23,674 research outputs found
On the Tradeoff between Energy Harvesting and Caching in Wireless Networks
Self-powered, energy harvesting small cell base stations (SBS) are expected
to be an integral part of next-generation wireless networks. However, due to
uncertainties in harvested energy, it is necessary to adopt energy efficient
power control schemes to reduce an SBSs' energy consumption and thus ensure
quality-of-service (QoS) for users. Such energy-efficient design can also be
done via the use of content caching which reduces the usage of the
capacity-limited SBS backhaul. of popular content at SBS can also prove
beneficial in this regard by reducing the backhaul usage. In this paper, an
online energy efficient power control scheme is developed for an energy
harvesting SBS equipped with a wireless backhaul and local storage. In our
model, energy arrivals are assumed to be Poisson distributed and the popularity
distribution of requested content is modeled using Zipf's law. The power
control problem is formulated as a (discounted) infinite horizon dynamic
programming problem and solved numerically using the value iteration algorithm.
Using simulations, we provide valuable insights on the impact of energy
harvesting and caching on the energy and sum-throughput performance of the SBS
as the network size is varied. Our results also show that the size of cache and
energy harvesting equipment at the SBS can be traded off, while still meeting
the desired system performance.Comment: To be presented at the IEEE International Conference on
Communications (ICC), London, U.K., 201
Cache-Oblivious Selection in Sorted X+Y Matrices
Let X[0..n-1] and Y[0..m-1] be two sorted arrays, and define the mxn matrix A
by A[j][i]=X[i]+Y[j]. Frederickson and Johnson gave an efficient algorithm for
selecting the k-th smallest element from A. We show how to make this algorithm
IO-efficient. Our cache-oblivious algorithm performs O((m+n)/B) IOs, where B is
the block size of memory transfers
Near-optimal loop tiling by means of cache miss equations and genetic algorithms
The effectiveness of the memory hierarchy is critical for the performance of current processors. The performance of the memory hierarchy can be improved by means of program transformations such as loop tiling, which is a code transformation targeted to reduce capacity misses. This paper presents a novel systematic approach to perform near-optimal loop tiling based on an accurate data locality analysis (cache miss equations) and a powerful technique to search the solution space that is based on a genetic algorithm. The results show that this approach can remove practically all capacity misses for all considered benchmarks. The reduction of replacement misses results in a decrease of the miss ratio that can be as significant as a factor of 7 for the matrix multiply kernel.Peer ReviewedPostprint (published version
Matrix-free GPU implementation of a preconditioned conjugate gradient solver for anisotropic elliptic PDEs
Many problems in geophysical and atmospheric modelling require the fast
solution of elliptic partial differential equations (PDEs) in "flat" three
dimensional geometries. In particular, an anisotropic elliptic PDE for the
pressure correction has to be solved at every time step in the dynamical core
of many numerical weather prediction models, and equations of a very similar
structure arise in global ocean models, subsurface flow simulations and gas and
oil reservoir modelling. The elliptic solve is often the bottleneck of the
forecast, and an algorithmically optimal method has to be used and implemented
efficiently. Graphics Processing Units have been shown to be highly efficient
for a wide range of applications in scientific computing, and recently
iterative solvers have been parallelised on these architectures. We describe
the GPU implementation and optimisation of a Preconditioned Conjugate Gradient
(PCG) algorithm for the solution of a three dimensional anisotropic elliptic
PDE for the pressure correction in NWP. Our implementation exploits the strong
vertical anisotropy of the elliptic operator in the construction of a suitable
preconditioner. As the algorithm is memory bound, performance can be improved
significantly by reducing the amount of global memory access. We achieve this
by using a matrix-free implementation which does not require explicit storage
of the matrix and instead recalculates the local stencil. Global memory access
can also be reduced by rewriting the algorithm using loop fusion and we show
that this further reduces the runtime on the GPU. We demonstrate the
performance of our matrix-free GPU code by comparing it to a sequential CPU
implementation and to a matrix-explicit GPU code which uses existing libraries.
The absolute performance of the algorithm for different problem sizes is
quantified in terms of floating point throughput and global memory bandwidth.Comment: 18 pages, 7 figure
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