An Efficient Real-time Data Pipeline for the CHIME Pathfinder Radio Telescope X-Engine
Bibliographic record
Abstract
The CHIME Pathfinder is a new interferometric radio telescope that uses a hybrid FPGA/GPU FX correlator. The GPU-based X-engine of this correlator processes over 819 Gb/s of 4+4-bit complex astronomical data from N=256 inputs across a 400 MHz radio band. A software framework is presented to manage this real-time data flow, which allows each of 16 processing servers to handle 51.2 Gb/s of astronomical data, plus 8 Gb/s of ancillary data. Each server receives data in the form of UDP packets from an FPGA F-engine over the eight 10 GbE links, combines data from these packets into large (32MB-256MB) buffered frames, and transfers them to multiple GPU co-processors for correlation. The results from the GPUs are combined and normalized, then transmitted to a collection server, where they are merged into a single file. Aggressive optimizations enable each server to handle this high rate of data; allowing the efficient correlation of 25 MHz of radio bandwidth per server. The solution scales well to larger values of N by adding additional servers.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.006 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".