FLEET : a Framework for evaLuating European options in a parallEl and disTributed environment
Bibliographic record
Abstract
Option pricing is one of the important problem in finance and demands efficient algo- rithms that produce accurate and fast results.This thesis aims to develop FLEETI: A Framework for evaluating European options in a parallEl and disTributed environment on a Network of Computers (NoCs).In this thesis, the NoC is an interconnection of a collection of heterogeneous computers and an eight node shared memory machine.We have implemented a multithreaded pricing algorithm on shared memory architecture us- ing Java OpenMP (JOMP IBWKOOO, EPC]).FLEET uses the Common Object Request Broker Architecture (CORBA [SGR99, Bol02]) as a client-server model where a client requests for the value of an option (with certain characteristics of the option) over the net- work to a server.The server computes the option value using the Black-Scholes [8573] model, a partial differential equation.The server is multithreaded and uses one thread- per-client policy to serve clients over the network.We use the explicit Forward-Time Central-Space (FTCS) finite-difference scheme to solve the Black-Scholes equation on the server side to evaluate the option price.'We implemented a database containing the 'FLEET."f@(processorsinmycase)workingtogetherunderonecommand'
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".