A List Scheduling Heuristic with New Node Priorities and Critical Child Technique for Task Scheduling with Communication Contention

International audienceTask scheduling is an important aspect for parallel programming. In this paper, the program to be scheduled is modeled as a Directed Acyclic Graph (DAG), and we target parallel embedded systems of multiple processors connected by buses and switches. This paper presents improvements for list scheduling heuristics with communication contention. We use new node priorities (top level and bottom level) to sort nodes and use an advanced technique of critical child to select a processor to execute a node. Experimental results show that our method is effective to reduce the schedule length, and the performance is greatly improved in the cases of medium and high communication. Since the communication cost is increasing from medium to high in modern applications like digital communication and video compression, our method will work well for scheduling these applications on parallel embedded systems

Advanced list scheduling heuristic for task scheduling with communication contention for parallel embedded systems

WOSInternational audienceModern embedded systems tend to use multiple cores or processors for processing parallel applications. This paper indeed aims at task scheduling with communication contention for parallel embedded systems and proposes three advanced techniques to improve the list scheduling heuristic. Five groups of node levels (two existing groups and three new groups) are firstly used as node priorities to generate node lists. Then the critical child technique improves the selection of a processor in the scheduling process. Finally, the communication delay technique enlarges the idle time intervals on communication links. We also propose an advanced dynamic list scheduling heuristic by combining the three techniques. Experimental results show that the combined advanced dynamic heuristic is efficient to shorten the schedule length for most of the randomly generated DAGs in the cases of medium and high communication. Our method accelerates an application up to 80% in the case of high communication and can also reduce the use of hardware resources

A List Scheduling Heuristic with New Node Priorities and Critical Child Technique for Task Scheduling with Communication Contention

International audienceTask scheduling is an important aspect for parallel programming. In this paper, the program to be scheduled is modeled as a Directed Acyclic Graph (DAG), and we target parallel embedded systems of multiple processors connected by buses and switches. This paper presents improvements for list scheduling heuristics with communication contention. We use new node priorities (top level and bottom level) to sort nodes and use an advanced technique of critical child to select a processor to execute a node. Experimental results show that our method is effective to reduce the schedule length, and the performance is greatly improved in the cases of medium and high communication. Since the communication cost is increasing from medium to high in modern applications like digital communication and video compression, our method will work well for scheduling these applications on parallel embedded systems

Heuristique statique améliorée d'ordonnancement de tâches: impact sur le tri des tâches et sur l'allocation de processeur

National audienceL'ordonnancement de tâches est une étape importante dans le prototypage rapide d'applications de traitement d'images sur des systèmes parallèles embarqués. Nous présentons ainsi dans cet article une heuristique statique améliorée d'ordonnancement par liste : d'une part, cette heuristique intègre de nouvelles règles de priorité de tâches, tenant compte de la contention sur les communications entre tâches ; d'autre part, cette heuristique affine l'allocation d'un processeur à une tâche courante, en impactant le choix du processeur par un ordonnancement partiel de la tâche successeur critique (" critical child ") à la tâche courante. Nos résultats expérimentaux soulignent une accélération effective de l'application implantée, dans un contexte de moyenne comme de forte communication

Heuristique statique améliorée d'ordonnancement de tâches: impact sur le tri des tâches et sur l'allocation de processeur

National audienceL'ordonnancement de tâches est une étape importante dans le prototypage rapide d'applications de traitement d'images sur des systèmes parallèles embarqués. Nous présentons ainsi dans cet article une heuristique statique améliorée d'ordonnancement par liste : d'une part, cette heuristique intègre de nouvelles règles de priorité de tâches, tenant compte de la contention sur les communications entre tâches ; d'autre part, cette heuristique affine l'allocation d'un processeur à une tâche courante, en impactant le choix du processeur par un ordonnancement partiel de la tâche successeur critique (" critical child ") à la tâche courante. Nos résultats expérimentaux soulignent une accélération effective de l'application implantée, dans un contexte de moyenne comme de forte communication

Automatic channel selection and spatial feature integration for multi-channel speech recognition across various array topologies

Automatic Speech Recognition (ASR) has shown remarkable progress, yet it still faces challenges in real-world distant scenarios across various array topologies each with multiple recording devices. The focal point of the CHiME-7 Distant ASR task is to devise a unified system capable of generalizing various array topologies that have multiple recording devices and offering reliable recognition performance in real-world environments. Addressing this task, we introduce an ASR system that demonstrates exceptional performance across various array topologies. First of all, we propose two attention-based automatic channel selection modules to select the most advantageous subset of multi-channel signals from multiple recording devices for each utterance. Furthermore, we introduce inter-channel spatial features to augment the effectiveness of multi-frame cross-channel attention, aiding it in improving the capability of spatial information awareness. Finally, we propose a multi-layer convolution fusion module drawing inspiration from the U-Net architecture to integrate the multi-channel output into a single-channel output. Experimental results on the CHiME-7 corpus with oracle segmentation demonstrate that the improvements introduced in our proposed ASR system lead to a relative reduction of 40.1% in the Macro Diarization Attributed Word Error Rates (DA-WER) when compared to the baseline ASR system on the Eval sets.Comment: Accepted by ICASSP 202

Contextualized End-to-End Speech Recognition with Contextual Phrase Prediction Network

Contextual information plays a crucial role in speech recognition technologies and incorporating it into the end-to-end speech recognition models has drawn immense interest recently. However, previous deep bias methods lacked explicit supervision for bias tasks. In this study, we introduce a contextual phrase prediction network for an attention-based deep bias method. This network predicts context phrases in utterances using contextual embeddings and calculates bias loss to assist in the training of the contextualized model. Our method achieved a significant word error rate (WER) reduction across various end-to-end speech recognition models. Experiments on the LibriSpeech corpus show that our proposed model obtains a 12.1% relative WER improvement over the baseline model, and the WER of the context phrases decreases relatively by 40.5%. Moreover, by applying a context phrase filtering strategy, we also effectively eliminate the WER degradation when using a larger biasing list.Comment: Accepted by interspeech202