20,697 research outputs found
QR Factorization of Tall and Skinny Matrices in a Grid Computing Environment
Previous studies have reported that common dense linear algebra operations do
not achieve speed up by using multiple geographical sites of a computational
grid. Because such operations are the building blocks of most scientific
applications, conventional supercomputers are still strongly predominant in
high-performance computing and the use of grids for speeding up large-scale
scientific problems is limited to applications exhibiting parallelism at a
higher level. We have identified two performance bottlenecks in the distributed
memory algorithms implemented in ScaLAPACK, a state-of-the-art dense linear
algebra library. First, because ScaLAPACK assumes a homogeneous communication
network, the implementations of ScaLAPACK algorithms lack locality in their
communication pattern. Second, the number of messages sent in the ScaLAPACK
algorithms is significantly greater than other algorithms that trade flops for
communication. In this paper, we present a new approach for computing a QR
factorization -- one of the main dense linear algebra kernels -- of tall and
skinny matrices in a grid computing environment that overcomes these two
bottlenecks. Our contribution is to articulate a recently proposed algorithm
(Communication Avoiding QR) with a topology-aware middleware (QCG-OMPI) in
order to confine intensive communications (ScaLAPACK calls) within the
different geographical sites. An experimental study conducted on the Grid'5000
platform shows that the resulting performance increases linearly with the
number of geographical sites on large-scale problems (and is in particular
consistently higher than ScaLAPACK's).Comment: Accepted at IPDPS10. (IEEE International Parallel & Distributed
Processing Symposium 2010 in Atlanta, GA, USA.
Adaptive Dispatching of Tasks in the Cloud
The increasingly wide application of Cloud Computing enables the
consolidation of tens of thousands of applications in shared infrastructures.
Thus, meeting the quality of service requirements of so many diverse
applications in such shared resource environments has become a real challenge,
especially since the characteristics and workload of applications differ widely
and may change over time. This paper presents an experimental system that can
exploit a variety of online quality of service aware adaptive task allocation
schemes, and three such schemes are designed and compared. These are a
measurement driven algorithm that uses reinforcement learning, secondly a
"sensible" allocation algorithm that assigns jobs to sub-systems that are
observed to provide a lower response time, and then an algorithm that splits
the job arrival stream into sub-streams at rates computed from the hosts'
processing capabilities. All of these schemes are compared via measurements
among themselves and with a simple round-robin scheduler, on two experimental
test-beds with homogeneous and heterogeneous hosts having different processing
capacities.Comment: 10 pages, 9 figure
A Multilevel Approach to Topology-Aware Collective Operations in Computational Grids
The efficient implementation of collective communiction operations has
received much attention. Initial efforts produced "optimal" trees based on
network communication models that assumed equal point-to-point latencies
between any two processes. This assumption is violated in most practical
settings, however, particularly in heterogeneous systems such as clusters of
SMPs and wide-area "computational Grids," with the result that collective
operations perform suboptimally. In response, more recent work has focused on
creating topology-aware trees for collective operations that minimize
communication across slower channels (e.g., a wide-area network). While these
efforts have significant communication benefits, they all limit their view of
the network to only two layers. We present a strategy based upon a multilayer
view of the network. By creating multilevel topology-aware trees we take
advantage of communication cost differences at every level in the network. We
used this strategy to implement topology-aware versions of several MPI
collective operations in MPICH-G2, the Globus Toolkit[tm]-enabled version of
the popular MPICH implementation of the MPI standard. Using information about
topology provided by MPICH-G2, we construct these multilevel topology-aware
trees automatically during execution. We present results demonstrating the
advantages of our multilevel approach by comparing it to the default
(topology-unaware) implementation provided by MPICH and a topology-aware
two-layer implementation.Comment: 16 pages, 8 figure
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