06 September 2018 11:08:20 AM ZIGGURAT_OPENMP: C version Number of processors available = 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: 0.070797 0.066838 RATE: 141.249061 149.616390 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 0.066051 0.066051 0.000000 Efficiency check: Computing values in parallel should be faster:' Sequential Parallel TIME: 0.297684 0.304923 RATE: 33.592719 32.795215 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 0.000000 Efficiency check: Computing values in parallel should be faster: Sequential Parallel TIME: 0.158088 0.166920 RATE: 63.256052 59.909098 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 0.000000 Efficiency check: Computing values in parallel should be faster: Sequential Parallel TIME: 0.837340 0.842756 RATE: 11.942583 11.865836 ZIGGURAT_OPENMP: Normal end of execution. 06 September 2018 11:08:23 AM 06 September 2018 11:08:23 AM ZIGGURAT_OPENMP: C version Number of processors available = 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: 0.071003 0.036295 RATE: 140.839921 275.519528 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 0.000000 1 0.617119 0.617119 0.000000 Efficiency check: Computing values in parallel should be faster:' Sequential Parallel TIME: 0.317376 0.160096 RATE: 31.508351 62.462606 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 0.000000 1 -1.070512 -1.070512 0.000000 Efficiency check: Computing values in parallel should be faster: Sequential Parallel TIME: 0.168982 0.085940 RATE: 59.177797 116.360897 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 0.000000 1 0.605476 0.605476 0.000000 Efficiency check: Computing values in parallel should be faster: Sequential Parallel TIME: 0.866709 0.444665 RATE: 11.537899 22.488831 ZIGGURAT_OPENMP: Normal end of execution. 06 September 2018 11:08:25 AM 06 September 2018 11:08:25 AM ZIGGURAT_OPENMP: C version Number of processors available = 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: 0.070412 0.018853 RATE: 142.022158 530.432476 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 0.000000 1 0.525170 0.525170 0.000000 2 0.130314 0.130314 0.000000 3 0.944491 0.944491 0.000000 Efficiency check: Computing values in parallel should be faster:' Sequential Parallel TIME: 0.294859 0.084151 RATE: 33.914528 118.833755 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 0.000000 1 0.314686 0.314686 0.000000 2 -0.989801 -0.989801 0.000000 3 -1.487724 -1.487724 0.000000 Efficiency check: Computing values in parallel should be faster: Sequential Parallel TIME: 0.171211 0.044466 RATE: 58.407328 224.892830 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 0.000000 1 0.162032 0.162032 0.000000 2 0.125027 0.125027 0.000000 3 0.264089 0.264089 0.000000 Efficiency check: Computing values in parallel should be faster: Sequential Parallel TIME: 0.836783 0.234431 RATE: 11.950528 42.656564 ZIGGURAT_OPENMP: Normal end of execution. 06 September 2018 11:08:27 AM