Software method level speculation for java
Notice bibliographique
Résumé
Speculative multithreading (SpMT), also known as thread level speculation (TLS), is a dynamic parallelization technique that relies on out-of-order execution, dependence buffering, and misspeculation rollback to achieve speedup of sequential programs on multiprocessors. A large number of hardware studies have shown good results for irregular programs, as have a smaller number of software studies in the context of loop level speculation for unmanaged languages. In this thesis we explore software method level speculation for Java. A software environment means that speculation will run on existing multiprocessors, at the cost of extra overhead. Method level speculation (MLS) is a kind of SpMT / TLS that creates threads on method invocation, executing the continuation speculatively. Although MLS can subsume loop level speculation, it is still a relatively unexplored paradigm. The Java programming language and virtual machine environment are rich and complex, posing many implementation challenges, but also supporting a compelling variety of object-oriented programs. We first describe the design and implementation of a prototype system in a Java bytecode interpreter. This includes support for various MLS components, such as return value prediction and dependence buffering, as well as various interactions with features of the Java virtual machine, for example bytecode interpretation, exception handling, and the Java memory model. Experimentally we found that although high thread overheads preclude speedup, we could extract significant parallelism if overheads were excluded. Furthermore, profiling revealed three key areas for optimization. The first key area for optimization was the return value prediction system. In our initial model, a variety of predictors were all executing naively on every method invocation, in order that a hybrid predictor might select the best performing ones. We developed an adaptive system wherein hybrid predictors dynamically specialize on a per-callsite basis, thus dramatically reducing speed and memory costs whilst maintaining high accuracy. The second area for optimization was the nesting model. Our initial system only allowed for out-of-order nesting, wherein a single parent thread creates multiple child threads. Enabling support for in-order nesting exposes significantly more parallelization opportunities, because now speculative child threads can create their own children that are even more speculative. This required developing a memory manager for child threads based on recycling aggregate data structures. We present an operational semantics for our nesting model derived from our implementation. Finally, we use this semantics to address the third area for optimization, namely a need for better fork heuristics. Initial heuristics based on online profiling made it difficult to identify the best places to create threads due to complex feedback interactions between speculation decisions at independent speculation points. This problem grew exponentially worse with the support for in-order nesting. Instead, we chose to clarify the effect of program structure on runtime parallelism. We did this by systematically exploring the interaction between speculation and a variety of coding idioms. The patterns we identify are intended to guide both manual parallelization and static compilation efforts.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».