32 research outputs found
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
Calculating an upper bound on the finishing time of a group of threads executing on a GPU: a preliminary case study
Graphics processor units (GPUs) today can be
used for computations that go beyond graphics and such use
can attain a performance that is orders of magnitude greater
than a normal processor. The software executing on a graphics
processor is composed of a set of (often thousands of) threads
which operate on different parts of the data and thereby
jointly compute a result which is delivered to another thread
executing on the main processor. Hence the response time of
a thread executing on the main processor is dependent on the
finishing time of the execution of threads executing on the GPU.
Therefore, we present a simple method for calculating an upper
bound on the finishing time of threads executing on a GPU, in
particular NVIDIA Fermi. Developing such a method is nontrivial
because threads executing on a GPU share hardware
resources at very fine granularity
Real-Time Scheduling on Heterogeneous Multiprocessors
Embedded computing is one of the most important areas in computer science today, witnessed by the fact that 98 % of all computers are embedded. Given that many embedded systems have to interact “promptly” with their physical environment, the scientific community has invested signifi-cant efforts in developing algorithms for scheduling the workload, which is generally implemented as a set of tasks, at the right time and in proving before run-time that all the timing requirements will be satisfied at run-time. This field of study is referred to as the real-time scheduling theory. The scheduling theory for a unicore processor is well-developed; the scientific results are taught at all major universities world-wide and the results are widely-used in industry. Scheduling theory for multicores is emerging but the focus so far has been for multicores with identical pro-cessing units. This is unfortunate because the computer industry is moving towards heterogeneous multicores with a constant number of distinct processor types — AMD Fusion, Intel Atom and NVIDIA Tegra are some of the examples of such multicores. This work deals with the problem of scheduling a set of tasks to meet their deadlines on het-erogeneous multiprocessors with a constant number of distinct processor types. On heterogeneou
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
A PTAS for assigning sporadic tasks on two-type heterogeneous multiprocessors
Consider the problem of determining a task-toprocessor assignment for a given collection of implicit-deadline sporadic tasks upon a multiprocessor platform in which there are two distinct kinds of processors. We propose a polynomialtime approximation scheme (PTAS) for this problem. It offers the following guarantee: for a given task set and a given platform, if there exists a feasible task-to-processor assignment, then given an input parameter, ϵ, our PTAS succeeds, in polynomial time, in finding such a feasible task-to-processor assignment on a platform in which each processor is 1+3ϵ times faster. In the simulations, our PTAS outperforms the state-of-the-art PTAS [1] and also for the vast majority of task sets, it requires significantly smaller processor speedup than (its upper bound of) 1+3ϵ for successfully determining a feasible task-to-processor assignment
Provably good task assignment on heterogeneous multiprocessor platforms for a restricted case but with a stronger adversary
Consider the problem of scheduling a set of
implicit-deadline sporadic tasks to meet all deadlines on
a heterogeneous multiprocessor platform. We consider a
restricted case where the maximum utilization of any task on
any processor in the system is no greater than one. We use
an algorithm proposed in [1] (we refer to it as LP-EE) from
state-of-the-art for assigning tasks to heterogeneous multiprocessor
platform and (re-)prove its performance guarantee
for this restricted case but for a stronger adversary. We show
that if a task set can be scheduled to meet deadlines on a
heterogeneous multiprocessor platform by an optimal task
assignment scheme that allows task migrations then LP-EE
meets deadlines as well with no migrations if given processors
twice as fast
Two-type heterogeneous multiprocessor scheduling: Is there a phase transition? (Extended Abstract)
Consider the problem of non-migratively scheduling a set of implicit-deadline sporadic tasks to meet all deadlines on a
two-type heterogeneous multiprocessor platform. We ask the following question: Does there exist a phase transition
behavior for the two-type heterogeneous multiprocessor scheduling problem? We also provide some initial observations
via simulations performed on randomly generated task sets
Intra-type migrative scheduling of implicit-deadline sporadic tasks on two- type heterogeneous multiprocessor
Consider the problem of scheduling a set of implicit-deadline sporadic tasks to meet all deadlines on a two-type
heterogeneous multiprocessor platform. Each processor is either of type-1 or type-2 with each task having different
execution time on each processor type. Jobs can migrate between processors of same type (referred to as intra-type
migration) but cannot migrate between processors of different types. We present a new scheduling algorithm namely,
LP-Relax(THR) which offers a guarantee that if a task set can be scheduled to meet deadlines by an optimal task
assignment scheme that allows intra-type migration then LP-Relax(THR) meets deadlines as well with intra-type
migration if given processors 1/THR as fast (referred to as speed competitive ratio) where THR <= 2/3
A conjecture about provably good task assignment on heterogeneous multiprocessor platforms but with a stronger adversary
Consider the problem of scheduling a set of
implicit-deadline sporadic tasks to meet all deadlines on a
heterogeneous multiprocessor platform. We use an algorithm
proposed in [1] (we refer to it as LP-EE) from state-of-the-art
for assigning tasks to heterogeneous multiprocessor platform
and (re-)prove its performance guarantee but for a stronger
adversary.We conjecture that if a task set can be scheduled to
meet deadlines on a heterogeneous multiprocessor platform
by an optimal task assignment scheme that allows task
migrations then LP-EE meets deadlines as well with no
migrations if given processors twice as fast. We illustrate
this with an example
Partitioned scheduling of multimode systems on multiprocessor platforms: when to do the mode transition?
Systems composed of distinct operational modes are a common necessity for embedded applications with strict timing
requirements. With the emergence of multi-core platforms protocols to handle these systems are required in order to
provide this basic functionality.In this work a description on the problems of creating an effective mode-transition
protocol are presented and it is proven that in some cases previous single-core protocols can not be extended to handle
the mode-transition in multi-core