2,327 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
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
Preemptive Thread Block Scheduling with Online Structural Runtime Prediction for Concurrent GPGPU Kernels
Recent NVIDIA Graphics Processing Units (GPUs) can execute multiple kernels
concurrently. On these GPUs, the thread block scheduler (TBS) uses the FIFO
policy to schedule their thread blocks. We show that FIFO leaves performance to
chance, resulting in significant loss of performance and fairness. To improve
performance and fairness, we propose use of the preemptive Shortest Remaining
Time First (SRTF) policy instead. Although SRTF requires an estimate of runtime
of GPU kernels, we show that such an estimate of the runtime can be easily
obtained using online profiling and exploiting a simple observation on GPU
kernels' grid structure. Specifically, we propose a novel Structural Runtime
Predictor. Using a simple Staircase model of GPU kernel execution, we show that
the runtime of a kernel can be predicted by profiling only the first few thread
blocks. We evaluate an online predictor based on this model on benchmarks from
ERCBench, and find that it can estimate the actual runtime reasonably well
after the execution of only a single thread block. Next, we design a thread
block scheduler that is both concurrent kernel-aware and uses this predictor.
We implement the SRTF policy and evaluate it on two-program workloads from
ERCBench. SRTF improves STP by 1.18x and ANTT by 2.25x over FIFO. When compared
to MPMax, a state-of-the-art resource allocation policy for concurrent kernels,
SRTF improves STP by 1.16x and ANTT by 1.3x. To improve fairness, we also
propose SRTF/Adaptive which controls resource usage of concurrently executing
kernels to maximize fairness. SRTF/Adaptive improves STP by 1.12x, ANTT by
2.23x and Fairness by 2.95x compared to FIFO. Overall, our implementation of
SRTF achieves system throughput to within 12.64% of Shortest Job First (SJF, an
oracle optimal scheduling policy), bridging 49% of the gap between FIFO and
SJF.Comment: 14 pages, full pre-review version of PACT 2014 poste
A Schedulability Analysis Framework for Real-time Infrastructure Systems Managing Heterogeneous Resources
REACTION 2012. 1st International workshop on Real-time and distributed computing in emerging applications. December 4th, 2012, San Juan, Puerto Rico.Electricity generating systems, such as smart grid
systems, and water management systems are infrastructure
systems that manage resources critical to human life. In the
systems, resources are produced and managed to supply them
to various consumers, such as building, car, factory, and
household, according to their needs and priorities. Reliable
supply of resources depends not only on sufficient production
of resources but also on reliable sharing of resource supply
facilities. This paper presents a schedulability analysis framework.
A prominent characteristic of the framework is that it
considers at once the two types of resources, i.e. consumable
resources, such as electricity, energy, and water, and sharable
resources, such as pipelines, storages, and processors, are
considered. To apply a formal approach to schedulability
analysis of infrastructure system, this paper classifies the types
of resources and real-time jobs for infrastructure systems. Then
based on the classification , it presents an architectural model
and a schedulability analysis framework.This research was supported by the KAIST High Risk High Return Project (HRHRP)
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