Use your phone for better time management and task scheduling
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I've dorked around with it a bit (for task scheduling) and mostly come away frustrated and missing the open canvas of automation via shell scripting.
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Some tasks run in application-specific hardware units, however, and even task scheduling may not be sufficient to optimize all software-based tasks to meet timing and throughput constraints.
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Atom is a concurrent programming language intended for embedded applications. Atom features compile-time task scheduling and generates code with deterministic execution time and memory consumption, simplifying worst case execution time analysis for applications that require hard realtime performance. Atom's concurrency model is that of guarded atomic actions, which eliminates the need for, and the problems of using, mutex locks. By removing run-time task scheduling and mutex locking—two services traditionally served by an RTOS—Atom can eliminate the need and overhead of an RTOS in embedded applications.
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Swift trust is critical to virtual teams when there is limited or no time to build interpersonal relationships. Trust is based on an early assumption that the given team is trustworthy, but this assumption is verified through actions around the joint task, scheduling, and monitoring.
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Deadline Task Scheduling For further details on the CBS and how it enables temporal isolation, refer to the original CBS paper, or the section about the CBS in this article T. Cucinotta and F. Checconi, "The IRMOS realtime scheduler", section "The CBS: EDF-based Scheduling and Temporal Isolation" appeared on lwn.net.
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However, given that on the Blue Gene multiple compute nodes share a single I/O node, the I/O node operating system does require multi-tasking, hence the selection of the Linux-based operating system. While in traditional multi-user computer systems and early supercomputers, job scheduling was in effect a task scheduling problem for processing and peripheral resources, in a massively parallel system, the job management system needs to manage the allocation of both computational and communication resources. It is essential to tune task scheduling, and the operating system, in different configurations of a supercomputer. A typical parallel job scheduler has a master scheduler which instructs some number of slave schedulers to launch, monitor, and control parallel jobs, and periodically receives reports from them about the status of job progress.
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MultiLisp is a functional programming language, a dialect of the language Lisp, and of its dialect Scheme, extended with constructs for parallel computing execution and shared memory. These extensions involve side effects, rendering MultiLisp nondeterministic. Along with its parallel-programming extensions, MultiLisp also had some unusual garbage collection and task scheduling algorithms. Like Scheme, MultiLisp was optimized for symbolic computing.
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Applications must request access to those resources via APIs like fork(), malloc() and write(). The RTOS is a monolithic collection of libraries that manages task scheduling, memory partitioning and device I/O. This large block of code needs to be safety certified and bug free to be secure. A separation kernel relies on hardware virtualization functionality to do the heavy lifting.
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SimEvents provides a graphical drag-and-drop interface for building a discrete-event model. It provides libraries of entity generators, random number generators, queues, servers, graphical displays and statistics reporting blocks. Integration with MATLAB allows customization of the process flow in a SimEvents model. A MATLAB function can be developed to represent a task- scheduling sequence, routing of parts, or production recipes in a process flow.
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Some compiled or interpreted languages provide an interface that allows application code to interact directly with the runtime system. An example is the `Thread` class in the Java language. The class allows code (that is animated by one thread) to do things such as start and stop other threads. Normally, core aspects of a language's behavior such as task scheduling and resource management are not accessible in this fashion.
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GNU is an operating system. In its original meaning, and one still common in hardware engineering, the operating system is a basic set of functions to control the hardware and manage things like task scheduling and system calls. In modern terminology used by software developers, the collection of these functions is usually referred to as a kernel. The GNU project does develop and include such kernels and, therefore, GNU is a proper operating system.
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A single-tasking system can only run one program at a time, while a multi-tasking operating system allows more than one program to be running in concurrency. This is achieved by time-sharing, where the available processor time is divided between multiple processes. These processes are each interrupted repeatedly in time slices by a task- scheduling subsystem of the operating system. Multi-tasking may be characterized in preemptive and co-operative types.
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The OS then may decide to operate any number of operations including task scheduling. Once the OS is done, the pipeline proceeds as planned by propagating interrupts down the pipeline. When an OS in a domain does not want to be interrupted, for any reason, it asks Adeos to stall the stage its domain occupies in the interrupt pipeline. By doing so, interrupts go no further in the pipeline and are stalled at the stage occupied by the domain.
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Task scheduling is an important activity in any computer system with multiple processes or threads sharing a single processor core. It is important to reduce and increase for embedded software running on an SoC's . Not every important computing activity in a SoC is performed in software running on on-chip processors, but scheduling can drastically improve performance of software-based tasks and other tasks involving shared resources. SoCs often schedule tasks according to network scheduling and randomized scheduling algorithms.
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In 2013, the Multicore Task Management API (MTAPI) working group released its first specification. MTAPI is a standard specification for an application program interface (API) that supports the coordination of tasks on embedded parallel systems with homogeneous and heterogeneous cores. Core features of MTAPI are runtime scheduling and mapping of tasks to processor cores. Due to its dynamic behavior, MTAPI is intended for optimizing throughput on multicore-systems, allowing the software developer to improve the task scheduling strategy for latency and fairness.
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SoCs are optimized to minimize latency for some or all of their functions. This can be accomplished by laying out elements with proper proximity and locality to each-other to minimize the interconnection delays and maximize the speed at which data is communicated between modules, functional units and memories. In general, optimizing to minimize latency is an NP-complete problem equivalent to the boolean satisfiability problem. For tasks running on processor cores, latency and throughput can be improved with task scheduling.
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They also determine whether it is worthwhile to replicate data and computation. ; Mapping : In the fourth and final stage of the design of parallel algorithms, the developers specify where each task is to execute. This mapping problem does not arise on uniprocessors or on shared-memory computers that provide automatic task scheduling. On the other hand, on the server side, multi-core processors are ideal because they allow many users to connect to a site simultaneously and have independent threads of execution.
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In mathematics, a graph partition is the reduction of a graph to a smaller graph by partitioning its set of nodes into mutually exclusive groups. Edges of the original graph that cross between the groups will produce edges in the partitioned graph. If the number of resulting edges is small compared to the original graph, then the partitioned graph may be better suited for analysis and problem-solving than the original. Finding a partition that simplifies graph analysis is a hard problem, but one that has applications to scientific computing, VLSI circuit design, and task scheduling in multiprocessor computers, among others.
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Much work on WCET analysis is on reducing the pessimism in analysis so that the estimated value is low enough to be valuable to the system designer. WCET analysis usually refers to the execution time of single thread, task or process. However, on modern hardware, especially multi-core, other tasks in the system will impact the WCET of a given task if they share cache, memory lines and other hardware features. Further, task scheduling events such as blocking or to be interruptions should be considered in WCET analysis if they can occur in a particular system.
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A/ROSE itself is very small, the kernel using only 6 KB, and the operating system as a whole about 28 KB. A/ROSE supports pre-emptive multitasking with round-robin task scheduling with a 110 microsecond context switch time and only 20 microseconds of latency (guaranteed interrupt response time). The system's task is primarily to move data around and start and stop tasks on the cards, and the entire API contains only ten calls. A/ROSE is a message passing system, and the main calls made by programs running under it are `Send()` and `Receive()`. Messages are short, including only 24 bytes of user data, and sent asynchronously.
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The kernel in Linux handles all operating system processes, such as memory management, task scheduling, I/O, interprocess communication, and overall system control. This is loaded in two stages - in the first stage, the kernel (as a compressed image file) is loaded into memory and decompressed, and a few fundamental functions such as basic memory management are set up. Control is then switched one final time to the main kernel start process. Once the kernel is fully operational - and as part of its startup, upon being loaded and executing - the kernel looks for an init process to run, which (separately) sets up a user space and the processes needed for a user environment and ultimate login.
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For each task, in addition to the configured runtime and (relative) period, the kernel keeps track of a current runtime and a current (absolute) deadline. Tasks are scheduled on CPUs based on their current deadlines, using global EDF. When a task scheduling policy is initially set to `SCHED_DEADLINE`, the current deadline is initialized to the current time plus the configured period, and the current budget is set equal to the configured budget. Each time a task is scheduled to run on any CPU, the kernel lets it run for at most the available current budget, and whenever the task is descheduled its current budget is decreased by the amount of time it has been run.
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With Stackless Python, a running program is split into microthreads that are managed by the language interpreter itself, not the operating system kernel—context switching and task scheduling is done purely in the interpreter (these are thus also regarded as a form of green thread). Microthreads manage the execution of different subtasks in a program on the same CPU core. Thus, they are an alternative to event-based asynchronous programming and also avoid the overhead of using separate threads for single-core programs (because no mode switching between user mode and kernel mode needs to be done, so CPU usage can be reduced). Although microthreads make it easier to deal with running subtasks on a single core, Stackless Python does not remove Python's Global Interpreter Lock, nor does it use multiple threads and/or processes.
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Infiltration of Scapa Flow by U-47 Kriegsmarine Commander of Submarines () Karl Dönitz devised a plan to attack Scapa Flow by submarine within days of the outbreak of war. Its goal would be twofold: first, displacing the Home Fleet from Scapa Flow would slacken the British North Sea blockade and grant Germany greater freedom to attack the Atlantic convoys; second, the blow would be a symbolic act of vengeance, striking at the same location where the German High Seas Fleet had scuttled itself following Germany's defeat in the First World War. Dönitz hand-picked Kapitänleutnant Günther Prien for the task, scheduling the raid for the night of 13/14 October 1939, when the tides would be high and the night moonless. Dönitz was aided by high-quality photographs from a reconnaissance overflight by Siegfried Knemeyer (who received his first Iron Cross for the mission), which revealed the weaknesses of the defences and an abundance of targets.
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This results in the important property that, on single-processor systems, or on partitioned multi- processor systems (where tasks are partitioned among available CPUs, so each task is pinned down on a specific CPU and cannot migrate), all accepted `SCHED_DEADLINE` tasks are guaranteed to be scheduled for an overall time equal to their budget in every time window as long as their period, unless the task itself blocks and doesn't need to run. Also, a peculiar property of the CBS algorithm is that it guarantees temporal isolation also in presence of tasks blocking and resuming execution: this is done by resetting a task scheduling deadline to a whole period apart, whenever a task wakes up too late. In the general case of tasks free to migrate on a multi-processor, as `SCHED_DEADLINE` implements global EDF, the general tardiness bound for global EDF applies, as explained in. In order to better understand how the scheduler works, consider a set of `SCHED_DEADLINE` tasks with potentially different periods, but having deadline equal to the period.
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The general principle of grid computing is to use distributed computing resources from diverse administrative domains to solve a single task, by using resources as they become available. Traditionally, most grid systems have approached the task scheduling challenge by using an "opportunistic match-making" approach in which tasks are matched to whatever resources may be available at a given time.Grid computing: experiment management, tool integration, and scientific workflows by Radu Prodan, Thomas Fahringer 2007 pages 1-4 Example architecture of a geographically disperse distributively owned distributed computing system connecting many personal computers over a network BOINC, developed at the University of California, Berkeley is an example of a volunteer-based, opportunistic grid computing system.Parallel and Distributed Computational Intelligence by Francisco Fernández de Vega 2010 pages 65-68 The applications based on the BOINC grid have reached multi-petaflop levels by using close to half a million computers connected on the internet, whenever volunteer resources become available.BOIN statistics, 2011 Another system, Folding@home, which is not based on BOINC, computes protein folding, has reached 8.8 petaflops by using clients that include GPU and PlayStation 3 systems.
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