A Random Number Generator Test Suite for the C++ ... - ETH ZÃ¼rich

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4. Using the “Random Number Generator Test Suite”4.3. Testing Parallel Random Number GeneratorsTo test a parallel application using different random number generators in different threads,there is a class called parallel_rng_imitator(from parallel_rng_imitator.h)which simulates such an application. The class contains a collection of definable generatorsand calls one after another. This generator fulfils the Boost specification and can be used ina normal way.There are some preconditions to keep in mind when using such a random number generator.All random number generators used in this parallel random number generator musthave the same result_type. Unfortunately the boost::uniform_01type doesnot support an default constructor, so it is not possible to map the result type to an othertype. To do this, a converter which fulfils the specified interface for generators has tobe written.All random number generators must have the same maximum and minimum value.Pre-seeded random number generators should be favoured because of a better controlover seeding the particular generators.// include Boosts RNGs#include // include parallel generator#include "parallel_rng_imitator.h"// import RNGs from Boostusing boost::minstd_rand0;using boost::lagged_fibonacci19937;using boost::lagged_fibonacci23209;using boost::lagged_fibonacci44497;using boost::mt19937;using boost::ecuyer1988;// make a RNG from two different Lagged Fibonacci RNGsparallel_rng_imitator parallelRNG;// does not compile, because the enlisted generators do not// have same result_typeparallel_rng_imitator parallelRNG_error_compile;// does compile, but throws an exception because the RNGs// does not have same min() or max() valueparallel_rng_imitator
4.4. Iterating a testminstd_rand // max = 2147483646> parallelRNG_error_runtime;4.4. Iterating a testThe idiom says that “Once doesn’t count”. So, we have to repeat a test multiple times andmake a statistic over all results. (Probably we also like to repeat this repetition. . . )This class iterates a given test n times and calculates a Kolmogorov-Smirnov statistic overall results. This is only possible if the test to iterate is derived from the chisquare_test,ks_test or gaussian_test base class. The iteration of a χ 2 or a gaussian test give anormal K-S statistic. But if we have to do this for a K-S test itself, we get four values, K¡and K £ for the original K¡ and the same for the original K £ .The iterate_test fulfils the test interface and acts like a normal test.template< class Test >iterate_test(Test test, std::size_t iterations) type of the testtest test to iterateiterations number of times to iterate the test4.5. Count failingsAnother way to decide about success or failure is to count the failings of each test andcompare with a maximal number of failures.This class iterates a given test n times and count the number of failings. If the test failsmore than the failLimit allows, then it will fail, else the test is passed. (Mathematically,failings failLimit) This is only possible if the test to iterate is derived from thechisquare_test, ks_test or gaussian_test base class. The iteration of a χ 2 ora gaussian test gives one value for failings, the K-S test variation results in two values, onefor K¡ and one for K £ .The count_fails_test fulfils the test interface and acts like a normal test.template< class Test >count_fails_test(Test test, std::size_t iterations, std::size_t failLimit) type of the testtest test to iterateiterations number of times to iterate the testfailLimit Limit deciding between failure or success4.6. Bit TestsIn some kind of tests, like in the “count-the-1’s” test from the Diehard test suite, overlappingranges of bits are tested. From each random number some new particular numbers are15
Page 1 and 2: ¡Institute for Theoretical Physics
Page 3: to my parentsUrs and Heidi
Page 7 and 8: Contents1. Introduction 12. What ar
Page 9 and 10: 1. IntroductionHow random is random
Page 11 and 12: 2.1. Types of random numberspossibl
Page 13 and 14: ¥3.1. χ 2 test (“Chi-square”
Page 15 and 16: 3.2. Kolmogorov-Smirnov test (KS te
Page 17 and 18: 3.3. Gaussian TestExample: Ising mo
Page 19 and 20: 4.2. The rng_test_suite environment
Page 21: 4.2. The rng_test_suite environment
Page 25 and 26: 4.8. The XML outputOriginal18019489
Page 27 and 28: 5.2. Run testExample: Throwing a di
Page 29 and 30: 5.2. Run testTest Class Name Descri
Page 31 and 32: 5.2. Run testSplitting up in subseq
Page 33 and 34: 5.4. Poker testThe first value lies
Page 35 and 36: 5.5. Coupon-collectors testTo calcu
Page 37 and 38: 5.6. Permutation testn number of co
Page 39 and 40: £ bin range percentage # expected
Page 41 and 42: N £ 2X N X 2 £N£1¤1¨§X5.10. S
Page 43 and 44: §25.12. Blocking testApply a χ 2
Page 45: - Generate n random number in X¢0;
Page 48 and 49: 5. Tests for Studying Random Data5.
Page 50 and 51: 5. Tests for Studying Random DataRu
Page 52 and 53: 5. Tests for Studying Random DataTh
Page 54 and 55: 5. Tests for Studying Random Dataat
Page 56 and 57: 5. Tests for Studying Random DataCo
Page 58 and 59: 6. Extending the Random Number Gene
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A. Collection of Test ParametersThe
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B. ExamplesTo point out the ease of
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Bibliography[1] Z. W. Birnbaum and
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Bibliography[26] A. Srinivasan, M.
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A Random Number Generator Test Suite for the C++ ... - ETH ZÃ¼rich

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