21 July 2014 03:53:57 PM ZIGGURAT_OPENMP: C++ version Number of processors = 2 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: 0.116844 0.124942 RATE: 85.5838 80.0369 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: 0.549411 0.55641 RATE: 18.2013 17.9724 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: 0.353653 0.360594 RATE: 28.2763 27.732 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: 1.43996 1.46862 RATE: 6.94464 6.80911 ZIGGURAT_OPENMP: Normal end of execution. 21 July 2014 03:54:02 PM 21 July 2014 03:54:02 PM ZIGGURAT_OPENMP: C++ version Number of processors = 2 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: 0.117037 0.0629014 RATE: 85.4427 158.979 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: 0.549453 0.299332 RATE: 18.1999 33.4077 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: 0.354332 0.212589 RATE: 28.2222 47.0391 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: 1.44327 0.768448 RATE: 6.92873 13.0132 ZIGGURAT_OPENMP: Normal end of execution. 21 July 2014 03:54:06 PM 21 July 2014 03:54:06 PM ZIGGURAT_OPENMP: C++ version Number of processors = 2 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: 0.116993 0.0629077 RATE: 85.4749 158.963 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: 0.549688 0.292786 RATE: 18.1921 34.1546 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: 0.358275 0.225441 RATE: 27.9115 44.3575 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: 1.44148 0.75201 RATE: 6.93733 13.2977 ZIGGURAT_OPENMP: Normal end of execution. 21 July 2014 03:54:10 PM