2,402 research outputs found
TANGO: Transparent heterogeneous hardware Architecture deployment for eNergy Gain in Operation
The paper is concerned with the issue of how software systems actually use
Heterogeneous Parallel Architectures (HPAs), with the goal of optimizing power
consumption on these resources. It argues the need for novel methods and tools
to support software developers aiming to optimise power consumption resulting
from designing, developing, deploying and running software on HPAs, while
maintaining other quality aspects of software to adequate and agreed levels. To
do so, a reference architecture to support energy efficiency at application
construction, deployment, and operation is discussed, as well as its
implementation and evaluation plans.Comment: Part of the Program Transformation for Programmability in
Heterogeneous Architectures (PROHA) workshop, Barcelona, Spain, 12th March
2016, 7 pages, LaTeX, 3 PNG figure
MPI-Vector-IO: Parallel I/O and Partitioning for Geospatial Vector Data
In recent times, geospatial datasets are growing in terms of size, complexity and heterogeneity. High performance systems are needed to analyze such data to produce actionable insights in an efficient manner. For polygonal a.k.a vector datasets, operations such as I/O, data partitioning, communication, and load balancing becomes challenging in a cluster environment. In this work, we present MPI-Vector-IO 1 , a parallel I/O library that we have designed using MPI-IO specifically for partitioning and reading irregular vector data formats such as Well Known Text. It makes MPI aware of spatial data, spatial primitives and provides support for spatial data types embedded within collective computation and communication using MPI message-passing library. These abstractions along with parallel I/O support are useful for parallel Geographic Information System (GIS) application development on HPC platforms
A Study of Energy and Locality Effects using Space-filling Curves
The cost of energy is becoming an increasingly important driver for the
operating cost of HPC systems, adding yet another facet to the challenge of
producing efficient code. In this paper, we investigate the energy implications
of trading computation for locality using Hilbert and Morton space-filling
curves with dense matrix-matrix multiplication. The advantage of these curves
is that they exhibit an inherent tiling effect without requiring specific
architecture tuning. By accessing the matrices in the order determined by the
space-filling curves, we can trade computation for locality. The index
computation overhead of the Morton curve is found to be balanced against its
locality and energy efficiency, while the overhead of the Hilbert curve
outweighs its improvements on our test system.Comment: Proceedings of the 2014 IEEE International Parallel & Distributed
Processing Symposium Workshops (IPDPSW
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