21,337 research outputs found
Cognitive Radio Networks: Realistic or Not?
A large volume of research has been conducted in the cognitive radio (CR)
area the last decade. However, the deployment of a commercial CR network is yet
to emerge. A large portion of the existing literature does not build on real
world scenarios, hence, neglecting various important interactions of the
research with commercial telecommunication networks. For instance, a lot of
attention has been paid to spectrum sensing as the front line functionality
that needs to be completed in an efficient and accurate manner to enable an
opportunistic CR network architecture. This is necessary to detect the
existence of spectrum holes without which no other procedure can be fulfilled.
However, simply sensing (cooperatively or not) the energy received from a
primary transmitter cannot enable correct dynamic spectrum access. For example,
the low strength of a primary transmitter's signal does not assure that there
will be no interference to a nearby primary receiver. In addition, the presence
of a primary transmitter's signal does not mean that CR network users cannot
access the spectrum since there might not be any primary receiver in the
vicinity. Despite the existing elegant and clever solutions to the DSA problem
no robust, implementable scheme has emerged. In this paper, we challenge the
basic premises of the proposed schemes. We further argue that addressing the
technical challenges we face in deploying robust CR networks can only be
achieved if we radically change the way we design their basic functionalities.
In support of our argument, we present a set of real-world scenarios, inspired
by realistic settings in commercial telecommunications networks, focusing on
spectrum sensing as a basic and critical functionality in the deployment of
CRs. We use these scenarios to show why existing DSA paradigms are not amenable
to realistic deployment in complex wireless environments.Comment: Work in progres
Scalable Breadth-First Search on a GPU Cluster
On a GPU cluster, the ratio of high computing power to communication
bandwidth makes scaling breadth-first search (BFS) on a scale-free graph
extremely challenging. By separating high and low out-degree vertices, we
present an implementation with scalable computation and a model for scalable
communication for BFS and direction-optimized BFS. Our communication model uses
global reduction for high-degree vertices, and point-to-point transmission for
low-degree vertices. Leveraging the characteristics of degree separation, we
reduce the graph size to one third of the conventional edge list
representation. With several other optimizations, we observe linear weak
scaling as we increase the number of GPUs, and achieve 259.8 GTEPS on a
scale-33 Graph500 RMAT graph with 124 GPUs on the latest CORAL early access
system.Comment: 12 pages, 13 figures. To appear at IPDPS 201
Adapting the interior point method for the solution of linear programs on high performance computers
In this paper we describe a unified algorithmic framework for the interior point method (IPM) of solving Linear Programs (LPs) which allows us to adapt it over a range of high performance computer architectures. We set out the reasons as to why IPM makes better use of high performance computer architecture than the sparse simplex method. In the inner iteration of the IPM a search direction is computed using Newton or higher order methods. Computationally this involves solving a sparse symmetric positive definite (SSPD) system of equations. The choice of direct and indirect methods for the solution of this system and the design of data structures to take advantage of coarse grain parallel and massively parallel computer architectures are considered in detail. Finally, we present experimental results of solving NETLIB test problems on examples of these architectures and put forward arguments as to why integration of the system within sparse simplex is beneficial
The impact of global communication latency at extreme scales on Krylov methods
Krylov Subspace Methods (KSMs) are popular numerical tools for solving large linear systems of equations. We consider their role in solving sparse systems on future massively parallel distributed memory machines, by estimating future performance of their constituent operations. To this end we construct a model that is simple, but which takes topology and network acceleration into account as they are important considerations. We show that, as the number of nodes of a parallel machine increases to very large numbers, the increasing latency cost of reductions may well become a problematic bottleneck for traditional formulations of these methods. Finally, we discuss how pipelined KSMs can be used to tackle the potential problem, and appropriate pipeline depths
- …