Description Usage Arguments Details
At-scale verification routines for distributed linear algebra.
1 2 3 4 5 6 7 8 9 10 11 | verify.svd(nrows = 1000, ncols = 1000, mean = 0, sd = 1, bldim = 8,
tol = 1e-07, ICTXT = .pbd_env$ictxt)
verify.chol(nrows = 1000, mean = 0, sd = 1, bldim = 8, tol = 1e-07,
ICTXT = .pbd_env$ictxt)
verify.inverse(nrows = 1000, mean = 0, sd = 1, bldim = 8, tol = 1e-07,
ICTXT = .pbd_env$ictxt)
verify.solve(nrows = 1000, mean = 0, sd = 1, const = 1, bldim = 8,
tol = 1e-07, ICTXT = .pbd_env$ictxt)
|
nrows, ncols |
global dimension. |
mean, sd |
mean and standard deviation when sampling from a normal distribution. |
bldim |
blocking dimension. |
tol |
numeric tolerance for testing equality. Differences smaller than
|
ICTXT |
BLACS context |
const |
numerical value for generating a constant |
These routines numerically verify the accuracy of the given
operation. Each operation generates only the local data that
is needed, and one never needs to store the global problem on
any one rank (unless bldim
is set inappropriately).
For example, verify.solve()
will generate the A
matrix and "true solution" x
to the problem Ax=b
,
each as distributed objects. Next, the "right hand side" b
is generated by multiplying A
and x
together.
Finally, the numericaly solution x
is computed and compared
against the known true value at the specified numerical tolerance.
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