Details:
- Removed explicit reference to The University of Texas at Austin in the
third clause of the license comment blocks of all relevant files and
replaced it with a more all-encompassing "copyright holder(s)".
- Removed duplicate words ("derived") from a few kernels' license
comment blocks.
- Homogenized license comment block in kernels/zen/3/bli_gemm_small.c
with format of all other comment blocks.
Details:
- Forgot to apply the column index range fix in 10f179f to situations
when "quiet" mode (-q) is requested. This commit applies the new
column index range modifications to the quiet case.
Details:
- Updated the irun.py script so that it updates the matlab column index
range (if found) to reflect the additional columns of data that are
substituted in. Thanks to Devangi Parikh for recognizing and reporting
this issue.
Details:
- Added irun.py script to 'build' directory. This irun.py script is a
python script for repeatedly invoking a test driver executable, such
as those found in test/3m4m, and replace the performance output column
with four columns that aggregate statistics. Specifically, the script
reports the minimum, average, maximum, and standard deviation for each
problem size. This script is useful especially (though not
exclusively) when trying to determine the impact of relatively minor
changes to the code, or other small optimizations that may be
difficult to distinguish from "noise." One way this "noise" manifests
is that a test executable may run slightly slower or faster for all
problem sizes (and all implementations) tested by the executable over
the life of a single execution. The cause of these minor
across-the-board pertubations in the overall performance signatures is
unknown, though we hypothesize that it may relate to any number of
issues such as operating system scheduling, where in memory the
program is loaded, or how the CPU clock frequency is throttled at the
time of execution. Regardless of the source of these subtle
performance anomalies, the statistical properties reported by the
irun.py script help the user to more precisely characterize the
underlying performance exhibited by any given test driver, which
allows him or her to make better judgments about the true difference
in performance between two implementations, or minor changes within a
single implementation.
