81,282 research outputs found
The Chameleon project in retrospective
In this paper we describe in retrospective the main results of a four year project, called Chameleon. As part of this project we developed a coarse-grained reconfigurable core for DSP algorithms in wireless devices denoted MONTIUM. After presenting the main achievements within this project we present the lessons learned from this project
Lessons Learned from Designing the Montium - a Coarse-Grained Reconfigurable Processing Tile
In this paper we describe in retrospective the main results of a four year project, called Chameleon. As part of this project we developed a coarse-grained reconfigurable core for DSP algorithms in wirelessdevices denoted MONTIUM. After presenting the main achievements within this project we present the lessons learned from this project
Fronthaul-Constrained Cloud Radio Access Networks: Insights and Challenges
As a promising paradigm for fifth generation (5G) wireless communication
systems, cloud radio access networks (C-RANs) have been shown to reduce both
capital and operating expenditures, as well as to provide high spectral
efficiency (SE) and energy efficiency (EE). The fronthaul in such networks,
defined as the transmission link between a baseband unit (BBU) and a remote
radio head (RRH), requires high capacity, but is often constrained. This
article comprehensively surveys recent advances in fronthaul-constrained
C-RANs, including system architectures and key techniques. In particular, key
techniques for alleviating the impact of constrained fronthaul on SE/EE and
quality of service for users, including compression and quantization,
large-scale coordinated processing and clustering, and resource allocation
optimization, are discussed. Open issues in terms of software-defined
networking, network function virtualization, and partial centralization are
also identified.Comment: 5 Figures, accepted by IEEE Wireless Communications. arXiv admin
note: text overlap with arXiv:1407.3855 by other author
Applying Constraint Databases in the Determination of Potential Minimal Conflicts to Polynomial Model-Based Diagnosis
Model-based Diagnosis allows the identification of the parts
which fail in a system. The models are based on the knowledge of the
system to diagnose, and may be represented by constraints associated
to the components. The variables of these constraints can be observable
or non-observable, depending on the situation of the sensors. In order to
obtain the potential minimal diagnosis in a system, an important issue is
related to finding out the potential minimal conflicts in an efficient way.
We consider that Constraint Databases represent an excellent option in
order to solve this problem in complex systems.
In this work we have used a novel logical architecture of Constraint
Databases which has allowed obtaining these potential conflicts by means
of the corresponding queries. Moreover, we have considered Gröbner
Bases as a projection operator to obtain the potential minimal conflicts
of a system. The first results obtained on this work, which are shown in
a heat exchangers example, have been very promising.Ministerio de Ciencia y Tecnología DPI2003-07146-C02-0
GLB: Lifeline-based Global Load Balancing library in X10
We present GLB, a programming model and an associated implementation that can
handle a wide range of irregular paral- lel programming problems running over
large-scale distributed systems. GLB is applicable both to problems that are
easily load-balanced via static scheduling and to problems that are hard to
statically load balance. GLB hides the intricate syn- chronizations (e.g.,
inter-node communication, initialization and startup, load balancing,
termination and result collection) from the users. GLB internally uses a
version of the lifeline graph based work-stealing algorithm proposed by
Saraswat et al. Users of GLB are simply required to write several pieces of
sequential code that comply with the GLB interface. GLB then schedules and
orchestrates the parallel execution of the code correctly and efficiently at
scale. We have applied GLB to two representative benchmarks: Betweenness
Centrality (BC) and Unbalanced Tree Search (UTS). Among them, BC can be
statically load-balanced whereas UTS cannot. In either case, GLB scales well--
achieving nearly linear speedup on different computer architectures (Power,
Blue Gene/Q, and K) -- up to 16K cores
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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