OV - - Loops

MAQAO

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Loops Index

8 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 0.06% 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 m1	Number processes: 1Number nodes: NARun Command: <executable> MPI Command: mpirun -np <number_processes>Dataset: Run Directory: /users/invite/vlnscdrc/kevin/XY-model
Run m2	Number processes: 2Run Command: <executable> MPI Command: mpirun -np <number_processes>Dataset: Run Directory: /users/invite/vlnscdrc/kevin/XY-model
Run m4	Number processes: 4Run Command: <executable> MPI Command: mpirun -np <number_processes>Dataset: Run Directory: /users/invite/vlnscdrc/kevin/XY-model
Run m8	Number processes: 8Run Command: <executable> MPI Command: mpirun -np <number_processes>Dataset: Run Directory: /users/invite/vlnscdrc/kevin/XY-model
Run m16	Number processes: 16Run Command: <executable> MPI Command: mpirun -np <number_processes>Dataset: Run Directory: /users/invite/vlnscdrc/kevin/XY-model
Run m32	Number processes: 32Run Command: <executable> MPI Command: mpirun -np <number_processes>Dataset: Run Directory: /users/invite/vlnscdrc/kevin/XY-model
Run m64	Number processes: 64Run Command: <executable> MPI Command: mpirun -np <number_processes>Dataset: Run Directory: /users/invite/vlnscdrc/kevin/XY-model
Run m128	Number processes: 128Run Command: <executable> MPI Command: mpirun -np <number_processes>Dataset: Run Directory: /users/invite/vlnscdrc/kevin/XY-model
Run m192	Number processes: 192Run Command: <executable> MPI Command: mpirun -np <number_processes>Dataset: Run Directory: /users/invite/vlnscdrc/kevin/XY-model

▶Filters

Loop id	Source Location	Source Function	Level	Max Thread Time / Walltime m1 (%)	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 (%)	Max Thread Time / Walltime m64 (%)	Max Thread Time / Walltime m128 (%)	Max Thread Time / Walltime m192 (%)	Exclusive Coverage m1 (%)	Exclusive Coverage m2 (%)	Exclusive Coverage m4 (%)	Exclusive Coverage m8 (%)	Exclusive Coverage m16 (%)	Exclusive Coverage m32 (%)	Exclusive Coverage m64 (%)	Exclusive Coverage m128 (%)	Exclusive Coverage m192 (%)	Inclusive Coverage m1 (%)	Inclusive Coverage m2 (%)	Inclusive Coverage m4 (%)	Inclusive Coverage m8 (%)	Inclusive Coverage m16 (%)	Inclusive Coverage m32 (%)	Inclusive Coverage m64 (%)	Inclusive Coverage m128 (%)	Inclusive Coverage m192 (%)	Max Exclusive Time Over Threads m1 (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 Exclusive Time Over Threads m64 (s)	Max Exclusive Time Over Threads m128 (s)	Max Exclusive Time Over Threads m192 (s)	Max Inclusive Time Over Threads m1 (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)	Max Inclusive Time Over Threads m64 (s)	Max Inclusive Time Over Threads m128 (s)	Max Inclusive Time Over Threads m192 (s)	Exclusive Time w.r.t. Wall Time m1 (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)	Exclusive Time w.r.t. Wall Time m64 (s)	Exclusive Time w.r.t. Wall Time m128 (s)	Exclusive Time w.r.t. Wall Time m192 (s)	Inclusive Time w.r.t. Wall Time m1 (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)	Inclusive Time w.r.t. Wall Time m64 (s)	Inclusive Time w.r.t. Wall Time m128 (s)	Inclusive Time w.r.t. Wall Time m192 (s)	Nb Threads m1	Nb Threads m2	Nb Threads m4	Nb Threads m8	Nb Threads m16	Nb Threads m32	Nb Threads m64	Nb Threads m128	Nb Threads m192	Vectorization Ratio (%)	Vector Length Use (%)	Speedup If No Scalar Integer	Speedup If FP Vectorized	Speedup If Fully Vectorized	Speedup If Perfect Load Balancing m1	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	Speedup If Perfect Load Balancing m64	Speedup If Perfect Load Balancing m128	Speedup If Perfect Load Balancing m192	Stride 0	Stride 1	Stride n	Stride Unknown	Stride Indirect	Array Access Efficiency	(m1) Efficiency	(m1) 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 (%)	(m64) Efficiency	(m64) Potential Speed-Up (%)	(m128) Efficiency	(m128) Potential Speed-Up (%)	(m192) Efficiency	(m192) Potential Speed-Up (%)
17	xy_model - simulation.cpp:27-56 [...]	metropolisHalfSweep(Lattice&, int, int, double, int, int, int)	InBetween	16.10	15.71	15.77	15.06	14.06	13.08	8.06	2.76	1.49	16.14	15.75	15.74	14.64	13.84	12.70	9.85	4.76	2.61	43.43	43.08	42.64	40.58	38.11	34.61	26.62	13.19	7.07	24.25	11.98	6.15	3.08	1.60	1.08	0.60	0.30	0.24	65.25	32.73	16.49	8.30	4.29	2.71	1.42	0.77	0.55	24.25	11.94	6.07	2.91	1.49	0.93	0.51	0.28	0.19	65.25	32.68	16.43	8.06	4.11	2.52	1.38	0.77	0.50	1	2	4	8	16	32	64	128	192	11.05	13.55	2.24	2.26	10.92	1	1	1.01	1.05	1.08	1.18	1.27	1.34	1.57	NA	NA	NA	NA	NA	0.00	1	0	1.02	0	1	0.01	0.64	5.25	1.02	0	0.82	2.3	0.74	2.55	0.68	1.53	0.68	0.84
10	xy_model - simulation.cpp:47-47	metropolisHalfSweep(Lattice&, int, int, double, int, int, int)	Innermost	4.59	4.63	4.62	4.65	4.75	3.83	2.57	1.18	0.58	4.61	4.61	4.51	4.26	4.01	3.55	2.82	1.47	0.76	4.61	4.61	4.51	4.26	4.01	3.55	2.82	1.47	0.76	6.92	3.53	1.80	0.95	0.54	0.31	0.19	0.13	0.09	6.92	3.53	1.80	0.95	0.54	0.31	0.19	0.13	0.09	6.92	3.50	1.74	0.85	0.43	0.26	0.15	0.09	0.05	6.92	3.50	1.74	0.85	0.43	0.26	0.15	0.09	0.05	1	2	4	8	16	32	64	128	192	NA	NA	1	NA	NA	1	1.01	1.04	1.12	1.26	1.24	1.41	1.84	2.08	NA	NA	NA	NA	NA	0.00	1	0	0.99	0.05	1	0.02	0.63	1.58	1	-0	0.84	0.58	0.74	0.74	0.63	0.55	0.66	0.26
14	xy_model - simulation.cpp:47-47	metropolisHalfSweep(Lattice&, int, int, double, int, int, int)	Innermost	4.19	4.36	4.26	4.38	4.39	3.71	2.57	1.00	0.52	4.20	4.32	4.00	4.01	3.70	3.42	2.49	1.24	0.67	4.20	4.32	4.00	4.01	3.70	3.42	2.49	1.24	0.67	6.31	3.33	1.66	0.89	0.50	0.30	0.19	0.11	0.08	6.31	3.33	1.66	0.89	0.50	0.30	0.19	0.11	0.08	6.31	3.28	1.54	0.80	0.40	0.25	0.13	0.07	0.05	6.31	3.28	1.54	0.80	0.40	0.25	0.13	0.07	0.05	1	2	4	8	16	32	64	128	192	NA	NA	1	NA	NA	1	1.01	1.08	1.12	1.26	1.24	1.61	1.85	2.12	NA	NA	NA	NA	NA	0.00	1	0	0.96	0.16	1.02	0	0.61	1.57	0.99	0.04	0.79	0.72	0.76	0.59	0.68	0.4	0.69	0.21
12	xy_model - simulation.cpp:47-47	metropolisHalfSweep(Lattice&, int, int, double, int, int, int)	Innermost	3.96	4.09	4.17	4.01	4.26	3.89	2.23	1.00	0.48	3.97	3.98	4.03	3.78	3.72	3.31	2.47	1.26	0.65	3.97	3.98	4.03	3.78	3.72	3.31	2.47	1.26	0.65	5.96	3.12	1.63	0.82	0.49	0.32	0.17	0.11	0.08	5.96	3.12	1.63	0.82	0.49	0.32	0.17	0.11	0.08	5.96	3.02	1.55	0.75	0.40	0.24	0.13	0.07	0.05	5.96	3.02	1.55	0.75	0.40	0.24	0.13	0.07	0.05	1	2	4	8	16	32	64	128	192	NA	NA	1	NA	NA	1	1.03	1.05	1.09	1.22	1.35	1.4	1.83	2.06	NA	NA	NA	NA	NA	0.00	1	0	0.99	0.04	0.96	0.16	0.61	1.47	0.93	0.26	0.77	0.75	0.73	0.68	0.63	0.46	0.67	0.21
9	xy_model - simulation.cpp:46-46	metropolisHalfSweep(Lattice&, int, int, double, int, int, int)	Innermost	3.66	3.58	3.60	3.69	4.13	3.41	2.03	0.86	0.52	3.67	3.54	3.48	3.43	3.28	2.85	2.17	1.18	0.62	3.67	3.54	3.48	3.43	3.28	2.85	2.17	1.18	0.62	5.51	2.73	1.40	0.75	0.47	0.28	0.15	0.09	0.08	5.51	2.73	1.40	0.75	0.47	0.28	0.15	0.09	0.08	5.52	2.69	1.34	0.68	0.35	0.21	0.11	0.07	0.04	5.52	2.69	1.34	0.68	0.35	0.21	0.11	0.07	0.04	1	2	4	8	16	32	64	128	192	NA	NA	1	NA	NA	1	1.02	1.05	1.1	1.34	1.36	1.45	1.68	2.31	NA	NA	NA	NA	NA	0.00	1	0	1.03	0	1.03	0	0.62	1.29	0.97	0.08	0.83	0.49	0.76	0.51	0.62	0.45	0.65	0.21
15	xy_model - simulation.cpp:46-46	metropolisHalfSweep(Lattice&, int, int, double, int, int, int)	Innermost	2.91	2.99	2.94	2.98	3.03	2.55	2.17	0.77	0.36	2.92	2.92	2.83	2.75	2.54	2.38	1.93	0.90	0.46	2.92	2.92	2.83	2.75	2.54	2.38	1.93	0.90	0.46	4.39	2.28	1.14	0.61	0.34	0.21	0.16	0.09	0.06	4.39	2.28	1.14	0.61	0.34	0.21	0.16	0.09	0.06	4.39	2.22	1.09	0.55	0.27	0.17	0.10	0.05	0.03	4.39	2.22	1.09	0.55	0.27	0.17	0.10	0.05	0.03	1	2	4	8	16	32	64	128	192	NA	NA	1	NA	NA	1	1.03	1.05	1.11	1.27	1.23	1.74	1.98	2.19	NA	NA	NA	NA	NA	0.00	1	0	0.99	0.03	1.01	0	0.62	1.05	1	0	0.79	0.5	0.68	0.61	0.65	0.31	0.7	0.14
11	xy_model - simulation.cpp:46-46	metropolisHalfSweep(Lattice&, int, int, double, int, int, int)	Innermost	2.82	2.81	2.96	2.84	2.81	2.55	1.69	0.81	0.36	2.83	2.70	2.84	2.66	2.56	2.35	1.74	0.83	0.48	2.83	2.70	2.84	2.66	2.56	2.35	1.74	0.83	0.48	4.25	2.14	1.16	0.58	0.32	0.21	0.13	0.09	0.06	4.25	2.14	1.16	0.58	0.32	0.21	0.13	0.09	0.06	4.25	2.05	1.10	0.53	0.28	0.17	0.09	0.05	0.03	4.25	2.05	1.10	0.53	0.28	0.17	0.09	0.05	0.03	1	2	4	8	16	32	64	128	191	NA	NA	1	NA	NA	1	1.04	1.05	1.09	1.17	1.24	1.51	2.27	2.08	NA	NA	NA	NA	NA	0.00	1	0	1.04	0	0.97	0.08	0.62	1.01	0.96	0.09	0.77	0.53	0.74	0.46	0.68	0.26	0.65	0.17
13	xy_model - simulation.cpp:46-46	metropolisHalfSweep(Lattice&, int, int, double, int, int, int)	Innermost	2.79	2.99	2.95	3.20	2.68	2.49	1.83	0.68	0.42	2.80	2.88	2.84	2.81	2.45	2.13	1.69	0.87	0.45	2.80	2.88	2.84	2.81	2.45	2.13	1.69	0.87	0.45	4.20	2.28	1.15	0.65	0.31	0.21	0.14	0.08	0.07	4.20	2.28	1.15	0.65	0.31	0.21	0.14	0.08	0.07	4.20	2.18	1.09	0.56	0.26	0.16	0.09	0.05	0.03	4.20	2.18	1.09	0.56	0.26	0.16	0.09	0.05	0.03	1	2	4	8	16	32	64	128	189	NA	NA	1	NA	NA	1	1.04	1.05	1.16	1.16	1.34	1.68	1.8	2.56	NA	NA	NA	NA	NA	0.00	1	0	0.96	0.11	0.96	0.11	0.58	1.19	1	0.01	0.84	0.33	0.75	0.42	0.64	0.31	0.68	0.14
16	xy_model - simulation.cpp:47-47	metropolisHalfSweep(Lattice&, int, int, double, int, int, int)	Innermost	2.29	2.41	2.55	2.69	2.24	2.19	1.56	0.72	0.30	2.30	2.38	2.37	2.24	2.02	1.91	1.46	0.69	0.38	2.30	2.38	2.37	2.24	2.02	1.91	1.46	0.69	0.38	3.45	1.84	0.99	0.55	0.25	0.18	0.11	0.08	0.05	3.45	1.84	0.99	0.55	0.25	0.18	0.11	0.08	0.05	3.45	1.80	0.91	0.44	0.22	0.14	0.08	0.04	0.03	3.45	1.80	0.91	0.44	0.22	0.14	0.08	0.04	0.03	1	2	4	8	16	32	64	128	188	NA	NA	1	NA	NA	1	1.02	1.09	1.23	1.18	1.31	1.66	2.42	2.17	NA	NA	NA	NA	NA	0.00	1	0	0.96	0.1	0.95	0.12	0.6	0.9	0.99	0.01	0.78	0.43	0.71	0.42	0.67	0.23	0.67	0.12
22	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	0.28	0.33	0.35	0.37	0.40	0.43	0.41	0.18	0.09	0.28	0.32	0.30	0.23	0.21	0.22	0.16	0.08	0.04	0.28	0.32	0.30	0.23	0.21	0.22	0.16	0.08	0.04	0.42	0.25	0.13	0.08	0.05	0.04	0.03	0.02	0.01	0.42	0.25	0.13	0.08	0.05	0.04	0.03	0.02	0.01	0.43	0.24	0.11	0.05	0.02	0.02	0.01	0.00	0.00	0.43	0.24	0.11	0.05	0.02	0.02	0.01	0.00	0.00	1	2	4	8	15	30	53	65	80	100	50	1	1	2	1	1.03	1.19	1.6	1.85	2.04	3.35	2.77	2.53	0	0	0	0	0	0.00	1	0	0.88	0.04	0.93	0.02	0.7	0.07	1.16	0	0.81	0.04	0.82	0.03	0.74	0.02	0.75	0.01
21	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	0.22	0.16	0.22	0.24	0.35	0.36	0.41	0.14	0.09	0.22	0.15	0.17	0.12	0.15	0.18	0.11	0.06	0.04	0.22	0.15	0.17	0.12	0.15	0.18	0.11	0.06	0.04	0.33	0.12	0.09	0.05	0.04	0.03	0.03	0.02	0.02	0.33	0.12	0.09	0.05	0.04	0.03	0.03	0.02	0.02	0.33	0.11	0.07	0.02	0.02	0.01	0.01	0.00	0.00	0.33	0.11	0.07	0.02	0.02	0.01	0.01	0.00	0.00	1	2	4	8	14	31	43	64	77	100	50	1	1	2	1	1.09	1.28	2.11	2.2	2.3	3.85	2.46	2.48	0	0	0	0	0	0.00	1	0	1.5	0	1.25	0	1.08	0	1.29	0	0.8	0.03	0.9	0.01	0.69	0.02	0.6	0.02
18	xy_model - simulation.cpp:26-56 [...]	metropolisHalfSweep(Lattice&, int, int, double, int, int, int)	Outermost	0.02	0.02	0.03	0.05	0.13	0.18	0.20	0.09	0.06	0.02	0.02	0.03	0.03	0.05	0.06	0.08	0.04	0.03	43.46	43.10	42.66	40.61	38.16	34.67	26.70	13.23	7.11	0.04	0.01	0.01	0.01	0.02	0.02	0.02	0.01	0.01	65.29	32.74	16.50	8.29	4.22	2.72	1.43	0.73	0.55	0.03	0.01	0.01	0.01	0.01	0.00	0.00	0.00	0.00	65.29	32.69	16.44	8.07	4.11	2.53	1.39	0.78	0.51	1	2	4	6	9	19	34	43	66	0	10	1	1	15	1	1.2	1	1.33	1.59	2.19	2.13	1.83	1.69	NA	NA	NA	NA	NA	0.00	1	0	1.4	0	0.87	0	0.48	0.01	0.41	0.03	0.27	0.04	0.13	0.07	0.12	0.03	0.08	0.03
29	xy_model -	exchangeHalo(Lattice&, ompi_communicator_t*)	Single	0.00	0.00	0.03	0.00	0.00	0.12	0.07	0.05	0.03	0.00	0.00	0.01	0.00	0.00	0.01	0.01	0.00	0.00	0.00	0.00	0.01	0.00	0.00	0.01	0.01	0.00	0.00	0.00	0.00	0.01	0.00	0.00	0.01	0.01	0.01	0.01	0.00	0.00	0.01	0.00	0.00	0.01	0.01	0.01	0.01	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0.00	0	0	2	0	0	3	7	2	3	0	12.5	1	1	8	0	0	1.33	0	0	1.5	1	1	1	0	2	0	8	0	60.00					1	0					1	0	1	0	1	0	1	0

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