24 August 2018 09:02:14 AM ZIGGURAT_OPENMP: C++ version Number of processors = 8 Number of threads = 1 TEST01 SHR3_SEEDED 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: 0.0726696 0.0697477 RATE: 137.609 143.374 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 0.0660511 0.0660511 0 Efficiency check: Computing values in parallel should be faster:' Sequential Parallel TIME: 0.314918 0.319064 RATE: 31.7543 31.3417 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 Efficiency check: Computing values in parallel should be faster: Sequential Parallel TIME: 0.172585 0.174627 RATE: 57.9424 57.2649 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 0.113969 0.113969 0 Efficiency check: Computing values in parallel should be faster: Sequential Parallel TIME: 0.910353 0.911671 RATE: 10.9847 10.9689 ZIGGURAT_OPENMP: Normal end of execution. 24 August 2018 09:02:17 AM 24 August 2018 09:02:17 AM ZIGGURAT_OPENMP: C++ version Number of processors = 8 Number of threads = 2 TEST01 SHR3_SEEDED 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: 0.0697457 0.0362262 RATE: 143.378 276.043 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 1 0.617119 0.617119 0 Efficiency check: Computing values in parallel should be faster:' Sequential Parallel TIME: 0.307857 0.167143 RATE: 32.4826 59.8289 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 0.97773 0.97773 0 1 -1.07051 -1.07051 0 Efficiency check: Computing values in parallel should be faster: Sequential Parallel TIME: 0.169407 0.0903381 RATE: 59.0295 110.695 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 0.332266 0.332266 0 1 0.605476 0.605476 0 Efficiency check: Computing values in parallel should be faster: Sequential Parallel TIME: 0.901303 0.523457 RATE: 11.0951 19.1038 ZIGGURAT_OPENMP: Normal end of execution. 24 August 2018 09:02:19 AM 24 August 2018 09:02:19 AM ZIGGURAT_OPENMP: C++ version Number of processors = 8 Number of threads = 4 TEST01 SHR3_SEEDED 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: 0.0698619 0.034868 RATE: 143.14 286.796 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 1 0.52517 0.52517 0 2 0.130314 0.130314 0 3 0.944491 0.944491 0 Efficiency check: Computing values in parallel should be faster:' Sequential Parallel TIME: 0.308563 0.104901 RATE: 32.4083 95.3278 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 1 0.314686 0.314686 0 2 -0.989801 -0.989801 0 3 -1.48772 -1.48772 0 Efficiency check: Computing values in parallel should be faster: Sequential Parallel TIME: 0.171316 0.0748028 RATE: 58.3718 133.685 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 0.427391 0.427391 0 1 0.162032 0.162032 0 2 0.125027 0.125027 0 3 0.264089 0.264089 0 Efficiency check: Computing values in parallel should be faster: Sequential Parallel TIME: 0.900929 0.323658 RATE: 11.0996 30.8968 ZIGGURAT_OPENMP: Normal end of execution. 24 August 2018 09:02:21 AM