A Framework for Management of Distributed Data Processing and Event Selection for the Icecube Neutrino Observatory
Bibliographic record
Abstract
IceCube is a one-gigaton neutrino detector designed to detect high-energy cosmic neutrinos. It is located at the geographic South Pole and was completed at the end of 2010. Simulation and data processing for IceCube requires a significant amount of computational power. We describe the design and functionality of IceProd, a management system based on Python, XMLRPC, and GridFTP. It is driven by a central database in order to coordinate and administer production of simulations and processing of data produced by the IceCube detector upon arrival in the northern hemisphere. IceProd runs as a separate layer on top of existing middleware and can take advantage of a variety of computing resources including grids and batch systems such as GLite, Condor, NorduGrid, PBS, and SGE. This is accomplished by a set of dedicated daemons that process job submission in a coordinated fashion through the use of middleware plug-ins that serve to abstract the details of job submission and job management. IceProd fills a gap between the user and existing middleware by making job scripting easier and collaboratively sharing productions more efficiently. We describe the implementation and performance of an extension to the IceProd framework that provides support for mapping workflow diagrams or DAGs consisting of interdependent tasks to an IceProd job that can span across multiple grid or cluster sites. We look at some use-cases where this new extension allows for optimal allocation of computing resources and addresses general aspects of this design, including security, data integrity, scalability, and throughput.
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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 | 0.012 | 0.010 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.010 | 0.009 |
| Research integrity | 0.003 | 0.007 |
| Insufficient payload (model declined to judge) | 0.011 | 0.008 |
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".