| baseline | locus440 |
|---|---|
[ 3.00 / 3 ] Architecture specific option -march=native is used | [ 3.00 / 3 ] Architecture specific option -march=native is used |
[ 2.40 / 3 ] Most of time spent in analyzed modules comes from functions without compilation information Functions without compilation information (typically not compiled with -g) cumulate 0.00% 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. | [ 2.40 / 3 ] Most of time spent in analyzed modules comes from functions without compilation information Functions without compilation information (typically not compiled with -g) cumulate 0.03% 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. |
[ 2 / 2 ] Application is correctly profiled ("Others" category represents 0.00 % 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 | [ 2 / 2 ] Application is correctly profiled ("Others" category represents 0.00 % 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 |
[ 3 / 3 ] Optimization level option is correctly used | [ 3 / 3 ] Optimization level option is correctly used |
[ 4 / 4 ] Application profile is long enough (546.11 s) To have good quality measurements, it is advised that the application profiling time is greater than 10 seconds. | [ 4 / 4 ] Application profile is long enough (17.08 s) To have good quality measurements, it is advised that the application profiling time is greater than 10 seconds. |
| baseline | locus440 |
|---|---|
[ 4 / 4 ] Loop profile is not flat At least one loop coverage is greater than 4% (41.29%), representing an hotspot for the application | [ 4 / 4 ] Loop profile is not flat At least one loop coverage is greater than 4% (16.15%), representing an hotspot for the application |
[ 2 / 2 ] Less than 10% (0.00%) is spend in BLAS2 operations BLAS2 calls usually could make a poor cache usage and could benefit from inlining. | [ 2 / 2 ] Less than 10% (0.00%) is spend in BLAS2 operations BLAS2 calls usually could make a poor cache usage and could benefit from inlining. |
[ 4 / 4 ] Enough time of the experiment time spent in analyzed innermost loops (41.29%) 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. | [ 4 / 4 ] Enough time of the experiment time spent in analyzed innermost loops (16.15%) 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. |
[ 3 / 3 ] Less than 10% (0.00%) is spend in BLAS1 operations It could be more efficient to inline by hand BLAS1 operations | [ 3 / 3 ] Less than 10% (0.00%) is spend in BLAS1 operations It could be more efficient to inline by hand BLAS1 operations |
[ 2 / 2 ] Less than 10% (0.00%) is spend in Libm/SVML (special functions) | [ 2 / 2 ] Less than 10% (0.00%) is spend in Libm/SVML (special functions) |
[ 4 / 4 ] Enough time of the experiment time spent in analyzed loops (41.36%) If the time spent in analyzed loops is less than 30%, standard loop optimizations will have a limited impact on application performances. | [ 0 / 4 ] Too little time of the experiment time spent in analyzed loops (17.33%) If the time spent in analyzed loops is less than 30%, standard loop optimizations will have a limited impact on application performances. |
[ 3 / 3 ] Cumulative Outermost/In between loops coverage (0.07%) lower than cumulative innermost loop coverage (41.29%) Having cumulative Outermost/In between loops coverage greater than cumulative innermost loop coverage will make loop optimization more complex | [ 3 / 3 ] Cumulative Outermost/In between loops coverage (1.18%) lower than cumulative innermost loop coverage (16.15%) Having cumulative Outermost/In between loops coverage greater than cumulative innermost loop coverage will make loop optimization more complex |
| Analysis | r_1 | r_2 | |
|---|---|---|---|
| Loop Computation Issues | Presence of a large number of scalar integer instructions | 1 | 0 |
| Control Flow Issues | Presence of 2 to 4 paths | 1 | 0 |
| Presence of more than 4 paths | 1 | 3 | |
| Non-innermost loop | 2 | 3 | |
| Data Access Issues | Presence of constant non-unit stride data access | 1 | 1 |
| Presence of expensive instructions: scatter/gather | 2 | 0 | |
| Presence of special instructions executing on a single port | 2 | 0 | |
| More than 20% of the loads are accessing the stack | 2 | 3 | |
| Vectorization Roadblocks | Presence of 2 to 4 paths | 1 | 0 |
| Presence of more than 4 paths | 1 | 3 | |
| Non-innermost loop | 2 | 3 | |
| Presence of constant non-unit stride data access | 1 | 1 | |
| Inefficient Vectorization | Presence of expensive instructions: scatter/gather | 2 | 0 |
| Presence of special instructions executing on a single port | 2 | 0 | |
| Use of masked instructions | 1 | 0 | |