Bibliographic record
Abstract
A basic problem with most current Java implementations is that they were designed to support very small applet-style programs. The ability to effectively execute very large, computationally complex application programs is not currently an area of strength for Java. However, Java is now being used as a general-purpose language for implementing all types of programming problems including computationally intense applications. The translation and execution techniques used by current Java implementations do not necessarily scale to support the performance requirements of such programs. Static compilation with aggressive optimization is an implementation technique that is commonly used for languages such as C, C++, and FORTRAN but has not been widely used to implement Java. This article identifies some of the reasons for Java's performance problems and examines how static compilation and optimization techniques can be applied to Java to alleviate these problems. WHY DOES JAVA HAVE POOR PERFORMANCE? Java's performance problems can be traced to two major sources: the inherent inefficiencies of object-oriented (OO) programming constructs and the use of a virtual machine (VM)-based implementation strategy. After explaining the details of these inefficiencies I show how static compilation technology is able to overcome them. OO LANGUAGE INEFFICIENCIES OO programming languages such as Java have been widely promoted as key tools for increasing programmer productivity. The productivity improvements of OO languages are generally achieved through code reuse. Using an OO language such as Java, a programmer needs to write less new code because OO languages support and encourage the reuse of existing code.
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 | 0.005 | 0.028 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.007 | 0.021 |
| Open science | 0.003 | 0.006 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.009 |
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".