18,915 research outputs found
Parallel integer relation detection: techniques and applications
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Paraiso : An Automated Tuning Framework for Explicit Solvers of Partial Differential Equations
We propose Paraiso, a domain specific language embedded in functional
programming language Haskell, for automated tuning of explicit solvers of
partial differential equations (PDEs) on GPUs as well as multicore CPUs. In
Paraiso, one can describe PDE solving algorithms succinctly using tensor
equations notation. Hydrodynamic properties, interpolation methods and other
building blocks are described in abstract, modular, re-usable and combinable
forms, which lets us generate versatile solvers from little set of Paraiso
source codes.
We demonstrate Paraiso by implementing a compressive hydrodynamics solver. A
single source code less than 500 lines can be used to generate solvers of
arbitrary dimensions, for both multicore CPUs and GPUs. We demonstrate both
manual annotation based tuning and evolutionary computing based automated
tuning of the program.Comment: 52 pages, 14 figures, accepted for publications in Computational
Science and Discover
Limits on Fundamental Limits to Computation
An indispensable part of our lives, computing has also become essential to
industries and governments. Steady improvements in computer hardware have been
supported by periodic doubling of transistor densities in integrated circuits
over the last fifty years. Such Moore scaling now requires increasingly heroic
efforts, stimulating research in alternative hardware and stirring controversy.
To help evaluate emerging technologies and enrich our understanding of
integrated-circuit scaling, we review fundamental limits to computation: in
manufacturing, energy, physical space, design and verification effort, and
algorithms. To outline what is achievable in principle and in practice, we
recall how some limits were circumvented, compare loose and tight limits. We
also point out that engineering difficulties encountered by emerging
technologies may indicate yet-unknown limits.Comment: 15 pages, 4 figures, 1 tabl
Grace: a Cross-platform Micromagnetic Simulator On Graphics Processing Units
A micromagnetic simulator running on graphics processing unit (GPU) is
presented. It achieves significant performance boost as compared to previous
central processing unit (CPU) simulators, up to two orders of magnitude for
large input problems. Different from GPU implementations of other research
groups, this simulator is developed with C++ Accelerated Massive Parallelism
(C++ AMP) and is hardware platform compatible. It runs on GPU from venders
include NVidia, AMD and Intel, which paved the way for fast micromagnetic
simulation on both high-end workstations with dedicated graphics cards and
low-end personal computers with integrated graphics card. A copy of the
simulator software is publicly available.Comment: 9 pages, 5 figure
Adapting the interior point method for the solution of linear programs on high performance computers
In this paper we describe a unified algorithmic framework for the interior point method (IPM) of solving Linear Programs (LPs) which allows us to adapt it over a range of high performance computer architectures. We set out the reasons as to why IPM makes better use of high performance computer architecture than the sparse simplex method. In the inner iteration of the IPM a search direction is computed using Newton or higher order methods. Computationally this involves solving a sparse symmetric positive definite (SSPD) system of equations. The choice of direct and indirect methods for the solution of this system and the design of data structures to take advantage of coarse grain parallel and massively parallel computer architectures are considered in detail. Finally, we present experimental results of solving NETLIB test problems on examples of these architectures and put forward arguments as to why integration of the system within sparse simplex is beneficial
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