37,663 research outputs found
H-2 suboptimal output synchronization of heterogeneous multi-agent systems
This paper deals with the H2 suboptimal output synchronization problem for
heterogeneous linear multi-agent systems. Given a multi-agent system with
possibly distinct agents and an associated H2 cost functional, the aim is to
design output feedback based protocols that guarantee the associated cost to be
smaller than a given upper bound while the controlled network achieves output
synchronization. A design method is provided to compute such protocols. For
each agent, the computation of its two local control gains involves two Riccati
inequalities, each of dimension equal to the state space dimension of the
agent. A simulation example is provided to illustrate the performance of the
proposed protocols.Comment: 9 pages, 2 figures. arXiv admin note: text overlap with
arXiv:2001.0759
Evaluating Cache Coherent Shared Virtual Memory for Heterogeneous Multicore Chips
The trend in industry is towards heterogeneous multicore processors (HMCs),
including chips with CPUs and massively-threaded throughput-oriented processors
(MTTOPs) such as GPUs. Although current homogeneous chips tightly couple the
cores with cache-coherent shared virtual memory (CCSVM), this is not the
communication paradigm used by any current HMC. In this paper, we present a
CCSVM design for a CPU/MTTOP chip, as well as an extension of the pthreads
programming model, called xthreads, for programming this HMC. Our goal is to
evaluate the potential performance benefits of tightly coupling heterogeneous
cores with CCSVM
Multiplex PI-Control for Consensus in Networks of Heterogeneous Linear Agents
In this paper, we propose a multiplex proportional-integral approach, for
solving consensus problems in networks of heterogeneous nodes dynamics affected
by constant disturbances. The proportional and integral actions are deployed on
two different layers across the network, each with its own topology. Sufficient
conditions for convergence are derived that depend upon the structure of the
network, the parameters characterizing the control layers and the node
dynamics. The effectiveness of the theoretical results is illustrated using a
power network model as a representative example.Comment: 13 pages, 6 Figures, Preprint submitted to Automatic
Speculative Segmented Sum for Sparse Matrix-Vector Multiplication on Heterogeneous Processors
Sparse matrix-vector multiplication (SpMV) is a central building block for
scientific software and graph applications. Recently, heterogeneous processors
composed of different types of cores attracted much attention because of their
flexible core configuration and high energy efficiency. In this paper, we
propose a compressed sparse row (CSR) format based SpMV algorithm utilizing
both types of cores in a CPU-GPU heterogeneous processor. We first
speculatively execute segmented sum operations on the GPU part of a
heterogeneous processor and generate a possibly incorrect results. Then the CPU
part of the same chip is triggered to re-arrange the predicted partial sums for
a correct resulting vector. On three heterogeneous processors from Intel, AMD
and nVidia, using 20 sparse matrices as a benchmark suite, the experimental
results show that our method obtains significant performance improvement over
the best existing CSR-based SpMV algorithms. The source code of this work is
downloadable at https://github.com/bhSPARSE/Benchmark_SpMV_using_CSRComment: 22 pages, 8 figures, Published at Parallel Computing (PARCO
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