21,091 research outputs found
One machine, one minute, three billion tetrahedra
This paper presents a new scalable parallelization scheme to generate the 3D
Delaunay triangulation of a given set of points. Our first contribution is an
efficient serial implementation of the incremental Delaunay insertion
algorithm. A simple dedicated data structure, an efficient sorting of the
points and the optimization of the insertion algorithm have permitted to
accelerate reference implementations by a factor three. Our second contribution
is a multi-threaded version of the Delaunay kernel that is able to concurrently
insert vertices. Moore curve coordinates are used to partition the point set,
avoiding heavy synchronization overheads. Conflicts are managed by modifying
the partitions with a simple rescaling of the space-filling curve. The
performances of our implementation have been measured on three different
processors, an Intel core-i7, an Intel Xeon Phi and an AMD EPYC, on which we
have been able to compute 3 billion tetrahedra in 53 seconds. This corresponds
to a generation rate of over 55 million tetrahedra per second. We finally show
how this very efficient parallel Delaunay triangulation can be integrated in a
Delaunay refinement mesh generator which takes as input the triangulated
surface boundary of the volume to mesh
Pairwise and incremental multi-stage alignment of metagenomes: A new proposal
Traditional comparisons between metagenomes are often performed using reference databases as intermediary templates from which to obtain distance metrics. However, in order to fully exploit the potential of the information contained within metagenomes, it becomes of interest to remove any intermediate agent that is prone to introduce errors or biased results. In this work, we perform an analysis over the state of the art methods and deduce that it is necessary to employ fine-grained methods in order to assess similarity between metagenomes. In addition, we propose our developed method for accurate and fast matching of reads.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech
Model-driven Scheduling for Distributed Stream Processing Systems
Distributed Stream Processing frameworks are being commonly used with the
evolution of Internet of Things(IoT). These frameworks are designed to adapt to
the dynamic input message rate by scaling in/out.Apache Storm, originally
developed by Twitter is a widely used stream processing engine while others
includes Flink, Spark streaming. For running the streaming applications
successfully there is need to know the optimal resource requirement, as
over-estimation of resources adds extra cost.So we need some strategy to come
up with the optimal resource requirement for a given streaming application. In
this article, we propose a model-driven approach for scheduling streaming
applications that effectively utilizes a priori knowledge of the applications
to provide predictable scheduling behavior. Specifically, we use application
performance models to offer reliable estimates of the resource allocation
required. Further, this intuition also drives resource mapping, and helps
narrow the estimated and actual dataflow performance and resource utilization.
Together, this model-driven scheduling approach gives a predictable application
performance and resource utilization behavior for executing a given DSPS
application at a target input stream rate on distributed resources.Comment: 54 page
Muppet: MapReduce-Style Processing of Fast Data
MapReduce has emerged as a popular method to process big data. In the past
few years, however, not just big data, but fast data has also exploded in
volume and availability. Examples of such data include sensor data streams, the
Twitter Firehose, and Facebook updates. Numerous applications must process fast
data. Can we provide a MapReduce-style framework so that developers can quickly
write such applications and execute them over a cluster of machines, to achieve
low latency and high scalability? In this paper we report on our investigation
of this question, as carried out at Kosmix and WalmartLabs. We describe
MapUpdate, a framework like MapReduce, but specifically developed for fast
data. We describe Muppet, our implementation of MapUpdate. Throughout the
description we highlight the key challenges, argue why MapReduce is not well
suited to address them, and briefly describe our current solutions. Finally, we
describe our experience and lessons learned with Muppet, which has been used
extensively at Kosmix and WalmartLabs to power a broad range of applications in
social media and e-commerce.Comment: VLDB201
Building Ideapreneurship Capability: Delivering Differentiated Customer Value From the Frontline
[Excerpt] Transformational innovation appears to be a dominant aspiration of most leading firms. While such innovation efforts are pervasive across industries and regions, the results from these endeavors can be highly varied. Instead of a single-minded focus on transformational innovation, could incremental innovation, if directly tied to real customer need, be a powerful opportunity for growth and sustainability in this dynamic world? HCL Technologies, a global IT services firm based in Noida, India believes so
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