Degree of parallelism

About: Degree of parallelism is a research topic. Over the lifetime, 1515 publications have been published within this topic receiving 25546 citations.

Optimization techniques for skeletons on grids.

Abstract: Skeletons are common patterns of parallelism, such as farm and pipeline, that can be abstracted and offered to the application programmer as programming primitives. We describe the use and implementation of skeletons on emerging computational grids, with the skeleton system Lithium, based on Java and RMI, as our reference programming syttem. Our main contribution is the exploration of optimization techniques for implementing skeletons on grids based on an optimized, future-based RMI mechanism, which we integrate into the macro-dataflow evaluation mechanism of Lithium. We discuss three optimizations: 1) a lookahead mechanism that allows to process multiple tasks concurrently at each grid server and thereby increases the overall degree of parallelism, 2) a lazy taskbinding technique that reduces interactions between grid servers and the task dispatcher, and 3) dynamic improvements that optimize the collecting of results and the work-load balancing. We report experimental results that demonstrate the improvements due to our optimizations on various testbeds, including a heterogeneous grid-like environment.

The generalized multiprocessor periodic resource interface model for hierarchical multiprocessor scheduling

Abstract: Composition is a practice of key importance in software engineering. When real-time applications are composed it is necessary that their timing properties (such as meeting the deadlines) are guaranteed. The composition is performed by establishing an interface between the application and the physical platform. Such an interface does typically contain information about the amount of computing capacity needed by the application. In multiprocessor platforms, the interface should also present information about the degree of parallelism. Recently there have been quite a few interface proposals. However, they are either too complex to be handled or too pessimistic.In this paper we propose the Generalized Multiprocessor Periodic Resource model (GMPR) that is strictly superior to the MPR model without requiring a too detailed description. We describe a method to generate the interface from the application specification. All these methods have been implemented in Matlab routines that are publicly available.

Capturing parallel attacks within the data independence framework

Abstract: We carry forward the work described in our previous papers (Broadfoot et al., 2000, Broadfoot and Roscoe, 2002, and Roscoe, 1998) on the application of data independence to the model checking of cryptographic protocols using CSP and FDR. In particular, we showed how techniques based on data independence could be used to justify, by means of a finite FDR check, systems where agents can perform an unbounded number of protocol runs. Whilst this allows for a more complete analysis, there was one significant incompleteness in the results we obtained: While each individual identity could perform an unlimited number of protocol runs sequentially, the degree of parallelism remained bounded. We report significant progress towards the solution of this problem, by "internalising" all or part of each agent identity within the "intruder" process. We consider the case where internal agents do introduce fresh values and address the issue of capturing the state of mind of internal agents (for the purposes of analysis).

Asynchronous Episodic Deep Deterministic Policy Gradient: Towards Continuous Control in Computationally Complex Environments

Abstract: Deep Deterministic Policy Gradient (DDPG) has been proved to be a successful reinforcement learning (RL) algorithm for continuous control tasks. However, DDPG still suffers from data insufficiency and training inefficiency, especially in computationally complex environments. In this paper, we propose Asynchronous Episodic DDPG (AE-DDPG), as an expansion of DDPG, which can achieve more effective learning with less training time required. First, we design a modified scheme for data collection in an asynchronous fashion. Generally, for asynchronous RL algorithms, sample efficiency or/and training stability diminish as the degree of parallelism increases. We consider this problem from the perspectives of both data generation and data utilization. In detail, we re-design experience replay by introducing the idea of episodic control so that the agent can latch on good trajectories rapidly. In addition, we also inject a new type of noise in action space to enrich the exploration behaviors. Experiments demonstrate that our AE-DDPG achieves higher rewards and requires less time consuming than most popular RL algorithms in Learning to Run task which has a computationally complex environment. Not limited to the control tasks in computationally complex environments, AE-DDPG also achieves higher rewards and 2- to 4-fold improvement in sample efficiency on average compared to other variants of DDPG in MuJoCo environments. Furthermore, we verify the effectiveness of each proposed technique component through abundant ablation study.