1,981 research outputs found
Resource provisioning in Science Clouds: Requirements and challenges
Cloud computing has permeated into the information technology industry in the
last few years, and it is emerging nowadays in scientific environments. Science
user communities are demanding a broad range of computing power to satisfy the
needs of high-performance applications, such as local clusters,
high-performance computing systems, and computing grids. Different workloads
are needed from different computational models, and the cloud is already
considered as a promising paradigm. The scheduling and allocation of resources
is always a challenging matter in any form of computation and clouds are not an
exception. Science applications have unique features that differentiate their
workloads, hence, their requirements have to be taken into consideration to be
fulfilled when building a Science Cloud. This paper will discuss what are the
main scheduling and resource allocation challenges for any Infrastructure as a
Service provider supporting scientific applications
Optimization of automatically generated multi-core code for the LTE RACH-PD algorithm
Embedded real-time applications in communication systems require high
processing power. Manual scheduling devel-oped for single-processor
applications is not suited to multi-core architectures. The Algorithm
Architecture Matching (AAM) methodology optimizes static application
implementation on multi-core architectures. The Random Access Channel Preamble
Detection (RACH-PD) is an algorithm for non-synchronized access of Long Term
Evolu-tion (LTE) wireless networks. LTE aims to improve the spectral efficiency
of the next generation cellular system. This paper de-scribes a complete
methodology for implementing the RACH-PD. AAM prototyping is applied to the
RACH-PD which is modelled as a Synchronous DataFlow graph (SDF). An efficient
implemen-tation of the algorithm onto a multi-core DSP, the TI C6487, is then
explained. Benchmarks for the solution are given
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