Dynamic peer-to-peer construction of clusters
Bibliographic record
Abstract
The use of parallel computing is increasing with the need to solve ever more complex problems. Unfortunately, while the cost of parallel systems (including clusters and small-scale shared memory machines) has decreased, such machines are still not within the reach of many users. This is particularly true if large numbers of processors are needed. A largely untapped resource for doing some, simpler, types of parallel computing \nare temporarily idle machines in distributed environments. Such environments range from the simple (identical machines connected via a LAN) to the complex (heterogeneous machines connected via the Internet). \nIn this thesis I describe a system for dynamically clustering together similar machines distributed across the Internet. This is done in a peer-to-peer (P2P) fashion with the goal of ultimately forming useful compute clusters without the need for a heavily centralized software system overseeing the process. In this sense my work builds on so-called \n"volunteer computing" efforts, such as SETI@Home but with the goal of supporting a #11;different class of compute problems. \nI #12;first consider the characteristics that are necessary to form good clusters of shared machines that can be used together effectively. Second, I exploit simple clustering algorithms to group together appropriate machines using the identified#12;ed characteristics. My system assembles workstations into clusters which are, in some sense, "close" in terms \nof bandwidth, latency and/or number of network hops and that are also computationally similar in terms of processor speed, memory capacity and available hard disk space. Finally, I assess the conditions under which my proposed system might be effective via simulation using generated network topologies that are intended to reflect real-world characteristics. The results of these simulations suggest that my system is tunable to different conditions and that the algorithms presented can #11;effectively group together appropriate machines to form clusters and can also manage those clusters #11;effectively as the constituent machines join and leave the system.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".