OV - - Loops

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Loops Index

9 loops have been discarded from the report because their ratio ((Max Inclusive Time Over Threads * 100) / Max Thread Active Time) is lower than the threshold set by object_coverage_threshold (0.01%). It represents about 0.04% of the application. To include them, change the value of object_coverage_threshold in the experiment directory configuration file, then rerun the command with the additionnal parameter --force-static-analysis

▶Scalability Runs Description

Run run_0	Number processes: 1Number nodes: NARun Command: <executable> MPI Command: mpirun -np <number_processes>Dataset: Run Directory: /home/kcamus/POP/POP3/XY_model/XY-model/
Run m2	Number processes: 2Run Command: <executable> MPI Command: mpirun -np <number_processes>Dataset: Run Directory: /home/kcamus/POP/POP3/XY_model/XY-model/
Run m4	Number processes: 4Run Command: <executable> MPI Command: mpirun -np <number_processes>Dataset: Run Directory: /home/kcamus/POP/POP3/XY_model/XY-model/
Run m8	Number processes: 8Run Command: <executable> MPI Command: mpirun -np <number_processes>Dataset: Run Directory: /home/kcamus/POP/POP3/XY_model/XY-model/
Run m16	Number processes: 16Run Command: <executable> MPI Command: mpirun -np <number_processes>Dataset: Run Directory: /home/kcamus/POP/POP3/XY_model/XY-model/
Run m32	Number processes: 32Run Command: <executable> MPI Command: mpirun -np <number_processes>Dataset: Run Directory: /home/kcamus/POP/POP3/XY_model/XY-model/

▶Filters

Loop id	Source Location	Source Function	Level	Max Thread Time / Walltime run_0 (%)	Max Thread Time / Walltime m2 (%)	Max Thread Time / Walltime m4 (%)	Max Thread Time / Walltime m8 (%)	Max Thread Time / Walltime m16 (%)	Max Thread Time / Walltime m32 (%)	Exclusive Coverage run_0 (%)	Exclusive Coverage m2 (%)	Exclusive Coverage m4 (%)	Exclusive Coverage m8 (%)	Exclusive Coverage m16 (%)	Exclusive Coverage m32 (%)	Inclusive Coverage run_0 (%)	Inclusive Coverage m2 (%)	Inclusive Coverage m4 (%)	Inclusive Coverage m8 (%)	Inclusive Coverage m16 (%)	Inclusive Coverage m32 (%)	Max Exclusive Time Over Threads run_0 (s)	Max Exclusive Time Over Threads m2 (s)	Max Exclusive Time Over Threads m4 (s)	Max Exclusive Time Over Threads m8 (s)	Max Exclusive Time Over Threads m16 (s)	Max Exclusive Time Over Threads m32 (s)	Max Inclusive Time Over Threads run_0 (s)	Max Inclusive Time Over Threads m2 (s)	Max Inclusive Time Over Threads m4 (s)	Max Inclusive Time Over Threads m8 (s)	Max Inclusive Time Over Threads m16 (s)	Max Inclusive Time Over Threads m32 (s)	Exclusive Time w.r.t. Wall Time run_0 (s)	Exclusive Time w.r.t. Wall Time m2 (s)	Exclusive Time w.r.t. Wall Time m4 (s)	Exclusive Time w.r.t. Wall Time m8 (s)	Exclusive Time w.r.t. Wall Time m16 (s)	Exclusive Time w.r.t. Wall Time m32 (s)	Inclusive Time w.r.t. Wall Time run_0 (s)	Inclusive Time w.r.t. Wall Time m2 (s)	Inclusive Time w.r.t. Wall Time m4 (s)	Inclusive Time w.r.t. Wall Time m8 (s)	Inclusive Time w.r.t. Wall Time m16 (s)	Inclusive Time w.r.t. Wall Time m32 (s)	Nb Threads run_0	Nb Threads m2	Nb Threads m4	Nb Threads m8	Nb Threads m16	Nb Threads m32	Vectorization Ratio (%)	Vector Length Use (%)	Speedup If No Scalar Integer	Speedup If FP Vectorized	Speedup If Fully Vectorized	Speedup If Perfect Load Balancing run_0	Speedup If Perfect Load Balancing m2	Speedup If Perfect Load Balancing m4	Speedup If Perfect Load Balancing m8	Speedup If Perfect Load Balancing m16	Speedup If Perfect Load Balancing m32	Stride 0	Stride 1	Stride n	Stride Unknown	Stride Indirect	Array Access Efficiency	(run_0) Efficiency	(run_0) Potential Speed-Up (%)	(m2) Efficiency	(m2) Potential Speed-Up (%)	(m4) Efficiency	(m4) Potential Speed-Up (%)	(m8) Efficiency	(m8) Potential Speed-Up (%)	(m16) Efficiency	(m16) Potential Speed-Up (%)	(m32) Efficiency	(m32) Potential Speed-Up (%)
18	xy_model - simulation.cpp:27-56 [...]	metropolisHalfSweep(Lattice&, int, int, double, int, int, int)	InBetween	13.99	13.81	13.93	14.21	14.24	13.72	14.01	13.82	13.85	13.90	13.66	13.15	35.15	34.41	34.69	34.56	34.00	32.77	40.94	20.29	10.25	5.31	2.70	1.39	102.67	50.55	25.60	13.02	6.43	3.45	40.94	20.25	10.14	5.14	2.54	1.27	102.67	50.43	25.39	12.77	6.31	3.16	1	2	4	8	16	32	8.87	13.81	1.66	1.74	9.81	1	1	1.01	1.03	1.07	1.09	NA	NA	NA	NA	NA	0.00	1	0	1.01	0	1.01	0	1	0.05	1.01	0	1.01	0
10	xy_model - simulation.cpp:46-46	metropolisHalfSweep(Lattice&, int, int, double, int, int, int)	Innermost	4.72	4.70	4.77	4.67	4.79	4.89	4.72	4.68	4.52	4.41	4.25	4.12	4.72	4.68	4.52	4.41	4.25	4.12	13.79	6.91	3.51	1.75	0.91	0.50	13.79	6.91	3.51	1.75	0.91	0.50	13.80	6.85	3.31	1.63	0.79	0.40	13.80	6.85	3.31	1.63	0.79	0.40	1	2	4	8	16	32	NA	NA	NA	NA	NA	1	1.01	1.06	1.07	1.15	1.25	NA	NA	NA	NA	NA	0.00	1	0	1.01	0	1.04	0	1.06	0	1.09	0	1.08	0
9	xy_model - random.tcc:458-3558 [...]	double std::__generate_canonical_pow2<double, 53ul, std::mersenne_twister_engine<unsigned long, 32ul, 624ul, 397ul, 31ul, 2567483615ul, 11ul, 4294967295ul, 7ul, 2636928640ul, 15ul, 4022730752ul, 18ul, 1812433253ul> >(std::mersenne_twister_engin...	Single	3.92	3.83	4.00	4.21	4.61	4.49	3.92	3.82	3.77	3.80	3.85	3.76	3.92	3.82	3.77	3.80	3.85	3.76	11.46	5.63	2.95	1.57	0.88	0.45	11.46	5.63	2.95	1.57	0.88	0.45	11.46	5.59	2.76	1.41	0.72	0.36	11.46	5.59	2.76	1.41	0.72	0.36	1	2	4	8	16	32	2.38	11.9	5.02	1	11.83	1	1.01	1.07	1.12	1.22	1.25	1.5	0	0	0.5	0	87.50	1	0	1.02	0	1.04	0	1.02	0	1	0	0.99	0.06
14	xy_model - simulation.cpp:46-46	metropolisHalfSweep(Lattice&, int, int, double, int, int, int)	Innermost	2.84	2.90	2.81	2.97	3.08	3.75	2.85	2.78	2.63	2.66	2.57	2.60	2.85	2.78	2.63	2.66	2.57	2.60	8.32	4.26	2.07	1.11	0.58	0.38	8.32	4.26	2.07	1.11	0.58	0.38	8.32	4.08	1.92	0.98	0.48	0.25	8.32	4.08	1.92	0.98	0.48	0.25	1	2	4	8	16	32	NA	NA	NA	NA	NA	1	1.04	1.08	1.13	1.23	1.51	NA	NA	NA	NA	NA	0.00	1	0	1.02	0	1.08	0	1.06	0	1.09	0	1.04	0
16	xy_model - simulation.cpp:46-46	metropolisHalfSweep(Lattice&, int, int, double, int, int, int)	Innermost	2.84	2.66	2.81	3.00	3.00	3.31	2.84	2.67	2.74	2.78	2.63	2.65	2.84	2.67	2.74	2.78	2.63	2.65	8.31	3.91	2.06	1.12	0.57	0.34	8.31	3.91	2.06	1.12	0.57	0.34	8.31	3.92	2.01	1.03	0.49	0.26	8.31	3.92	2.01	1.03	0.49	0.26	1	2	4	8	16	32	NA	NA	NA	NA	NA	1	1	1.03	1.09	1.17	1.31	NA	NA	NA	NA	NA	0.00	1	0	1.06	0	1.04	0	1.01	0	1.06	0	1.02	0
12	xy_model - simulation.cpp:46-46	metropolisHalfSweep(Lattice&, int, int, double, int, int, int)	Innermost	2.80	2.66	2.87	3.01	3.19	3.11	2.80	2.67	2.82	2.75	2.75	2.64	2.80	2.67	2.82	2.75	2.75	2.64	8.19	3.91	2.11	1.12	0.60	0.31	8.19	3.91	2.11	1.12	0.60	0.31	8.19	3.92	2.06	1.02	0.51	0.25	8.19	3.92	2.06	1.02	0.51	0.25	1	2	4	8	16	32	NA	NA	NA	NA	NA	1	1	1.02	1.11	1.18	1.24	NA	NA	NA	NA	NA	0.00	1	0	1.05	0	0.99	0.02	1.01	0	1	0	1.01	0
15	xy_model - simulation.cpp:47-47	metropolisHalfSweep(Lattice&, int, int, double, int, int, int)	Innermost	2.18	2.23	2.31	2.25	2.58	2.66	2.19	2.12	2.22	2.16	2.16	2.05	2.19	2.12	2.22	2.16	2.16	2.05	6.39	3.27	1.70	0.84	0.49	0.27	6.39	3.27	1.70	0.84	0.49	0.27	6.39	3.10	1.63	0.80	0.40	0.20	6.39	3.10	1.63	0.80	0.40	0.20	1	2	4	8	16	32	NA	NA	NA	NA	NA	1	1.05	1.05	1.05	1.22	1.36	NA	NA	NA	NA	NA	0.00	1	0	1.03	0	0.98	0.04	1	-0	0.99	0.01	1.01	0
17	xy_model - simulation.cpp:47-47	metropolisHalfSweep(Lattice&, int, int, double, int, int, int)	Innermost	1.92	1.90	2.03	2.19	2.26	2.42	1.92	1.85	2.01	1.96	1.97	1.87	1.92	1.85	2.01	1.96	1.97	1.87	5.61	2.79	1.50	0.82	0.43	0.24	5.61	2.79	1.50	0.82	0.43	0.24	5.61	2.71	1.47	0.73	0.37	0.18	5.61	2.71	1.47	0.73	0.37	0.18	1	2	4	8	16	32	NA	NA	NA	NA	NA	1	1.03	1.02	1.13	1.18	1.36	NA	NA	NA	NA	NA	0.00	1	0	1.04	0	0.96	0.09	0.97	0.06	0.96	0.08	0.97	0.05
11	xy_model - simulation.cpp:47-47	metropolisHalfSweep(Lattice&, int, int, double, int, int, int)	Innermost	1.92	1.98	2.13	2.13	2.79	3.01	1.92	1.98	1.96	1.99	2.05	1.99	1.92	1.98	1.96	1.99	2.05	1.99	5.61	2.92	1.56	0.79	0.53	0.31	5.61	2.92	1.56	0.79	0.53	0.31	5.61	2.91	1.44	0.74	0.38	0.19	5.61	2.91	1.44	0.74	0.38	0.19	1	2	4	8	16	32	NA	NA	NA	NA	NA	1	1	1.09	1.08	1.4	1.59	NA	NA	NA	NA	NA	0.00	1	0	0.96	0.07	0.98	0.05	0.95	0.1	0.92	0.16	0.91	0.18
13	xy_model - simulation.cpp:47-47	metropolisHalfSweep(Lattice&, int, int, double, int, int, int)	Innermost	1.89	1.85	1.96	2.10	2.34	2.02	1.89	1.84	1.94	1.94	1.95	1.70	1.89	1.84	1.94	1.94	1.95	1.70	5.52	2.72	1.44	0.79	0.45	0.21	5.52	2.72	1.44	0.79	0.45	0.21	5.52	2.69	1.42	0.72	0.36	0.16	5.52	2.69	1.42	0.72	0.36	0.16	1	2	4	8	16	32	NA	NA	NA	NA	NA	1	1.01	1.02	1.09	1.23	1.25	NA	NA	NA	NA	NA	0.00	1	0	1.02	0	0.97	0.06	0.96	0.08	0.95	0.09	1.05	0
23	xy_model - random.tcc:412-417	std::mersenne_twister_engine<unsigned long, 32ul, 624ul, 397ul, 31ul, 2567483615ul, 11ul, 4294967295ul, 7ul, 2636928640ul, 15ul, 4022730752ul, 18ul, 1812433253ul>::_M_gen_rand()	Single	0.30	0.38	0.38	0.33	0.50	0.59	0.30	0.35	0.35	0.28	0.30	0.33	0.30	0.35	0.35	0.28	0.30	0.33	0.89	0.56	0.28	0.13	0.09	0.06	0.89	0.56	0.28	0.13	0.09	0.06	0.89	0.51	0.26	0.11	0.06	0.03	0.89	0.51	0.26	0.11	0.06	0.03	1	2	4	8	16	32	100	50	1	1	2	1	1.09	1.09	1.19	1.68	1.9	0	0	1	0	0	75.00	1	0	0.87	0.04	0.86	0.05	1.06	0	0.98	0.01	0.88	0.04
22	xy_model - random.tcc:404-409	std::mersenne_twister_engine<unsigned long, 32ul, 624ul, 397ul, 31ul, 2567483615ul, 11ul, 4294967295ul, 7ul, 2636928640ul, 15ul, 4022730752ul, 18ul, 1812433253ul>::_M_gen_rand()	Single	0.17	0.21	0.21	0.21	0.32	0.39	0.17	0.20	0.18	0.18	0.23	0.18	0.17	0.20	0.18	0.18	0.23	0.18	0.50	0.31	0.16	0.08	0.06	0.04	0.50	0.31	0.16	0.08	0.06	0.04	0.49	0.30	0.13	0.07	0.04	0.02	0.49	0.30	0.13	0.07	0.04	0.02	1	2	4	8	16	30	100	50	1	1	2	1	1.03	1.17	1.21	1.42	2.14	0	0	1	0	0	75.00	1	0	0.82	0.04	0.93	0.01	0.93	0.01	0.73	0.06	0.88	0.02
33	xy_model -	exchangeHalo(Lattice&, int)	Single	0.00	0.00	0.02	0.03	0.03	0.05	0.00	0.00	0.01	0.01	0.01	0.00	0.00	0.00	0.01	0.01	0.01	0.00	0.00	0.00	0.02	0.01	0.01	0.01	0.00	0.00	0.02	0.01	0.01	0.01	0.00	0.00	0.01	0.00	0.00	0.00	0.00	0.00	0.01	0.00	0.00	0.00	0	0	2	5	4	3	0	12.5	1	1	8	0	0	1.2	1.67	1	1	0	2	0	8	0	60.00					1	0	1	0	1	0	1	0
19	xy_model - simulation.cpp:26-56 [...]	metropolisHalfSweep(Lattice&, int, int, double, int, int, int)	Outermost	0.00	0.00	0.02	0.04	0.05	0.10	0.00	0.00	0.01	0.02	0.02	0.02	0.00	0.00	34.70	34.57	34.02	32.79	0.00	0.00	0.02	0.01	0.01	0.01	0.00	0.00	25.51	13.03	6.41	3.31	0.00	0.00	0.01	0.01	0.00	0.00	0.00	0.00	25.40	12.77	6.31	3.17	0	0	2	6	8	11	0	7.5	1	1	13	0	0	1	2	1.6	1.69	NA	NA	NA	NA	NA	0.00					1	0	1	0	1	0	1	0

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