5 August 2020 9:28:59.025 AM ZIGGURAT_OPENMP: FORTRAN90 version The number of processors available is: 8 The number of threads available is: 1 TEST01 SHR3 computes random integers. Since the output is completely determined by the input value of SEED, we can run in parallel as long as we make an array of seeds. The number of threads is 1 Correctness check: Computing values sequentially should reach the same result as doing it in parallel: THREAD Sequential Parallel Difference 0 -1863796367 -1863796367 0 Efficiency check: Computing values in parallel should be faster: Sequential Parallel TIME: 0.762307E-01 0.679597E-01 RATE: 131.181 147.146 TEST02 R4_UNI computes uniformly random single precision real values. Since the output is completely determined by the input value of SEED, we can run in parallel as long as we make an array of seeds. The number of threads is 1 Correctness check: Computing values sequentially should reach the same result as doing it in parallel: THREAD Sequential Parallel Difference 0 0.660511E-01 0.660511E-01 0.00000 Efficiency check: Computing values in parallel should be faster: Sequential Parallel TIME: 0.104295 0.102568 RATE: 95.8822 97.4965 TEST03 R4_NOR computes normal random single precision real values. Since the output is completely determined by the input value of SEED and the tables, we can run in parallel as long as we make an array of seeds and share the tables. The number of threads is 1 Correctness check: Computing values sequentially should reach the same result as doing it in parallel: THREAD Sequential Parallel Difference 0 -0.326194 -0.326194 0.00000 Efficiency check: Computing values in parallel should be faster: Sequential Parallel TIME: 0.147802 0.149545 RATE: 67.6581 66.8696 TEST04 R4_EXP computes exponential random single precision real values. Since the output is completely determined by the input value of SEED and the tables, we can run in parallel as long as we make an array of seeds and share the tables. The number of threads is 1 Correctness check: Computing values sequentially should reach the same result as doing it in parallel: THREAD Sequential Parallel Difference 0 0.351739 0.351739 0.00000 Efficiency check: Computing values in parallel should be faster: Sequential Parallel TIME: 0.148077 0.150970 RATE: 67.5324 66.2385 ZIGGURAT_OPENMP: Normal end of execution. 5 August 2020 9:28:59.973 AM 5 August 2020 9:28:59.974 AM ZIGGURAT_OPENMP: FORTRAN90 version The number of processors available is: 8 The number of threads available is: 2 TEST01 SHR3 computes random integers. Since the output is completely determined by the input value of SEED, we can run in parallel as long as we make an array of seeds. The number of threads is 2 Correctness check: Computing values sequentially should reach the same result as doing it in parallel: THREAD Sequential Parallel Difference 0 1249912034 1249912034 0 1 503020437 503020437 0 Efficiency check: Computing values in parallel should be faster: Sequential Parallel TIME: 0.720670E-01 0.358380E-01 RATE: 138.760 279.034 TEST02 R4_UNI computes uniformly random single precision real values. Since the output is completely determined by the input value of SEED, we can run in parallel as long as we make an array of seeds. The number of threads is 2 Correctness check: Computing values sequentially should reach the same result as doing it in parallel: THREAD Sequential Parallel Difference 0 0.791018 0.791018 0.00000 1 0.617119 0.617119 0.00000 Efficiency check: Computing values in parallel should be faster: Sequential Parallel TIME: 0.104827 0.544689E-01 RATE: 95.3956 183.591 TEST03 R4_NOR computes normal random single precision real values. Since the output is completely determined by the input value of SEED and the tables, we can run in parallel as long as we make an array of seeds and share the tables. The number of threads is 2 Correctness check: Computing values sequentially should reach the same result as doing it in parallel: THREAD Sequential Parallel Difference 0 0.977730 0.977730 0.00000 1 -1.07051 -1.07051 0.00000 Efficiency check: Computing values in parallel should be faster: Sequential Parallel TIME: 0.148248 0.782864E-01 RATE: 67.4544 127.736 TEST04 R4_EXP computes exponential random single precision real values. Since the output is completely determined by the input value of SEED and the tables, we can run in parallel as long as we make an array of seeds and share the tables. The number of threads is 2 Correctness check: Computing values sequentially should reach the same result as doing it in parallel: THREAD Sequential Parallel Difference 0 1.73583 1.73583 0.00000 1 0.502068 0.502068 0.00000 Efficiency check: Computing values in parallel should be faster: Sequential Parallel TIME: 0.148168 0.793219E-01 RATE: 67.4910 126.069 ZIGGURAT_OPENMP: Normal end of execution. 5 August 2020 9:29:00.696 AM 5 August 2020 9:29:00.697 AM ZIGGURAT_OPENMP: FORTRAN90 version The number of processors available is: 8 The number of threads available is: 4 TEST01 SHR3 computes random integers. Since the output is completely determined by the input value of SEED, we can run in parallel as long as we make an array of seeds. The number of threads is 4 Correctness check: Computing values sequentially should reach the same result as doing it in parallel: THREAD Sequential Parallel Difference 0 -1669655539 -1669655539 0 1 108105747 108105747 0 2 -1587791136 -1587791136 0 3 1909075432 1909075432 0 Efficiency check: Computing values in parallel should be faster: Sequential Parallel TIME: 0.714346E-01 0.190677E-01 RATE: 139.988 524.447 TEST02 R4_UNI computes uniformly random single precision real values. Since the output is completely determined by the input value of SEED, we can run in parallel as long as we make an array of seeds. The number of threads is 4 Correctness check: Computing values sequentially should reach the same result as doing it in parallel: THREAD Sequential Parallel Difference 0 0.111253 0.111253 0.00000 1 0.525170 0.525170 0.00000 2 0.130314 0.130314 0.00000 3 0.944491 0.944491 0.00000 Efficiency check: Computing values in parallel should be faster: Sequential Parallel TIME: 0.107222 0.287882E-01 RATE: 93.2646 347.365 TEST03 R4_NOR computes normal random single precision real values. Since the output is completely determined by the input value of SEED and the tables, we can run in parallel as long as we make an array of seeds and share the tables. The number of threads is 4 Correctness check: Computing values sequentially should reach the same result as doing it in parallel: THREAD Sequential Parallel Difference 0 -0.828252 -0.828252 0.00000 1 0.314686 0.314686 0.00000 2 -0.989801 -0.989801 0.00000 3 -1.48772 -1.48772 0.00000 Efficiency check: Computing values in parallel should be faster: Sequential Parallel TIME: 0.149132 0.422823E-01 RATE: 67.0545 236.506 TEST04 R4_EXP computes exponential random single precision real values. Since the output is completely determined by the input value of SEED and the tables, we can run in parallel as long as we make an array of seeds and share the tables. The number of threads is 4 Correctness check: Computing values sequentially should reach the same result as doing it in parallel: THREAD Sequential Parallel Difference 0 0.129717 0.129717 0.00000 1 0.439901 0.439901 0.00000 2 0.834098 0.834098 0.00000 3 0.973891 0.973891 0.00000 Efficiency check: Computing values in parallel should be faster: Sequential Parallel TIME: 0.148717 0.425433E-01 RATE: 67.2419 235.055 ZIGGURAT_OPENMP: Normal end of execution. 5 August 2020 9:29:01.307 AM 5 August 2020 9:29:01.308 AM ZIGGURAT_OPENMP: FORTRAN90 version The number of processors available is: 8 The number of threads available is: 8 TEST01 SHR3 computes random integers. Since the output is completely determined by the input value of SEED, we can run in parallel as long as we make an array of seeds. The number of threads is 8 Correctness check: Computing values sequentially should reach the same result as doing it in parallel: THREAD Sequential Parallel Difference 0 2066176573 2066176573 0 1 -1303848666 -1303848666 0 2 167050157 167050157 0 3 412856606 412856606 0 4 -541773661 -541773661 0 5 -189888513 -189888513 0 6 -435391081 -435391081 0 7 1328385438 1328385438 0 Efficiency check: Computing values in parallel should be faster: Sequential Parallel TIME: 0.735369E-01 0.179418E-01 RATE: 135.986 557.358 TEST02 R4_UNI computes uniformly random single precision real values. Since the output is completely determined by the input value of SEED, we can run in parallel as long as we make an array of seeds. The number of threads is 8 Correctness check: Computing values sequentially should reach the same result as doing it in parallel: THREAD Sequential Parallel Difference 0 0.981069 0.981069 0.00000 1 0.196424 0.196424 0.00000 2 0.538894 0.538894 0.00000 3 0.596126 0.596126 0.00000 4 0.373859 0.373859 0.00000 5 0.455788 0.455788 0.00000 6 0.398628 0.398628 0.00000 7 0.809289 0.809289 0.00000 Efficiency check: Computing values in parallel should be faster: Sequential Parallel TIME: 0.110070 0.201877E-01 RATE: 90.8516 495.352 TEST03 R4_NOR computes normal random single precision real values. Since the output is completely determined by the input value of SEED and the tables, we can run in parallel as long as we make an array of seeds and share the tables. The number of threads is 8 Correctness check: Computing values sequentially should reach the same result as doing it in parallel: THREAD Sequential Parallel Difference 0 -1.33701 -1.33701 0.00000 1 1.76707 1.76707 0.00000 2 0.673030 0.673030 0.00000 3 -0.489069E-01 -0.489069E-01 0.00000 4 -0.393083 -0.393083 0.00000 5 -0.447175 -0.447175 0.00000 6 -0.819305E-01 -0.819305E-01 0.00000 7 -0.861219 -0.861219 0.00000 Efficiency check: Computing values in parallel should be faster: Sequential Parallel TIME: 0.153320 0.355535E-01 RATE: 65.2230 281.266 TEST04 R4_EXP computes exponential random single precision real values. Since the output is completely determined by the input value of SEED and the tables, we can run in parallel as long as we make an array of seeds and share the tables. The number of threads is 8 Correctness check: Computing values sequentially should reach the same result as doing it in parallel: THREAD Sequential Parallel Difference 0 0.445202 0.445202 0.00000 1 0.451912 0.451912 0.00000 2 0.355638 0.355638 0.00000 3 0.743365 0.743365 0.00000 4 1.85271 1.85271 0.00000 5 0.674086 0.674086 0.00000 6 0.810988 0.810988 0.00000 7 0.686205 0.686205 0.00000 Efficiency check: Computing values in parallel should be faster: Sequential Parallel TIME: 0.151661 0.359549E-01 RATE: 65.9365 278.126 ZIGGURAT_OPENMP: Normal end of execution. 5 August 2020 9:29:01.907 AM