17,000 research outputs found
Parallel algorithms for two processors precedence constraint scheduling
The final publication is available at link.springer.comPeer ReviewedPostprint (author's final draft
Multi-criteria scheduling of pipeline workflows
Mapping workflow applications onto parallel platforms is a challenging
problem, even for simple application patterns such as pipeline graphs. Several
antagonist criteria should be optimized, such as throughput and latency (or a
combination). In this paper, we study the complexity of the bi-criteria mapping
problem for pipeline graphs on communication homogeneous platforms. In
particular, we assess the complexity of the well-known chains-to-chains problem
for different-speed processors, which turns out to be NP-hard. We provide
several efficient polynomial bi-criteria heuristics, and their relative
performance is evaluated through extensive simulations
Non-Cooperative Scheduling of Multiple Bag-of-Task Applications
Multiple applications that execute concurrently on heterogeneous platforms
compete for CPU and network resources. In this paper we analyze the behavior of
non-cooperative schedulers using the optimal strategy that maximize their
efficiency while fairness is ensured at a system level ignoring applications
characteristics. We limit our study to simple single-level master-worker
platforms and to the case where each scheduler is in charge of a single
application consisting of a large number of independent tasks. The tasks of a
given application all have the same computation and communication requirements,
but these requirements can vary from one application to another. In this
context, we assume that each scheduler aims at maximizing its throughput. We
give closed-form formula of the equilibrium reached by such a system and study
its performance. We characterize the situations where this Nash equilibrium is
optimal (in the Pareto sense) and show that even though no catastrophic
situation (Braess-like paradox) can occur, such an equilibrium can be
arbitrarily bad for any classical performance measure
Revisiting Matrix Product on Master-Worker Platforms
This paper is aimed at designing efficient parallel matrix-product algorithms
for heterogeneous master-worker platforms. While matrix-product is
well-understood for homogeneous 2D-arrays of processors (e.g., Cannon algorithm
and ScaLAPACK outer product algorithm), there are three key hypotheses that
render our work original and innovative:
- Centralized data. We assume that all matrix files originate from, and must
be returned to, the master.
- Heterogeneous star-shaped platforms. We target fully heterogeneous
platforms, where computational resources have different computing powers.
- Limited memory. Because we investigate the parallelization of large
problems, we cannot assume that full matrix panels can be stored in the worker
memories and re-used for subsequent updates (as in ScaLAPACK).
We have devised efficient algorithms for resource selection (deciding which
workers to enroll) and communication ordering (both for input and result
messages), and we report a set of numerical experiments on various platforms at
Ecole Normale Superieure de Lyon and the University of Tennessee. However, we
point out that in this first version of the report, experiments are limited to
homogeneous platforms
Energy Efficient Scheduling and Routing via Randomized Rounding
We propose a unifying framework based on configuration linear programs and
randomized rounding, for different energy optimization problems in the dynamic
speed-scaling setting. We apply our framework to various scheduling and routing
problems in heterogeneous computing and networking environments. We first
consider the energy minimization problem of scheduling a set of jobs on a set
of parallel speed scalable processors in a fully heterogeneous setting. For
both the preemptive-non-migratory and the preemptive-migratory variants, our
approach allows us to obtain solutions of almost the same quality as for the
homogeneous environment. By exploiting the result for the
preemptive-non-migratory variant, we are able to improve the best known
approximation ratio for the single processor non-preemptive problem.
Furthermore, we show that our approach allows to obtain a constant-factor
approximation algorithm for the power-aware preemptive job shop scheduling
problem. Finally, we consider the min-power routing problem where we are given
a network modeled by an undirected graph and a set of uniform demands that have
to be routed on integral routes from their sources to their destinations so
that the energy consumption is minimized. We improve the best known
approximation ratio for this problem.Comment: 27 page
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