Task (computing)

About: Task (computing) is a research topic. Over the lifetime, 9718 publications have been published within this topic receiving 129364 citations.

Tightening the bounds on feasible preemptions

Abstract: Data caches are an increasingly important architectural feature in most modern computer systems. They help bridge the gap between processor speeds and memory access times. One inherent difficulty of using data caches in a real-time system is the unpredictability of memory accesses, which makes it difficult to calculate worst-case execution times (WCETs) of real-time tasks.While cache analysis for single real-time tasks has been the focus of much research in the past, bounding the preemption delay in a multitask preemptive environment is a challenging problem, particularly for data caches.This article makes multiple contributions in the context of independent, periodic tasks with deadlines less than or equal to their periods executing on a single processor.1) For every task, we derive data cache reference patterns for all scalar and nonscalar references. These patterns are used to derive an upper bound on the WCET of real-time tasks.2) We show that, when considering cache preemption effects, the critical instant does not occur upon simultaneous release of all tasks. We provide results for task sets with phase differences to prove our claim.3) We develop a method to calculate tight upper bounds on the maximum number of possible preemptions for each job of a task and, considering the worst-case placement of these preemption points, derive a much tighter bound on its WCET. We provide results using both static-and dynamic-priority schemes.Our results show significant improvements in the bounds derived. We achieve up to an order of magnitude improvement over two prior methods and up to half an order of magnitude over a third prior method for the number of preemptions, the WCET and the response time of a task. Consideration of the best-case and worst-case execution times of higher-priority jobs enables these improvements.

Highly distributed parallel processing on multi-core device

Abstract: There is provided a highly distributed multi-core system with an adaptive scheduler. By resolving data dependencies in a given list of parallel tasks and selecting a subset of tasks to execute based on provided software priorities, applications can be executed in a highly distributed manner across several types of slave processing cores. Moreover, by overriding provided priorities as necessary to adapt to hardware or other system requirements, the task scheduler may provide for low-level hardware optimizations that enable the timely completion of time-sensitive workloads, which may be of particular interest for real-time applications. Through this modularization of software development and hardware optimization, the conventional demand on application programmers to micromanage multi-core processing for optimal performance is thus avoided, thereby streamlining development and providing a higher quality end product.

Containerized Computational Task Execution Management Using a Secure Distributed Transaction Ledger

Abstract: A peer-to-peer network includes a cryptocurrency system with a block chain, a client system coupled to the cryptocurrency system, and a host system coupled to the cryptocurrency system The client system publishes a container execution request that includes information associating the client system with a containerized computational task, the cryptocurrency system incorporates the container execution request into a block chain of a cryptocurrency system, and the host system receives the container execution request via the block chain, retrieves the containerized computational task, and executes the containerized computational task

Assignment of Dynamically Perceived Tasks by Token Passing in Multirobot Systems

Abstract: The problem of assigning tasks to a group of robots acting in a dynamic environment is a fundamental issue for a multirobot system (MRS) and several techniques have been studied to address this problem. Such techniques usually rely on the assumption that tasks to be assigned are inserted into the system in a coherent fashion. In this work we consider a scenario where tasks to be accomplished are perceived by the robots during mission execution. This issue has a significative impact on the task allocation process and, at the same time, makes it strictly dependent on perception capabilities of robots. More specifically, we present an asynchronous distributed mechanism based on Token Passing for allocating tasks in a team of robots. We tested and evaluated our approach by means of experiments both in a simulated environment and with real robots; our scenario comprises a set of robots that must cooperatively collect a set of objects scattered in the working environment. Each object collection task requires the cooperation of two robots. The experiments in the simulation environment allowed us to extract quantitative data from several missions and in different operative conditions and to characterize in a statistical way the results of our approach, especially when the team size increases

“Good Robot!”: Efficient Reinforcement Learning for Multi-Step Visual Tasks with Sim to Real Transfer

Abstract: Current Reinforcement Learning (RL) algorithms struggle with long-horizon tasks where time can be wasted exploring dead ends and task progress may be easily reversed. We develop the SPOT framework, which explores within action safety zones, learns about unsafe regions without exploring them, and prioritizes experiences that reverse earlier progress to learn with remarkable efficiency. The SPOT framework successfully completes simulated trials of a variety of tasks, improving a baseline trial success rate from 13% to 100% when stacking 4 cubes, from 13% to 99% when creating rows of 4 cubes, and from 84% to 95% when clearing toys arranged in adversarial patterns. Efficiency with respect to actions per trial typically improves by 30% or more, while training takes just 1-20 k actions, depending on the task. Furthermore, we demonstrate direct sim to real transfer. We are able to create real stacks in 100% of trials with 61% efficiency and real rows in 100% of trials with 59% efficiency by directly loading the simulation-trained model on the real robot with no additional real-world fine-tuning. To our knowledge, this is the first instance of reinforcement learning with successful sim to real transfer applied to long term multi-step tasks such as block-stacking and row-making with consideration of progress reversal. Code is available at https://github.com/jhu-lcsr/good_robot.