprecedented precision for PTA pulsars, e.g., $\sim$$2 \times 10^{−5}$ pc cm$^{−3}$ for PSR J1909-3744. Configuring the SKA-low and SKA-mid as two and four sub-arrays, respectively, it is shown that comparable precision is achievable, using observation strategies similar to those pursued by the InPTA, for a larger sample of 62 pulsars, requiring about 26 and 7 h per epoch for the SKA-mid and the SKA-low telescopes, respectively. We also review the ongoing efforts to develop PTA-relevant general relativistic constructs that will be required to search for nanohertz gravitational waves from isolated super-massive black hole binary systems like blazar OJ 287. These efforts should be relevant to pursue persistent multi-messenger gravitational wave astronomy during the forthcoming era of the SKA telescope, the thirty meter telescope, and the next-generation eventhorizon telescope.

This paper presents high-performance computing efforts with FPGA for the accelerated pulsar/transient search for the square kilometre array (SKA). Case studies are presented from within SKA and pathfinder telescopes highlighting future opportunities. It reviews the scenario that has shifted from offline processing of the radio telescope data to digitizing several hundreds/thousands of antenna outputs over huge bandwidths, forming several hundreds of beams, and processing the data in the SKA real-time pulsar search pipelines. A briefaccount of the different architectures of the accelerators, primarily, the new generation field programmable gate array-based accelerators, showing their critical roles to achieve high-performance computing and in handlingthe enormous data volume problems of the SKA is presented here. It also presents power-performance efficiency of this emerging technology and presents potential future scenarios.

The SKA pulsar search pipeline will be used for real time detection of pulsars. Modern radio telescopes, such as SKA will be generating petabytes of data in their full scale of operation. Hence, experience based and data-driven algorithms are being investigated for applications, such as candidate detection. Here, we describe our findings from testing a state of the art object detection algorithm called Mask R-CNN to detect candidate signatures in the SKA pulsar search pipeline. We have trained the Mask R-CNN model to detect candidate images. A custom semi-auto annotation tool was developed and investigated to rapidly mark the regions of interest in large datasets. We have used a simulation dataset to train and build the candidate detection algorithm. A more detailed analysis is planned. This paper presents details of this initial investigationhighlighting the future prospects.