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[ 4 / 4 ] Application profile is long enough (123.58 s)
To have good quality measurements, it is advised that the application profiling time is greater than 10 seconds.
[ 0 / 3 ] Some functions are compiled with a low optimization level (O0 or O1)
To have better performances, it is advised to help the compiler by using a proper optimization level (-O2 of higher). Warning, depending on compilers, faster optimization levels can decrease numeric accuracy.
[ 0 / 3 ] Most of time spent in analyzed modules comes from functions without compilation information
Functions without compilation information (typically not compiled with -g) cumulate 100.00% of the time spent in analyzed modules. Check that -g is present. Remark: if -g is indeed used, this can also be due to some compiler built-in functions (typically math) or statically linked libraries. This warning can be ignored in that case.
[ 0 / 3 ] Compilation of some functions is not optimized for the target processor
Architecture specific options are needed to produce efficient code for a specific processor ( -x(target) or -ax(target) ).
[ 2 / 2 ] Application is correctly profiled ("Others" category represents 0.05 % of the execution time)
To have a representative profiling, it is advised that the category "Others" represents less than 20% of the execution time in order to analyze as much as possible of the user code
[ 4 / 4 ] Enough time of the experiment time spent in analyzed loops (43.65%)
If the time spent in analyzed loops is less than 30%, standard loop optimizations will have a limited impact on application performances.
[ 4 / 4 ] Loop profile is not flat
No hotspot found in the application (greatest loop coverage is 2.97%), but the twenty hottest loops cumulated coverage is representative enough (29.27% > 20%)
[ 4 / 4 ] Enough time of the experiment time spent in analyzed innermost loops (35.91%)
If the time spent in analyzed innermost loops is less than 15%, standard innermost loop optimizations such as vectorisation will have a limited impact on application performances.
[ 0 / 3 ] More than 10% (20.53%) is spend in BLAS1 operations
It could be more efficient to inline by hand BLAS1 operations
[ 3 / 3 ] Cumulative Outermost/In between loops coverage (7.74%) lower than cumulative innermost loop coverage (35.91%)
Having cumulative Outermost/In between loops coverage greater than cumulative innermost loop coverage will make loop optimization more complex
[ 2 / 2 ] Less than 10% (0.83%) is spend in Libm/SVML (special functions)
[ 0 / 2 ] More than 10% (21.61%) is spend in BLAS2 operations
BLAS2 calls usually make a poor cache usage. Try blocking. Such calls should probably benefit from inlining. Such inlining will have to be hand made.
| Loop ID | Module | Analysis | Penalty Score | Coverage (%) | Vectorization Ratio (%) | Vector Length Use (%) |
|---|---|---|---|---|---|---|
| ►3162 | exec | Inefficient vectorization. | 2 | 2.97 | 100 | 50 |
| ○ | [SA] Inefficient vectorization: use of shorter than available vector length - Force compiler to use proper vector length. CAUTION: use of 512 bits vectors could be more expensive than 256 bits on some processors. Use intrinsics (costly and not portable). The issue costs 2 points. | 2 | ||||
| ○ | Warning! There is no dynamic data for this loop. Some checks can not been performed. | 0 | ||||
| ►2654 | exec | Partial or unexisting vectorization - Use pragma to force vectorization and check potential dependencies between array access. | 12 | 2.72 | 0 | 12.5 |
| ○ | [SA] Presence of constant non unit stride data access - Use array restructuring, perform loop interchange or use gather instructions to lower a bit the cost. There are 6 issues ( = data accesses) costing 2 point each. | 12 | ||||
| ►2653 | exec | Partial or unexisting vectorization - Use pragma to force vectorization and check potential dependencies between array access. | 16 | 2.31 | 0 | 12.5 |
| ○ | [SA] Presence of constant non unit stride data access - Use array restructuring, perform loop interchange or use gather instructions to lower a bit the cost. There are 8 issues ( = data accesses) costing 2 point each. | 16 | ||||
| ►3085 | exec | Inefficient vectorization. | 2 | 2.07 | 100 | 50 |
| ○ | [SA] Inefficient vectorization: use of shorter than available vector length - Force compiler to use proper vector length. CAUTION: use of 512 bits vectors could be more expensive than 256 bits on some processors. Use intrinsics (costly and not portable). The issue costs 2 points. | 2 | ||||
| ○ | Warning! There is no dynamic data for this loop. Some checks can not been performed. | 0 | ||||
| ►3293 | exec | Partial or unexisting vectorization - Use pragma to force vectorization and check potential dependencies between array access. | 21 | 2.02 | 7.69 | 12.98 |
| ○ | [SA] Presence of constant non unit stride data access - Use array restructuring, perform loop interchange or use gather instructions to lower a bit the cost. There are 10 issues ( = data accesses) costing 2 point each. | 20 | ||||
| ○ | [SA] Presence of calls - Inline either by compiler or by hand and use SVML for libm calls. There are 1 issues (= calls) costing 1 point each. | 1 | ||||
| ►2563 | exec | Partial or unexisting vectorization - Use pragma to force vectorization and check potential dependencies between array access. | 14 | 1.66 | 73.5 | 20.51 |
| ○ | [SA] Presence of constant non unit stride data access - Use array restructuring, perform loop interchange or use gather instructions to lower a bit the cost. There are 5 issues ( = data accesses) costing 2 point each. | 10 | ||||
| ○ | [SA] Presence of indirect accesses - Use array restructuring or gather instructions to lower the cost. There are 1 issues ( = indirect data accesses) costing 4 point each. | 4 | ||||
| ►2399 | exec | Inefficient vectorization. | 4 | 1.59 | 100 | 50 |
| ○ | [SA] Inefficient vectorization: use of shorter than available vector length - Force compiler to use proper vector length. CAUTION: use of 512 bits vectors could be more expensive than 256 bits on some processors. Use intrinsics (costly and not portable). The issue costs 2 points. | 2 | ||||
| ○ | [SA] Presence of a large number of scalar integer instructions - Simplify loop structure, perform loop splitting or perform unroll and jam. This issue costs 2 points. | 2 | ||||
| ○ | Warning! There is no dynamic data for this loop. Some checks can not been performed. | 0 | ||||
| ►2649 | exec | Partial or unexisting vectorization - Use pragma to force vectorization and check potential dependencies between array access. | 12 | 1.58 | 0 | 12.5 |
| ○ | [SA] Presence of constant non unit stride data access - Use array restructuring, perform loop interchange or use gather instructions to lower a bit the cost. There are 4 issues ( = data accesses) costing 2 point each. | 8 | ||||
| ○ | [SA] Presence of indirect accesses - Use array restructuring or gather instructions to lower the cost. There are 1 issues ( = indirect data accesses) costing 4 point each. | 4 | ||||
| ►4303 | exec | Partial or unexisting vectorization - Use pragma to force vectorization and check potential dependencies between array access. | 14 | 1.57 | 0 | 11.86 |
| ○ | [SA] Too many paths (8 paths) - Simplify control structure. There are 8 issues ( = paths) costing 1 point each with a malus of 4 points. | 12 | ||||
| ○ | [SA] Non innermost loop (InBetween) - Collapse loop with innermost ones. This issue costs 2 points. | 2 | ||||
| ○ | Warning! Some static analysis are missing because the loop has too many paths. Use a higher value for --maximal_path_number option. | 0 | ||||
| ○4552 | exec | Partial or unexisting vectorization - No issue detected | 0 | 1.34 | 0 | 6.25 |