369 research outputs found
ILP-based approaches to partitioning recurrent workloads upon heterogeneous multiprocessors
The problem of partitioning systems of independent constrained-deadline sporadic tasks upon heterogeneous multiprocessor platforms is considered. Several different integer linear program (ILP) formulations of this problem, offering different tradeoffs between effectiveness (as quantified by speedup bound) and running time efficiency, are presented
A C-DAG task model for scheduling complex real-time tasks on heterogeneous platforms: preemption matters
Recent commercial hardware platforms for embedded real-time systems feature
heterogeneous processing units and computing accelerators on the same
System-on-Chip. When designing complex real-time application for such
architectures, the designer needs to make a number of difficult choices: on
which processor should a certain task be implemented? Should a component be
implemented in parallel or sequentially? These choices may have a great impact
on feasibility, as the difference in the processor internal architectures
impact on the tasks' execution time and preemption cost. To help the designer
explore the wide space of design choices and tune the scheduling parameters, in
this paper we propose a novel real-time application model, called C-DAG,
specifically conceived for heterogeneous platforms. A C-DAG allows to specify
alternative implementations of the same component of an application for
different processing engines to be selected off-line, as well as conditional
branches to model if-then-else statements to be selected at run-time. We also
propose a schedulability analysis for the C-DAG model and a heuristic
allocation algorithm so that all deadlines are respected. Our analysis takes
into account the cost of preempting a task, which can be non-negligible on
certain processors. We demonstrate the effectiveness of our approach on a large
set of synthetic experiments by comparing with state of the art algorithms in
the literature
Provably good scheduling of sporadic tasks with resource sharing on a two-type heterogeneous multiprocessor platform
Consider the problem of scheduling a set of implicit-deadline sporadic tasks to meet all deadlines on a two-type
heterogeneous multiprocessor platform where a task may request at most one of |R| shared resources. There are m1
processors of type-1 and m2 processors of type-2. Tasks may migrate only when requesting or releasing resources. We
present a new algorithm, FF-3C-vpr, which offers a guarantee that if a task set is schedulable to meet deadlines by an
optimal task assignment scheme that only allows tasks to migrate when requesting or releasing a resource, then FF-3Cvpr
also meets deadlines if given processors 4+6*ceil(|R|/min(m1,m2)) times as fast. As far as we know, it is the first
result for resource sharing on heterogeneous platforms with provable performance
Real-time scheduling with resource sharing on heterogeneous multiprocessors
Consider the problem of scheduling a task set τ of implicit-deadline sporadic tasks to meet all deadlines on a t-type heterogeneous multiprocessor platform where tasks may access multiple shared resources. The multiprocessor platform has m k processors of type-k, where k∈{1,2,…,t}. The execution time of a task depends on the type of processor on which it executes. The set of shared resources is denoted by R. For each task τ i , there is a resource set R i ⊆R such that for each job of τ i , during one phase of its execution, the job requests to hold the resource set R i exclusively with the interpretation that (i) the job makes a single request to hold all the resources in the resource set R i and (ii) at all times, when a job of τ i holds R i , no other job holds any resource in R i . Each job of task τ i may request the resource set R i at most once during its execution. A job is allowed to migrate when it requests a resource set and when it releases the resource set but a job is not allowed to migrate at other times. Our goal is to design a scheduling algorithm for this problem and prove its performance.
We propose an algorithm, LP-EE-vpr, which offers the guarantee that if an implicit-deadline sporadic task set is schedulable on a t-type heterogeneous multiprocessor platform by an optimal scheduling algorithm that allows a job to migrate only when it requests or releases a resource set, then our algorithm also meets the deadlines with the same restriction on job migration, if given processors 4×(1+MAXP×⌈|P|×MAXPmin{m1,m2,…,mt}⌉) times as fast. (Here MAXP and |P| are computed based on the resource sets that tasks request.) For the special case that each task requests at most one resource, the bound of LP-EE-vpr collapses to 4×(1+⌈|R|min{m1,m2,…,mt}⌉). To the best of our knowledge, LP-EE-vpr is the first algorithm with proven performance guarantee for real-time scheduling of sporadic tasks with resource sharing on t-type heterogeneous multiprocessors
Predictability of Fixed-Job Priority Schedulers on Heterogeneous Multiprocessor Real-Time Systems
The multiprocessor Fixed-Job Priority (FJP) scheduling of real-time systems
is studied. An important property for the schedulability analysis, the
predictability (regardless to the execution times), is studied for
heterogeneous multiprocessor platforms. Our main contribution is to show that
any FJP schedulers are predictable on unrelated platforms. A convenient
consequence is the fact that any FJP schedulers are predictable on uniform
multiprocessors
Feasibility Analysis of Conditional DAG Tasks
Feasibility analysis for Conditional DAG tasks (C-DAGs) upon multiprocessor platforms is shown to be complete for the complexity class pspace. It is shown that as a consequence integer linear programming solvers (ILP solvers) are likely to prove inadequate for such analysis. A demarcation is identified between the feasibility-analysis problems on C-DAGs that are efficiently solvable using ILP solvers and those that are not, by characterizing a restricted class of C-DAGs for which feasibility analysis is shown to be efficiently solvable using ILP solvers
Integrating Job Parallelism in Real-Time Scheduling Theory
We investigate the global scheduling of sporadic, implicit deadline,
real-time task systems on multiprocessor platforms. We provide a task model
which integrates job parallelism. We prove that the time-complexity of the
feasibility problem of these systems is linear relatively to the number of
(sporadic) tasks for a fixed number of processors. We propose a scheduling
algorithm theoretically optimal (i.e., preemptions and migrations neglected).
Moreover, we provide an exact feasibility utilization bound. Lastly, we propose
a technique to limit the number of migrations and preemptions
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