44,654 research outputs found
A Distributed Economics-based Infrastructure for Utility Computing
Existing attempts at utility computing revolve around two approaches. The
first consists of proprietary solutions involving renting time on dedicated
utility computing machines. The second requires the use of heavy, monolithic
applications that are difficult to deploy, maintain, and use.
We propose a distributed, community-oriented approach to utility computing.
Our approach provides an infrastructure built on Web Services in which modular
components are combined to create a seemingly simple, yet powerful system. The
community-oriented nature generates an economic environment which results in
fair transactions between consumers and providers of computing cycles while
simultaneously encouraging improvements in the infrastructure of the
computational grid itself.Comment: 8 pages, 1 figur
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PGGA: A predictable and grouped genetic algorithm for job scheduling
This paper presents a predictable and grouped genetic algorithm (PGGA) for job scheduling. The novelty of the PGGA is twofold: (1) a job workload estimation algorithm is designed to estimate a job workload based on its historical execution records, (2) the divisible load theory (DLT) is employed to predict an optimal fitness value by which the PGGA speeds up the convergence process in searching a large scheduling space. Comparison with traditional scheduling methods such as first-come-first-serve (FCFS) and random scheduling, heuristics such as a typical genetic algorithm, Min-Min and Max-Min indicates that the PGGA is more effective and efficient in finding optimal scheduling solutions
Mixing multi-core CPUs and GPUs for scientific simulation software
Recent technological and economic developments have led to widespread availability of
multi-core CPUs and specialist accelerator processors such as graphical processing units
(GPUs). The accelerated computational performance possible from these devices can be very
high for some applications paradigms. Software languages and systems such as NVIDIA's
CUDA and Khronos consortium's open compute language (OpenCL) support a number of
individual parallel application programming paradigms. To scale up the performance of some
complex systems simulations, a hybrid of multi-core CPUs for coarse-grained parallelism and
very many core GPUs for data parallelism is necessary. We describe our use of hybrid applica-
tions using threading approaches and multi-core CPUs to control independent GPU devices.
We present speed-up data and discuss multi-threading software issues for the applications
level programmer and o er some suggested areas for language development and integration
between coarse-grained and ne-grained multi-thread systems. We discuss results from three
common simulation algorithmic areas including: partial di erential equations; graph cluster
metric calculations and random number generation. We report on programming experiences
and selected performance for these algorithms on: single and multiple GPUs; multi-core CPUs;
a CellBE; and using OpenCL. We discuss programmer usability issues and the outlook and
trends in multi-core programming for scienti c applications developers
Decomposition Algorithms for Stochastic Programming on a Computational Grid
We describe algorithms for two-stage stochastic linear programming with
recourse and their implementation on a grid computing platform. In particular,
we examine serial and asynchronous versions of the L-shaped method and a
trust-region method. The parallel platform of choice is the dynamic,
heterogeneous, opportunistic platform provided by the Condor system. The
algorithms are of master-worker type (with the workers being used to solve
second-stage problems, and the MW runtime support library (which supports
master-worker computations) is key to the implementation. Computational results
are presented on large sample average approximations of problems from the
literature.Comment: 44 page
Status and Future Perspectives for Lattice Gauge Theory Calculations to the Exascale and Beyond
In this and a set of companion whitepapers, the USQCD Collaboration lays out
a program of science and computing for lattice gauge theory. These whitepapers
describe how calculation using lattice QCD (and other gauge theories) can aid
the interpretation of ongoing and upcoming experiments in particle and nuclear
physics, as well as inspire new ones.Comment: 44 pages. 1 of USQCD whitepapers
Parallel Graph Decompositions Using Random Shifts
We show an improved parallel algorithm for decomposing an undirected
unweighted graph into small diameter pieces with a small fraction of the edges
in between. These decompositions form critical subroutines in a number of graph
algorithms. Our algorithm builds upon the shifted shortest path approach
introduced in [Blelloch, Gupta, Koutis, Miller, Peng, Tangwongsan, SPAA 2011].
By combining various stages of the previous algorithm, we obtain a
significantly simpler algorithm with the same asymptotic guarantees as the best
sequential algorithm
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