XL Fortran Enterprise Edition for AIX : User's Guide - IBM

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Reducing the costs of memory access through the effective use of caches and
translation look-aside buffers.
Overlapping computation and memory access through effective utilization of the
data prefetching capabilities provided by the hardware.
Improving the utilization of processor resources through reordering and
balancing the usage of instructions with complementary resource requirements.
-qhot=vector is the default when -qhot is specified. Compiling with -qhot=vector
transforms some loops to exploit optimized versions of functions rather than the
standard versions. The optimized functions reside in a built-in library that includes
functions and operations such as reciprocal, square root, and so on. The optimized
versions make different trade-offs with respect to precision versus performance.
Usage of -qstrict implies -qhot=novector.
Getting the Most out of -qhot
Try using -qhot along with -O3 for all of your code. (The compiler assumes at least
-O2 level for -qhot.) It is designed to have a neutral effect when no opportunities
for transformation exist.
v If you encounter unacceptably long compile times (this can happen with
complex loop nests) or if your performance degrades with the use of -qhot, try
using -qhot=novector, or -qstrict or -qcompact along with -qhot.
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If necessary, deactivate -qhot selectively, allowing it to improve some of your
code.
Optimizing Loops and Array Language
The -qhot option does the following transformations to improve the performance
of loops, array language, and memory management:
v Scalar replacement, loop blocking, distribution, fusion, interchange, reversal,
skewing, and unrolling
v Reducing generation of temporary arrays
It requires at least level 2 of -O. The -C option inhibits it.
If you have SMP hardware, you can enable automatic parallelization of loops by
specifying the -qsmp option. This optimization includes explicitly coded DO loops
as well as DO loops that are generated by the compiler for array language
(WHERE, FORALL, array assignment, and so on). The compiler can only
parallelize loops that are independent (each iteration can be computed
independently of any other iteration). One case where the compiler will not
automatically parallelize loops is where the loops contain I/O, because doing so
could lead to unexpected results. In this case, by using the PARALLEL DO or
work-sharing DO directive, you can advise the compiler that such a loop can be
safely parallelized. However, the type of I/O must be one of the following:
v Direct-access I/O where each iteration writes to or reads from a different record
v Sequential I/O where each iteration writes to or reads from a different unit
v Stream-access I/O where each iteration uses the POS= specifier to write to, or
read from, a different part of the file.
v Stream-access I/O where each iteration writes to, or reads from, a different unit.
For more details, refer to the description of the PARALLEL DO or work-sharing
DO directive in the XL Fortran Enterprise Edition for AIX Language Reference.
You can use the -qhot and -qsmp options on:
v Programs with performance bottlenecks that are caused by loops and structured
memory accesses
312 XL Fortran Enterprise Edition for AIX : User’s Guide