Heimdall++ Optimizes GPU Utilization, Achieving 2.66× Faster Single-Pulse Detection for Radio Astronomy

Modern radio astronomy generates enormous datasets, demanding increasingly efficient methods for real-time analysis, particularly for detecting brief, transient signals. Bingzheng Xia from the Hangzhou Institute for Advanced Study, Zujie Ren from Zhejiang Lab, and Kuang Ma, along with their colleagues, have developed Heimdall++, a significant advancement in single-pulse detection technology. Building upon the existing Heimdall software, this new system overcomes limitations in processing speed by optimising how graphics processing units (GPUs) are used and by enabling more parallel processing. The team achieves substantial performance gains, demonstrating up to a 2. 66-fold increase in speed when analysing single observational files and a 2. 05-fold improvement in batch processing, all while ensuring the accuracy of the original Heimdall’s results, representing a crucial step towards handling the ever-growing flood of radio astronomical data.

Recognizing the increasing data volumes from modern radio telescopes, the team focused on maximizing processing speed and efficiency, beginning with a detailed analysis of the original Heimdall pipeline to identify sequential processing and data transfer as key areas for improvement. To address these limitations, scientists implemented a fine-grained parallelization strategy, breaking down computationally intensive tasks into independent units distributed across multiple CPU threads and GPU processing streams, enabling concurrent execution and maximizing hardware utilization. This approach dynamically adjusts to the capabilities of the target hardware, and minimizes data movement overhead by leveraging a unified memory system that automatically manages data residency between the computer’s processor and graphics card, eliminating explicit data copies and reducing latency.

Experimental results demonstrate that Heimdall++ achieves up to a 2. 66-fold increase in processing speed for single files and a 2. 05-fold improvement in batch processing scenarios, with significantly higher GPU utilization, enabling more efficient processing of large-scale radio astronomy data and paving the way for comprehensive, real-time surveys with next-generation radio telescopes.

Parallel Data Processing for Radio Astronomy

Scientists developed Heimdall++, a significantly enhanced data processing pipeline for detecting fast radio bursts and irregular pulsars, achieving substantial performance gains over its predecessor, Heimdall. Recognizing the increasing data volumes from modern radio telescopes, the team focused on maximizing GPU utilization and overall processing speed, beginning with a detailed analysis of Heimdall to identify sequential processing loops and data transfer bottlenecks. To overcome these limitations, scientists implemented a fine-grained parallelization strategy, decomposing the computationally intensive process of analyzing different dispersion measures into independent tasks distributed across multiple CPU threads and GPU processing streams, enabling concurrent kernel execution and maximizing hardware utilization. This approach dynamically adjusts to the capabilities of the target hardware, and minimizes data movement overhead by leveraging a unified memory system that automatically manages data residency between the computer’s processor and graphics card, eliminating the need for explicit data copies and reducing latency.

The team also optimized memory access patterns within computationally intensive stages by refactoring the code to exploit GPU shared memory and coalesced access patterns, significantly reducing traffic to global memory and improving throughput. For multi-file processing, scientists designed a multi-threaded, pipelined execution framework that decouples CPU-bound pipeline creation from GPU-accelerated processing, utilizing thread-safe task queues to connect the two stages, enabling overlapping CPU and GPU activities and masking CPU-side latency. Experimental results demonstrate that Heimdall++ achieves up to a 2. 66-fold increase in processing speed for single files and a 2. 05-fold improvement in multi-file batch processing, while maintaining complete consistency with the original Heimdall’s search results.

Heimdall++ Delivers Substantial Radio Transient Processing Speedup

Scientists have developed Heimdall++, a significantly enhanced data processing tool for detecting fast radio bursts and irregular pulsars, achieving substantial performance gains over its predecessor, Heimdall. Addressing limitations in the original software, the team developed a system that achieves substantial improvements in processing speed and efficiency through refined parallelization and memory management. Experiments demonstrate that Heimdall++ achieves up to a 2. 66-fold increase in processing single, large-scale observational files, and a 2. 05-fold speedup when processing multiple files simultaneously, while maintaining complete consistency with the original Heimdall’s search results.

The breakthrough stems from a redesigned data processing pipeline, incorporating fine-grained parallelization and optimized memory management. Researchers decomposed the computationally intensive process of analyzing different dispersion measures into independent tasks, distributing them across multiple CPU threads and GPU processing streams for concurrent execution, maximizing the utilization of the GPU’s processing cores and increasing the degree of parallelism. To minimize delays caused by data transfer between the computer’s processor and graphics card, Heimdall++ leverages a unified memory system, automatically managing data residency and eliminating the need for explicit data copies. These combined optimizations enable Heimdall++ to sustain higher concurrency and scalability, particularly in large-scale survey workloads involving numerous files.

Heimdall++ Delivers Realtime Pulse Detection Speedup

Scientists have developed Heimdall++, a significantly enhanced version of the Heimdall software pipeline, designed for real-time detection of single radio pulses in astronomy. Addressing limitations in the original software, the team developed a system that achieves substantial improvements in processing speed and efficiency through refined parallelization and memory management. Evaluations using observational data demonstrate that Heimdall++ delivers up to a 2. 66-fold increase in processing speed for single files and a 2. 05-fold improvement in batch processing scenarios, while maintaining complete consistency with the results obtained from the original Heimdall.

The advancements stem from a redesigned architecture incorporating fine-grained parallelization across multiple processing streams and a unified memory management system, which minimizes data transfer overhead between the computer’s processor and graphics card. Furthermore, a multi-threaded framework decouples computationally intensive tasks, mitigating bottlenecks and improving overall throughput. These improvements are crucial for handling the increasing volume of data generated by modern radio telescopes and will reduce computational costs for observatories, laying a strong foundation for future high-throughput surveys with next-generation instruments, enabling astronomers to detect and analyse faint and transient radio signals with greater efficiency.