OV - - Loops

MAQAO

options

Loops Index

6 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 0.03% 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 mpi=1	Number processes: 1Number nodes: NARun Command: <executable> MPI Command: /home/kcamus/openmpi-5.0.10/__install/bin/mpirun -np <number_processes>Dataset: Run Directory: .
Run mpi=2	Number processes: 2Run Command: <executable> MPI Command: /home/kcamus/openmpi-5.0.10/__install/bin/mpirun -np <number_processes>Dataset: Run Directory: .
Run mpi=4	Number processes: 4Run Command: <executable> MPI Command: /home/kcamus/openmpi-5.0.10/__install/bin/mpirun -np <number_processes>Dataset: Run Directory: .
Run mpi=8	Number processes: 8Run Command: <executable> MPI Command: /home/kcamus/openmpi-5.0.10/__install/bin/mpirun -np <number_processes>Dataset: Run Directory: .
Run mpi=16	Number processes: 16Run Command: <executable> MPI Command: /home/kcamus/openmpi-5.0.10/__install/bin/mpirun -np <number_processes>Dataset: Run Directory: .
Run mpi=32	Number processes: 32Run Command: <executable> MPI Command: /home/kcamus/openmpi-5.0.10/__install/bin/mpirun -np <number_processes>Dataset: Run Directory: .
Run mpi=64	Number processes: 64Run Command: <executable> MPI Command: /home/kcamus/openmpi-5.0.10/__install/bin/mpirun -np <number_processes>Dataset: Run Directory: .

▶Filters

Loop id	Source Location	Source Function	Level	Max Thread Time / Walltime mpi=1 (%)	Max Thread Time / Walltime mpi=2 (%)	Max Thread Time / Walltime mpi=4 (%)	Max Thread Time / Walltime mpi=8 (%)	Max Thread Time / Walltime mpi=16 (%)	Max Thread Time / Walltime mpi=32 (%)	Max Thread Time / Walltime mpi=64 (%)	Exclusive Coverage mpi=1 (%)	Exclusive Coverage mpi=2 (%)	Exclusive Coverage mpi=4 (%)	Exclusive Coverage mpi=8 (%)	Exclusive Coverage mpi=16 (%)	Exclusive Coverage mpi=32 (%)	Exclusive Coverage mpi=64 (%)	Inclusive Coverage mpi=1 (%)	Inclusive Coverage mpi=2 (%)	Inclusive Coverage mpi=4 (%)	Inclusive Coverage mpi=8 (%)	Inclusive Coverage mpi=16 (%)	Inclusive Coverage mpi=32 (%)	Inclusive Coverage mpi=64 (%)	Max Exclusive Time Over Threads mpi=1 (s)	Max Exclusive Time Over Threads mpi=2 (s)	Max Exclusive Time Over Threads mpi=4 (s)	Max Exclusive Time Over Threads mpi=8 (s)	Max Exclusive Time Over Threads mpi=16 (s)	Max Exclusive Time Over Threads mpi=32 (s)	Max Exclusive Time Over Threads mpi=64 (s)	Max Inclusive Time Over Threads mpi=1 (s)	Max Inclusive Time Over Threads mpi=2 (s)	Max Inclusive Time Over Threads mpi=4 (s)	Max Inclusive Time Over Threads mpi=8 (s)	Max Inclusive Time Over Threads mpi=16 (s)	Max Inclusive Time Over Threads mpi=32 (s)	Max Inclusive Time Over Threads mpi=64 (s)	Exclusive Time w.r.t. Wall Time mpi=1 (s)	Exclusive Time w.r.t. Wall Time mpi=2 (s)	Exclusive Time w.r.t. Wall Time mpi=4 (s)	Exclusive Time w.r.t. Wall Time mpi=8 (s)	Exclusive Time w.r.t. Wall Time mpi=16 (s)	Exclusive Time w.r.t. Wall Time mpi=32 (s)	Exclusive Time w.r.t. Wall Time mpi=64 (s)	Inclusive Time w.r.t. Wall Time mpi=1 (s)	Inclusive Time w.r.t. Wall Time mpi=2 (s)	Inclusive Time w.r.t. Wall Time mpi=4 (s)	Inclusive Time w.r.t. Wall Time mpi=8 (s)	Inclusive Time w.r.t. Wall Time mpi=16 (s)	Inclusive Time w.r.t. Wall Time mpi=32 (s)	Inclusive Time w.r.t. Wall Time mpi=64 (s)	Nb Threads mpi=1	Nb Threads mpi=2	Nb Threads mpi=4	Nb Threads mpi=8	Nb Threads mpi=16	Nb Threads mpi=32	Nb Threads mpi=64	Vectorization Ratio (%)	Vector Length Use (%)	Speedup If No Scalar Integer	Speedup If FP Vectorized	Speedup If Fully Vectorized	Speedup If Perfect Load Balancing mpi=1	Speedup If Perfect Load Balancing mpi=2	Speedup If Perfect Load Balancing mpi=4	Speedup If Perfect Load Balancing mpi=8	Speedup If Perfect Load Balancing mpi=16	Speedup If Perfect Load Balancing mpi=32	Speedup If Perfect Load Balancing mpi=64	Stride 0	Stride 1	Stride n	Stride Unknown	Stride Indirect	Array Access Efficiency	(mpi=1) Efficiency	(mpi=1) Potential Speed-Up (%)	(mpi=2) Efficiency	(mpi=2) Potential Speed-Up (%)	(mpi=4) Efficiency	(mpi=4) Potential Speed-Up (%)	(mpi=8) Efficiency	(mpi=8) Potential Speed-Up (%)	(mpi=16) Efficiency	(mpi=16) Potential Speed-Up (%)	(mpi=32) Efficiency	(mpi=32) Potential Speed-Up (%)	(mpi=64) Efficiency	(mpi=64) Potential Speed-Up (%)
2	xy_model - random.tcc:404-3374 [...]	metropolisHalfSweep(Lattice&, int, int, double, int, int, int)	InBetween	21.21	20.57	20.87	21.21	21.97	23.11	22.48	21.22	20.50	20.42	20.71	20.96	20.37	20.28	23.85	23.19	22.91	23.11	23.66	23.04	22.96	34.78	16.87	8.57	4.36	2.27	1.21	0.61	39.10	19.08	9.56	4.90	2.55	1.33	0.70	34.78	16.81	8.37	4.25	2.15	1.06	0.53	39.10	19.01	9.39	4.74	2.43	1.20	0.60	1	2	4	8	16	32	64	33.33	34.4	1.39	1.11	1.65	1	1	1.02	1.03	1.05	1.15	1.16	NA	NA	NA	NA	NA	0.00	1	0	1.03	0	1.04	0	1.02	0	1.01	0	1.03	0	1.03	0
1	xy_model - simulation.cpp:26-28	metropolisHalfSweep(Lattice&, int, int, double, int, int, int)	Innermost	1.53	1.54	1.57	1.70	1.94	2.09	2.95	1.53	1.53	1.48	1.47	1.52	1.52	1.64	1.53	1.53	1.48	1.47	1.52	1.52	1.64	2.50	1.26	0.64	0.35	0.20	0.11	0.08	2.50	1.26	0.64	0.35	0.20	0.11	0.08	2.50	1.25	0.61	0.30	0.16	0.08	0.04	2.50	1.25	0.61	0.30	0.16	0.08	0.04	1	2	4	8	16	32	64	0	23.96	1	1	6.08	1	1.01	1.06	1.16	1.28	1.39	1.89	NA	NA	NA	NA	NA	0.00	1	0	1	0	1.03	0	1.04	0	1	0	0.99	0.01	0.92	0.13
5	xy_model - random.tcc:412-417	metropolisHalfSweep(Lattice&, int, int, double, int, int, int)	Innermost	0.62	0.74	0.74	0.85	0.87	1.33	1.47	0.62	0.68	0.69	0.63	0.65	0.65	0.73	0.62	0.68	0.69	0.63	0.65	0.65	0.73	1.01	0.61	0.31	0.17	0.09	0.07	0.04	1.01	0.61	0.31	0.17	0.09	0.07	0.04	1.01	0.56	0.28	0.13	0.07	0.03	0.02	1.01	0.56	0.28	0.13	0.07	0.03	0.02	1	2	4	8	16	30	61	94.12	95.59	1	1	1	1	1.09	1.08	1.35	1.35	1.94	2.02	0	0	0	2	0	50.00	1	0	0.91	0.06	0.9	0.07	0.98	0.01	0.95	0.03	0.94	0.04	0.84	0.12
8	xy_model - random.tcc:404-3368 [...]	metropolisHalfSweep(Lattice&, int, int, double, int, int, int)	InBetween	0.46	0.52	0.49	0.53	0.92	0.95	0.92	0.46	0.48	0.43	0.38	0.51	0.48	0.43	1.11	1.16	1.01	0.93	1.17	1.15	1.04	0.75	0.43	0.20	0.11	0.09	0.05	0.03	1.81	0.98	0.45	0.24	0.16	0.10	0.06	0.76	0.39	0.18	0.08	0.05	0.03	0.01	1.81	0.95	0.41	0.19	0.12	0.06	0.03	1	2	4	8	16	32	56	0	26.52	1.97	1.22	2.39	1	1.1	1.13	1.41	1.81	2	1.97	NA	NA	NA	NA	NA	50.00	1	0	0.96	0.02	1.07	0	1.21	0	0.9	0.05	0.94	0.03	1.06	0
11	xy_model - random.tcc:412-417	metropolisHalfSweep(Lattice&, int, int, double, int, int, int)	Innermost	0.44	0.46	0.51	0.53	0.68	1.05	1.29	0.44	0.45	0.41	0.39	0.47	0.47	0.43	0.44	0.45	0.41	0.39	0.47	0.47	0.43	0.71	0.38	0.21	0.11	0.07	0.05	0.04	0.71	0.38	0.21	0.11	0.07	0.05	0.04	0.71	0.37	0.17	0.08	0.05	0.02	0.01	0.71	0.37	0.17	0.08	0.05	0.02	0.01	1	2	4	8	16	32	57	94.12	95.59	1	1	1	1	1.01	1.26	1.39	1.45	2.27	2.81	0	0	0	2	0	50.00	1	0	0.97	0.02	1.08	0	1.13	0	0.93	0.03	0.92	0.04	1	0
3	xy_model - random.tcc:404-409	metropolisHalfSweep(Lattice&, int, int, double, int, int, int)	Innermost	0.30	0.28	0.38	0.41	0.39	0.67	1.11	0.30	0.23	0.33	0.28	0.24	0.30	0.33	0.30	0.23	0.33	0.28	0.24	0.30	0.33	0.49	0.23	0.16	0.08	0.04	0.04	0.03	0.49	0.23	0.16	0.08	0.04	0.04	0.03	0.50	0.19	0.14	0.06	0.03	0.02	0.01	0.50	0.19	0.14	0.06	0.03	0.02	0.01	1	2	4	8	16	31	55	93.75	95.31	1	1	1	1	1.19	1.14	1.48	1.6	2.15	3.03	0	0	0	2	0	50.00	1	0	1.29	0	0.91	0.03	1.08	0	1.24	0	0.98	0.01	0.91	0.03
9	xy_model - random.tcc:404-409	metropolisHalfSweep(Lattice&, int, int, double, int, int, int)	Innermost	0.21	0.24	0.21	0.27	0.34	0.57	0.92	0.21	0.23	0.17	0.15	0.19	0.20	0.17	0.21	0.23	0.17	0.15	0.19	0.20	0.17	0.34	0.20	0.09	0.06	0.04	0.03	0.03	0.34	0.20	0.09	0.06	0.04	0.03	0.03	0.35	0.19	0.07	0.03	0.02	0.01	0.00	0.35	0.19	0.07	0.03	0.02	0.01	0.00	1	2	4	8	16	24	34	93.75	95.31	1	1	1	1	1.08	1.24	1.83	1.81	2.18	2.93	0	0	0	2	0	50.00	1	0	0.93	0.02	1.25	0	1.44	0	1.11	0	1.04	0	1.19	0
4	xy_model - random.tcc:404-409	metropolisHalfSweep(Lattice&, int, int, double, int, int, int)	Innermost	0.03	0.03	0.07	0.05	0.19	0.29	0.37	0.03	0.03	0.03	0.03	0.04	0.03	0.03	0.03	0.03	0.03	0.03	0.04	0.03	0.03	0.05	0.03	0.03	0.01	0.02	0.01	0.01	0.05	0.03	0.03	0.01	0.02	0.01	0.01	0.05	0.03	0.01	0.01	0.00	0.00	0.00	0.05	0.03	0.01	0.01	0.00	0.00	0.00	1	2	4	8	7	8	8	0	32.5	1	1	2.11	1	1	2.4	1.45	2.33	2.4	1.78	1	0	0	1	0	75.00	1	0	1	0	1	0	0.91	0	0.83	0.01	1	0	1.11	0
20	xy_model - random.tcc:458-3374 [...]	initializeLattice(Lattice&, int, int, int)	Innermost	0.02	0.01	0.02	0.02	0.00	0.00	0.00	0.02	0.01	0.01	0.02	0.00	0.00	0.00	0.02	0.01	0.01	0.02	0.00	0.00	0.00	0.03	0.01	0.01	0.01	0.00	0.00	0.00	0.03	0.01	0.01	0.01	0.00	0.00	0.00	0.03	0.01	0.01	0.01	0.00	0.00	0.00	0.03	0.01	0.01	0.01	0.00	0.00	0.00	1	1	3	8	0	0	0	6.67	28.61	2.54	1.27	1.56	1	1	1.5	1	0	0	0	NA	NA	NA	NA	NA	0.00	1	0	3	0	1.5	0	0.75	0.01
6	xy_model - random.tcc:404-3374 [...]	metropolisHalfSweep(Lattice&, int, int, double, int, int, int)	Outermost	0.01	0.00	0.04	0.02	0.10	0.10	0.18	0.01	0.00	0.02	0.01	0.01	0.01	0.01	24.82	0.00	23.98	24.06	24.59	24.04	24.05	0.02	0.00	0.01	0.01	0.01	0.01	0.01	40.68	0.00	9.80	5.04	2.63	1.34	0.69	0.02	0.00	0.01	0.00	0.00	0.00	0.00	40.68	0.00	9.83	4.94	2.53	1.25	0.63	1	0	3	3	2	3	3	0	28.95	4.75	1	2.05	1	0	1.5	1	1.33	1	1	NA	NA	NA	NA	NA	0.00	1	0			0.67	0.01	1.33	0	1.33	0	1.33	0	1.33	0
10	xy_model - random.tcc:404-409	metropolisHalfSweep(Lattice&, int, int, double, int, int, int)	Innermost	0.00	0.01	0.01	0.05	0.05	0.10	0.18	0.00	0.01	0.00	0.02	0.01	0.00	0.01	0.00	0.01	0.00	0.02	0.01	0.00	0.01	0.00	0.01	0.00	0.01	0.00	0.00	0.00	0.00	0.01	0.00	0.01	0.00	0.00	0.00	0.00	0.01	0.00	0.00	0.00	0.00	0.00	0.00	0.01	0.00	0.00	0.00	0.00	0.00	0	1	1	3	2	1	2	0	32.5	1	1	2.11	0	1	1	1.2	1	1	1	1	0	0	1	0	75.00			1	0	1	0	1	0	1	0	1	0	1	0

×