233,002 research outputs found
Deflated BiCGStab for linear equations in QCD problems
The large systems of complex linear equations that are generated in QCD
problems often have multiple right-hand sides (for multiple sources) and
multiple shifts (for multiple masses). Deflated GMRES methods have previously
been developed for solving multiple right-hand sides. Eigenvectors are
generated during solution of the first right-hand side and used to speed up
convergence for the other right-hand sides. Here we discuss deflating
non-restarted methods such as BiCGStab. For effective deflation, both left and
right eigenvectors are needed. Fortunately, with the Wilson matrix, left
eigenvectors can be derived from the right eigenvectors. We demonstrate for
difficult problems with kappa near kappa_c that deflating eigenvalues can
significantly improve BiCGStab. We also will look at improving solution of
twisted mass problems with multiple shifts. Projecting over previous solutions
is an easy way to reduce the work needed.Comment: 7 pages, 4 figures, presented at the XXV International Symposium on
Lattice Field Theory, 30 July - 4 August 2007, Regensburg, German
Computational linear algebra over finite fields
We present here algorithms for efficient computation of linear algebra
problems over finite fields
An Efficient Parallel Solver for SDD Linear Systems
We present the first parallel algorithm for solving systems of linear
equations in symmetric, diagonally dominant (SDD) matrices that runs in
polylogarithmic time and nearly-linear work. The heart of our algorithm is a
construction of a sparse approximate inverse chain for the input matrix: a
sequence of sparse matrices whose product approximates its inverse. Whereas
other fast algorithms for solving systems of equations in SDD matrices exploit
low-stretch spanning trees, our algorithm only requires spectral graph
sparsifiers
Improving Performance of Iterative Methods by Lossy Checkponting
Iterative methods are commonly used approaches to solve large, sparse linear
systems, which are fundamental operations for many modern scientific
simulations. When the large-scale iterative methods are running with a large
number of ranks in parallel, they have to checkpoint the dynamic variables
periodically in case of unavoidable fail-stop errors, requiring fast I/O
systems and large storage space. To this end, significantly reducing the
checkpointing overhead is critical to improving the overall performance of
iterative methods. Our contribution is fourfold. (1) We propose a novel lossy
checkpointing scheme that can significantly improve the checkpointing
performance of iterative methods by leveraging lossy compressors. (2) We
formulate a lossy checkpointing performance model and derive theoretically an
upper bound for the extra number of iterations caused by the distortion of data
in lossy checkpoints, in order to guarantee the performance improvement under
the lossy checkpointing scheme. (3) We analyze the impact of lossy
checkpointing (i.e., extra number of iterations caused by lossy checkpointing
files) for multiple types of iterative methods. (4)We evaluate the lossy
checkpointing scheme with optimal checkpointing intervals on a high-performance
computing environment with 2,048 cores, using a well-known scientific
computation package PETSc and a state-of-the-art checkpoint/restart toolkit.
Experiments show that our optimized lossy checkpointing scheme can
significantly reduce the fault tolerance overhead for iterative methods by
23%~70% compared with traditional checkpointing and 20%~58% compared with
lossless-compressed checkpointing, in the presence of system failures.Comment: 14 pages, 10 figures, HPDC'1
Hardness Results for Structured Linear Systems
We show that if the nearly-linear time solvers for Laplacian matrices and
their generalizations can be extended to solve just slightly larger families of
linear systems, then they can be used to quickly solve all systems of linear
equations over the reals. This result can be viewed either positively or
negatively: either we will develop nearly-linear time algorithms for solving
all systems of linear equations over the reals, or progress on the families we
can solve in nearly-linear time will soon halt
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