Re: [math] Improving tests and performance of RandomGenerator implementations
On 8/3/11 10:05 AM, Luc Maisonobe wrote: > Le 03/08/2011 18:15, Phil Steitz a écrit : >> On 8/3/11 9:02 AM, sebb wrote: >>> On 3 August 2011 09:06, Luc Maisonobe >>> wrote: Le 03/08/2011 09:38, Luc Maisonobe a écrit : > Le 01/08/2011 22:40, Luc Maisonobe a écrit : >> Hi Phil, >> >> Le 01/08/2011 20:39, Phil Steitz a écrit : >>> On 8/1/11 1:31 AM, luc.maison...@free.fr wrote: Hi Phil, - Mail original - > In my own applications, I noticed what appears to be poor > performance in the nextInt(int) method of the Mersenne > twister, > which I was using to *improve* speed. I think that for > small n, the > default implementation in BistreamGenerator may be running > too many > iterations. Mersenne twister uses a quite large pool. It creates pseudo-random bits by twisting it and creates large bunches at a time (624 words at a time). Hence when you ask for large sets, you should have several calls that return fast, and one call that takes a longer time to generate another large pool. So good performances are obtained for generating large sets, not small sets. Well generators should be faster and are preferred over Mersenne twister now, which is now an old generator. Well generators also have large pools, but they don't generate bits in large batches in advance, they do generate a few words at a time. >>> Yeah, I know. Both are faster than the JDK, though, even for >>> just >>> 32-bit chunks in my tests at least. >>> >>> One thing I have been thinking about is exposing nextInt[], >>> nextDouble[], nextGaussian[] etc methods that take advantage >>> of the >>> pools. So you basically generate a large block of bits use >>> this to >>> fill the output arrays. >> Seems a very good idea. Most of the time, people generate >> only one kind >> of numbers several times, so it really does make sense. >> > I am still figuring out how the code works, but I > thought it would be good to run some benchmarks - using > Gilles' new > stuff - against the Harmony implementation in > java.util.Random of > this method. That led me to notice that there are no unit > tests for > BitstreamGenerator. I propose that we add > 0) RandomGeneratorAbstractTest with an abstract makeGenerator > method including fixed seed tests for all RandomGenerator > methods > 1) BitstreamGeneratorTest extending > RandomGeneratorAbstractTest > implementing makeGenerator with a BitStreamGenerator that > uses the > JDK generator for next(int) > 2) Make the test classes for Mersenne and Weil generators > extend > RandomGeneratorAbstractTest, moving redundant tests up > into the base > class > > Sound reasonable? +1 > Also, any recollection why we are using a > different implementation in BitStreamGenerator for > next(int) than > Harmony and the JDK use? I don't understand what you mean. next(int) is used to generate the raw bits and is the heart of each generator. Each generator has its own implementation. Replacing next(int) by the JDK generation would imply dropping completely Mersenne twister and Well generators. >>> I am sorry. I meant nextInt(int). It is that code that seems >>> to be >>> slow in BitStreamGenerator and different from the JDK and >>> Harmony. >> Could you point me at some code ? There are many pitfalls in >> netInt(int) >> if one wants to make sure the generator is uniform, which >> explain the >> strange implementation, with the mask computation and the >> loop. By the >> way, even this implementation would benefit from your >> proposed array >> generation, as the mask could be computed only once. > I have looked at the implementation for JDK and Harmony and am > a little > puzzled. > > The trick for the power of two (i.e. if ((n& -n) == n)) is > not useful > for the very elaborate generators like Mersenne twister or > Well. Both > are proven to be equidistributed even for the low order bits. > They are > based on linear recurrences but not linear congruences and do > not suffer > from the drawbacks of the latter. > > What puzzles me more is the loop. It is documented as avoiding > the > uneven distributions, but at first glance the modulo operation > bothers > me. As documentation explicitly states it is designed for > this
Re: [math] Improving tests and performance of RandomGenerator implementations
Le 03/08/2011 18:15, Phil Steitz a écrit : On 8/3/11 9:02 AM, sebb wrote: On 3 August 2011 09:06, Luc Maisonobe wrote: Le 03/08/2011 09:38, Luc Maisonobe a écrit : Le 01/08/2011 22:40, Luc Maisonobe a écrit : Hi Phil, Le 01/08/2011 20:39, Phil Steitz a écrit : On 8/1/11 1:31 AM, luc.maison...@free.fr wrote: Hi Phil, - Mail original - In my own applications, I noticed what appears to be poor performance in the nextInt(int) method of the Mersenne twister, which I was using to *improve* speed. I think that for small n, the default implementation in BistreamGenerator may be running too many iterations. Mersenne twister uses a quite large pool. It creates pseudo-random bits by twisting it and creates large bunches at a time (624 words at a time). Hence when you ask for large sets, you should have several calls that return fast, and one call that takes a longer time to generate another large pool. So good performances are obtained for generating large sets, not small sets. Well generators should be faster and are preferred over Mersenne twister now, which is now an old generator. Well generators also have large pools, but they don't generate bits in large batches in advance, they do generate a few words at a time. Yeah, I know. Both are faster than the JDK, though, even for just 32-bit chunks in my tests at least. One thing I have been thinking about is exposing nextInt[], nextDouble[], nextGaussian[] etc methods that take advantage of the pools. So you basically generate a large block of bits use this to fill the output arrays. Seems a very good idea. Most of the time, people generate only one kind of numbers several times, so it really does make sense. I am still figuring out how the code works, but I thought it would be good to run some benchmarks - using Gilles' new stuff - against the Harmony implementation in java.util.Random of this method. That led me to notice that there are no unit tests for BitstreamGenerator. I propose that we add 0) RandomGeneratorAbstractTest with an abstract makeGenerator method including fixed seed tests for all RandomGenerator methods 1) BitstreamGeneratorTest extending RandomGeneratorAbstractTest implementing makeGenerator with a BitStreamGenerator that uses the JDK generator for next(int) 2) Make the test classes for Mersenne and Weil generators extend RandomGeneratorAbstractTest, moving redundant tests up into the base class Sound reasonable? +1 Also, any recollection why we are using a different implementation in BitStreamGenerator for next(int) than Harmony and the JDK use? I don't understand what you mean. next(int) is used to generate the raw bits and is the heart of each generator. Each generator has its own implementation. Replacing next(int) by the JDK generation would imply dropping completely Mersenne twister and Well generators. I am sorry. I meant nextInt(int). It is that code that seems to be slow in BitStreamGenerator and different from the JDK and Harmony. Could you point me at some code ? There are many pitfalls in netInt(int) if one wants to make sure the generator is uniform, which explain the strange implementation, with the mask computation and the loop. By the way, even this implementation would benefit from your proposed array generation, as the mask could be computed only once. I have looked at the implementation for JDK and Harmony and am a little puzzled. The trick for the power of two (i.e. if ((n& -n) == n)) is not useful for the very elaborate generators like Mersenne twister or Well. Both are proven to be equidistributed even for the low order bits. They are based on linear recurrences but not linear congruences and do not suffer from the drawbacks of the latter. What puzzles me more is the loop. It is documented as avoiding the uneven distributions, but at first glance the modulo operation bothers me. As documentation explicitly states it is designed for this, it is most probably true, I simply don't understand how yet. So our current implementation is slow, then go ahead and change it to the one you showed me. I would simply suggest to get rid of the ((n& -n) == n) test. I'll try to understand the condition in the while loop to understand how it rejects uneven distributions, just out of curiosity for myself. OK, I finally understood the algorithm and how it rejects the largest incomplete numbers from k*n to (2^31)-1 where k*n is the largest multiple of n that fits in a positive integer. The trick lies in the addition of (n-1) which overflows the integer and wraps the result back to negative values. It is smart. +1 to use it. Provided that the algorithm is documented ... Yeah, I was going to try to decipher it (and the current impl) and provide some doc. One other thing to consider in this decision is do we have to worry about encumberance. The Harmony impl looks very similar to what is described in the JDK javadoc. I wonder if SunOracle might have claim to it. Where did you get the current impl, Lu
Re: [math] Improving tests and performance of RandomGenerator implementations
On 8/3/11 9:02 AM, sebb wrote: > On 3 August 2011 09:06, Luc Maisonobe wrote: >> Le 03/08/2011 09:38, Luc Maisonobe a écrit : >>> Le 01/08/2011 22:40, Luc Maisonobe a écrit : Hi Phil, Le 01/08/2011 20:39, Phil Steitz a écrit : > On 8/1/11 1:31 AM, luc.maison...@free.fr wrote: >> Hi Phil, >> >> - Mail original - >>> In my own applications, I noticed what appears to be poor >>> performance in the nextInt(int) method of the Mersenne twister, >>> which I was using to *improve* speed. I think that for small n, the >>> default implementation in BistreamGenerator may be running too many >>> iterations. >> Mersenne twister uses a quite large pool. It creates pseudo-random bits >> by twisting it and creates large bunches at a time (624 words at a >> time). >> Hence when you ask for large sets, you should have several calls that >> return fast, and one call that takes a longer time to generate another >> large pool. >> >> So good performances are obtained for generating large sets, not >> small sets. >> >> Well generators should be faster and are preferred over Mersenne >> twister now, >> which is now an old generator. Well generators also have large pools, >> but they >> don't generate bits in large batches in advance, they do generate a >> few words >> at a time. > Yeah, I know. Both are faster than the JDK, though, even for just > 32-bit chunks in my tests at least. > > One thing I have been thinking about is exposing nextInt[], > nextDouble[], nextGaussian[] etc methods that take advantage of the > pools. So you basically generate a large block of bits use this to > fill the output arrays. Seems a very good idea. Most of the time, people generate only one kind of numbers several times, so it really does make sense. >>> I am still figuring out how the code works, but I >>> thought it would be good to run some benchmarks - using Gilles' new >>> stuff - against the Harmony implementation in java.util.Random of >>> this method. That led me to notice that there are no unit tests for >>> BitstreamGenerator. I propose that we add >>> 0) RandomGeneratorAbstractTest with an abstract makeGenerator >>> method including fixed seed tests for all RandomGenerator methods >>> 1) BitstreamGeneratorTest extending RandomGeneratorAbstractTest >>> implementing makeGenerator with a BitStreamGenerator that uses the >>> JDK generator for next(int) >>> 2) Make the test classes for Mersenne and Weil generators extend >>> RandomGeneratorAbstractTest, moving redundant tests up into the base >>> class >>> >>> Sound reasonable? >> +1 >> >>> Also, any recollection why we are using a >>> different implementation in BitStreamGenerator for next(int) than >>> Harmony and the JDK use? >> I don't understand what you mean. next(int) is used to generate the raw >> bits and is the heart of each generator. Each generator has its own >> implementation. Replacing next(int) by the JDK generation would imply >> dropping completely Mersenne twister and Well generators. > I am sorry. I meant nextInt(int). It is that code that seems to be > slow in BitStreamGenerator and different from the JDK and Harmony. Could you point me at some code ? There are many pitfalls in netInt(int) if one wants to make sure the generator is uniform, which explain the strange implementation, with the mask computation and the loop. By the way, even this implementation would benefit from your proposed array generation, as the mask could be computed only once. >>> I have looked at the implementation for JDK and Harmony and am a little >>> puzzled. >>> >>> The trick for the power of two (i.e. if ((n & -n) == n)) is not useful >>> for the very elaborate generators like Mersenne twister or Well. Both >>> are proven to be equidistributed even for the low order bits. They are >>> based on linear recurrences but not linear congruences and do not suffer >>> from the drawbacks of the latter. >>> >>> What puzzles me more is the loop. It is documented as avoiding the >>> uneven distributions, but at first glance the modulo operation bothers >>> me. As documentation explicitly states it is designed for this, it is >>> most probably true, I simply don't understand how yet. >>> >>> So our current implementation is slow, then go ahead and change it to >>> the one you showed me. I would simply suggest to get rid of the ((n & >>> -n) == n) test. I'll try to understand the condition in the while loop >>> to understand how it rejects uneven distributions, just out of curiosity >>> for myself. >> OK, I finally understood the algorithm and how it rejects the largest >> incomplete numbers from k*n to (2^31)-1 where k*n is the largest multiple of >> n that fits in a positive integer. The trick lies in the addit
Re: [math] Improving tests and performance of RandomGenerator implementations
On 3 August 2011 09:06, Luc Maisonobe wrote: > Le 03/08/2011 09:38, Luc Maisonobe a écrit : >> >> Le 01/08/2011 22:40, Luc Maisonobe a écrit : >>> >>> Hi Phil, >>> >>> Le 01/08/2011 20:39, Phil Steitz a écrit : On 8/1/11 1:31 AM, luc.maison...@free.fr wrote: > > Hi Phil, > > - Mail original - >> >> In my own applications, I noticed what appears to be poor >> performance in the nextInt(int) method of the Mersenne twister, >> which I was using to *improve* speed. I think that for small n, the >> default implementation in BistreamGenerator may be running too many >> iterations. > > Mersenne twister uses a quite large pool. It creates pseudo-random bits > by twisting it and creates large bunches at a time (624 words at a > time). > Hence when you ask for large sets, you should have several calls that > return fast, and one call that takes a longer time to generate another > large pool. > > So good performances are obtained for generating large sets, not > small sets. > > Well generators should be faster and are preferred over Mersenne > twister now, > which is now an old generator. Well generators also have large pools, > but they > don't generate bits in large batches in advance, they do generate a > few words > at a time. Yeah, I know. Both are faster than the JDK, though, even for just 32-bit chunks in my tests at least. One thing I have been thinking about is exposing nextInt[], nextDouble[], nextGaussian[] etc methods that take advantage of the pools. So you basically generate a large block of bits use this to fill the output arrays. >>> >>> Seems a very good idea. Most of the time, people generate only one kind >>> of numbers several times, so it really does make sense. >>> > >> I am still figuring out how the code works, but I >> thought it would be good to run some benchmarks - using Gilles' new >> stuff - against the Harmony implementation in java.util.Random of >> this method. That led me to notice that there are no unit tests for >> BitstreamGenerator. I propose that we add >> 0) RandomGeneratorAbstractTest with an abstract makeGenerator >> method including fixed seed tests for all RandomGenerator methods >> 1) BitstreamGeneratorTest extending RandomGeneratorAbstractTest >> implementing makeGenerator with a BitStreamGenerator that uses the >> JDK generator for next(int) >> 2) Make the test classes for Mersenne and Weil generators extend >> RandomGeneratorAbstractTest, moving redundant tests up into the base >> class >> >> Sound reasonable? > > +1 > >> Also, any recollection why we are using a >> different implementation in BitStreamGenerator for next(int) than >> Harmony and the JDK use? > > I don't understand what you mean. next(int) is used to generate the raw > bits and is the heart of each generator. Each generator has its own > implementation. Replacing next(int) by the JDK generation would imply > dropping completely Mersenne twister and Well generators. I am sorry. I meant nextInt(int). It is that code that seems to be slow in BitStreamGenerator and different from the JDK and Harmony. >>> >>> Could you point me at some code ? There are many pitfalls in netInt(int) >>> if one wants to make sure the generator is uniform, which explain the >>> strange implementation, with the mask computation and the loop. By the >>> way, even this implementation would benefit from your proposed array >>> generation, as the mask could be computed only once. >> >> I have looked at the implementation for JDK and Harmony and am a little >> puzzled. >> >> The trick for the power of two (i.e. if ((n & -n) == n)) is not useful >> for the very elaborate generators like Mersenne twister or Well. Both >> are proven to be equidistributed even for the low order bits. They are >> based on linear recurrences but not linear congruences and do not suffer >> from the drawbacks of the latter. >> >> What puzzles me more is the loop. It is documented as avoiding the >> uneven distributions, but at first glance the modulo operation bothers >> me. As documentation explicitly states it is designed for this, it is >> most probably true, I simply don't understand how yet. >> >> So our current implementation is slow, then go ahead and change it to >> the one you showed me. I would simply suggest to get rid of the ((n & >> -n) == n) test. I'll try to understand the condition in the while loop >> to understand how it rejects uneven distributions, just out of curiosity >> for myself. > > OK, I finally understood the algorithm and how it rejects the largest > incomplete numbers from k*n to (2^31)-1 where k*n is the largest multiple of > n that fits in a positive integer. The trick lies in the addition of (n-1) > which overflows the integer and wraps th
Re: [math] Improving tests and performance of RandomGenerator implementations
Le 03/08/2011 09:38, Luc Maisonobe a écrit : Le 01/08/2011 22:40, Luc Maisonobe a écrit : Hi Phil, Le 01/08/2011 20:39, Phil Steitz a écrit : On 8/1/11 1:31 AM, luc.maison...@free.fr wrote: Hi Phil, - Mail original - In my own applications, I noticed what appears to be poor performance in the nextInt(int) method of the Mersenne twister, which I was using to *improve* speed. I think that for small n, the default implementation in BistreamGenerator may be running too many iterations. Mersenne twister uses a quite large pool. It creates pseudo-random bits by twisting it and creates large bunches at a time (624 words at a time). Hence when you ask for large sets, you should have several calls that return fast, and one call that takes a longer time to generate another large pool. So good performances are obtained for generating large sets, not small sets. Well generators should be faster and are preferred over Mersenne twister now, which is now an old generator. Well generators also have large pools, but they don't generate bits in large batches in advance, they do generate a few words at a time. Yeah, I know. Both are faster than the JDK, though, even for just 32-bit chunks in my tests at least. One thing I have been thinking about is exposing nextInt[], nextDouble[], nextGaussian[] etc methods that take advantage of the pools. So you basically generate a large block of bits use this to fill the output arrays. Seems a very good idea. Most of the time, people generate only one kind of numbers several times, so it really does make sense. I am still figuring out how the code works, but I thought it would be good to run some benchmarks - using Gilles' new stuff - against the Harmony implementation in java.util.Random of this method. That led me to notice that there are no unit tests for BitstreamGenerator. I propose that we add 0) RandomGeneratorAbstractTest with an abstract makeGenerator method including fixed seed tests for all RandomGenerator methods 1) BitstreamGeneratorTest extending RandomGeneratorAbstractTest implementing makeGenerator with a BitStreamGenerator that uses the JDK generator for next(int) 2) Make the test classes for Mersenne and Weil generators extend RandomGeneratorAbstractTest, moving redundant tests up into the base class Sound reasonable? +1 Also, any recollection why we are using a different implementation in BitStreamGenerator for next(int) than Harmony and the JDK use? I don't understand what you mean. next(int) is used to generate the raw bits and is the heart of each generator. Each generator has its own implementation. Replacing next(int) by the JDK generation would imply dropping completely Mersenne twister and Well generators. I am sorry. I meant nextInt(int). It is that code that seems to be slow in BitStreamGenerator and different from the JDK and Harmony. Could you point me at some code ? There are many pitfalls in netInt(int) if one wants to make sure the generator is uniform, which explain the strange implementation, with the mask computation and the loop. By the way, even this implementation would benefit from your proposed array generation, as the mask could be computed only once. I have looked at the implementation for JDK and Harmony and am a little puzzled. The trick for the power of two (i.e. if ((n & -n) == n)) is not useful for the very elaborate generators like Mersenne twister or Well. Both are proven to be equidistributed even for the low order bits. They are based on linear recurrences but not linear congruences and do not suffer from the drawbacks of the latter. What puzzles me more is the loop. It is documented as avoiding the uneven distributions, but at first glance the modulo operation bothers me. As documentation explicitly states it is designed for this, it is most probably true, I simply don't understand how yet. So our current implementation is slow, then go ahead and change it to the one you showed me. I would simply suggest to get rid of the ((n & -n) == n) test. I'll try to understand the condition in the while loop to understand how it rejects uneven distributions, just out of curiosity for myself. OK, I finally understood the algorithm and how it rejects the largest incomplete numbers from k*n to (2^31)-1 where k*n is the largest multiple of n that fits in a positive integer. The trick lies in the addition of (n-1) which overflows the integer and wraps the result back to negative values. It is smart. +1 to use it. Luc Luc Luc Phil Mersenne twister and Well should be fast for generating large sets, but most importantly they have very good and *proven* properties (equidistribution on large dimensions, null correlation, maximal period ...). These properties are essential for example in Monte-Carlo simulations with lots of variables that must be independent or have controlled correlations. Luc The Harmony impl is almost identical to what is documented in the JDK javadoc. Phil ---
Re: [math] Improving tests and performance of RandomGenerator implementations
Le 01/08/2011 22:40, Luc Maisonobe a écrit : Hi Phil, Le 01/08/2011 20:39, Phil Steitz a écrit : On 8/1/11 1:31 AM, luc.maison...@free.fr wrote: Hi Phil, - Mail original - In my own applications, I noticed what appears to be poor performance in the nextInt(int) method of the Mersenne twister, which I was using to *improve* speed. I think that for small n, the default implementation in BistreamGenerator may be running too many iterations. Mersenne twister uses a quite large pool. It creates pseudo-random bits by twisting it and creates large bunches at a time (624 words at a time). Hence when you ask for large sets, you should have several calls that return fast, and one call that takes a longer time to generate another large pool. So good performances are obtained for generating large sets, not small sets. Well generators should be faster and are preferred over Mersenne twister now, which is now an old generator. Well generators also have large pools, but they don't generate bits in large batches in advance, they do generate a few words at a time. Yeah, I know. Both are faster than the JDK, though, even for just 32-bit chunks in my tests at least. One thing I have been thinking about is exposing nextInt[], nextDouble[], nextGaussian[] etc methods that take advantage of the pools. So you basically generate a large block of bits use this to fill the output arrays. Seems a very good idea. Most of the time, people generate only one kind of numbers several times, so it really does make sense. I am still figuring out how the code works, but I thought it would be good to run some benchmarks - using Gilles' new stuff - against the Harmony implementation in java.util.Random of this method. That led me to notice that there are no unit tests for BitstreamGenerator. I propose that we add 0) RandomGeneratorAbstractTest with an abstract makeGenerator method including fixed seed tests for all RandomGenerator methods 1) BitstreamGeneratorTest extending RandomGeneratorAbstractTest implementing makeGenerator with a BitStreamGenerator that uses the JDK generator for next(int) 2) Make the test classes for Mersenne and Weil generators extend RandomGeneratorAbstractTest, moving redundant tests up into the base class Sound reasonable? +1 Also, any recollection why we are using a different implementation in BitStreamGenerator for next(int) than Harmony and the JDK use? I don't understand what you mean. next(int) is used to generate the raw bits and is the heart of each generator. Each generator has its own implementation. Replacing next(int) by the JDK generation would imply dropping completely Mersenne twister and Well generators. I am sorry. I meant nextInt(int). It is that code that seems to be slow in BitStreamGenerator and different from the JDK and Harmony. Could you point me at some code ? There are many pitfalls in netInt(int) if one wants to make sure the generator is uniform, which explain the strange implementation, with the mask computation and the loop. By the way, even this implementation would benefit from your proposed array generation, as the mask could be computed only once. I have looked at the implementation for JDK and Harmony and am a little puzzled. The trick for the power of two (i.e. if ((n & -n) == n)) is not useful for the very elaborate generators like Mersenne twister or Well. Both are proven to be equidistributed even for the low order bits. They are based on linear recurrences but not linear congruences and do not suffer from the drawbacks of the latter. What puzzles me more is the loop. It is documented as avoiding the uneven distributions, but at first glance the modulo operation bothers me. As documentation explicitly states it is designed for this, it is most probably true, I simply don't understand how yet. So our current implementation is slow, then go ahead and change it to the one you showed me. I would simply suggest to get rid of the ((n & -n) == n) test. I'll try to understand the condition in the while loop to understand how it rejects uneven distributions, just out of curiosity for myself. Luc Luc Phil Mersenne twister and Well should be fast for generating large sets, but most importantly they have very good and *proven* properties (equidistribution on large dimensions, null correlation, maximal period ...). These properties are essential for example in Monte-Carlo simulations with lots of variables that must be independent or have controlled correlations. Luc The Harmony impl is almost identical to what is documented in the JDK javadoc. Phil - To unsubscribe, e-mail: dev-unsubscr...@commons.apache.org For additional commands, e-mail: dev-h...@commons.apache.org - To unsubscribe, e-mail: dev-unsubscr...@commons.apache.org For additional commands, e-mail: dev-h...@commons.apache.
Re: [math] Improving tests and performance of RandomGenerator implementations
On 8/1/11 1:40 PM, Luc Maisonobe wrote: > Hi Phil, > > Le 01/08/2011 20:39, Phil Steitz a écrit : >> On 8/1/11 1:31 AM, luc.maison...@free.fr wrote: >>> Hi Phil, >>> >>> - Mail original - In my own applications, I noticed what appears to be poor performance in the nextInt(int) method of the Mersenne twister, which I was using to *improve* speed. I think that for small n, the default implementation in BistreamGenerator may be running too many iterations. >>> Mersenne twister uses a quite large pool. It creates >>> pseudo-random bits >>> by twisting it and creates large bunches at a time (624 words at >>> a time). >>> Hence when you ask for large sets, you should have several calls >>> that >>> return fast, and one call that takes a longer time to generate >>> another >>> large pool. >>> >>> So good performances are obtained for generating large sets, not >>> small sets. >>> >>> Well generators should be faster and are preferred over Mersenne >>> twister now, >>> which is now an old generator. Well generators also have large >>> pools, but they >>> don't generate bits in large batches in advance, they do >>> generate a few words >>> at a time. >> >> Yeah, I know. Both are faster than the JDK, though, even for just >> 32-bit chunks in my tests at least. >> >> One thing I have been thinking about is exposing nextInt[], >> nextDouble[], nextGaussian[] etc methods that take advantage of the >> pools. So you basically generate a large block of bits use this to >> fill the output arrays. > > Seems a very good idea. Most of the time, people generate only one > kind of numbers several times, so it really does make sense. What I am not certain of and maybe you can clarify is beyond optimizing the chunking, does this really buy you anything in the common use case where the same generator instance is used repeatedly to fill an array. Won't the generator leverage the pool in this case anyway? The additional info on how many bits are going to be required should in theory be useful, but I am not sure how much it will get you. > >>> I am still figuring out how the code works, but I thought it would be good to run some benchmarks - using Gilles' new stuff - against the Harmony implementation in java.util.Random of this method. That led me to notice that there are no unit tests for BitstreamGenerator. I propose that we add 0) RandomGeneratorAbstractTest with an abstract makeGenerator method including fixed seed tests for all RandomGenerator methods 1) BitstreamGeneratorTest extending RandomGeneratorAbstractTest implementing makeGenerator with a BitStreamGenerator that uses the JDK generator for next(int) 2) Make the test classes for Mersenne and Weil generators extend RandomGeneratorAbstractTest, moving redundant tests up into the base class Sound reasonable? >>> +1 >>> Also, any recollection why we are using a different implementation in BitStreamGenerator for next(int) than Harmony and the JDK use? >>> I don't understand what you mean. next(int) is used to generate >>> the raw >>> bits and is the heart of each generator. Each generator has its own >>> implementation. Replacing next(int) by the JDK generation would >>> imply >>> dropping completely Mersenne twister and Well generators. >> >> I am sorry. I meant nextInt(int). It is that code that seems to be >> slow in BitStreamGenerator and different from the JDK and Harmony. > > Could you point me at some code ? There are many pitfalls in > netInt(int) if one wants to make sure the generator is uniform, > which explain the strange implementation, with the mask > computation and the loop. By the way, even this implementation > would benefit from your proposed array generation, as the mask > could be computed only once. See the JDK javadoc [1], which I assume matches the internal implementation and the Harmony code [2]. I know the pitfalls you are referring to, since I played a good bit with my own code to just translate bits when I needed to optimize performance in one of my own apps. I don't quite get the code we have now. I am a little baffled by the shifts to create the mask. I agree that for this case, you could definitely get a boost if you know n in advance and are filling an array. Thanks for looking at this. Phil [1] http://download.oracle.com/javase/6/docs/api/java/util/Random.html#nextInt%28int%29 [2] http://s.apache.org/ZWt > > Luc > > >> >> Phil >>> >>> Mersenne twister and Well should be fast for generating large >>> sets, but >>> most importantly they have very good and *proven* properties >>> (equidistribution >>> on large dimensions, null correlation, maximal period ...). >>> These properties >>> are essential for example in Monte-Carlo simulations with lots >>> of variables that >>> must be independent or have controlled correlations. >>> >>> Luc >>> The Harmony impl is almost ident
Re: [math] Improving tests and performance of RandomGenerator implementations
Hi Phil, Le 01/08/2011 20:39, Phil Steitz a écrit : On 8/1/11 1:31 AM, luc.maison...@free.fr wrote: Hi Phil, - Mail original - In my own applications, I noticed what appears to be poor performance in the nextInt(int) method of the Mersenne twister, which I was using to *improve* speed. I think that for small n, the default implementation in BistreamGenerator may be running too many iterations. Mersenne twister uses a quite large pool. It creates pseudo-random bits by twisting it and creates large bunches at a time (624 words at a time). Hence when you ask for large sets, you should have several calls that return fast, and one call that takes a longer time to generate another large pool. So good performances are obtained for generating large sets, not small sets. Well generators should be faster and are preferred over Mersenne twister now, which is now an old generator. Well generators also have large pools, but they don't generate bits in large batches in advance, they do generate a few words at a time. Yeah, I know. Both are faster than the JDK, though, even for just 32-bit chunks in my tests at least. One thing I have been thinking about is exposing nextInt[], nextDouble[], nextGaussian[] etc methods that take advantage of the pools. So you basically generate a large block of bits use this to fill the output arrays. Seems a very good idea. Most of the time, people generate only one kind of numbers several times, so it really does make sense. I am still figuring out how the code works, but I thought it would be good to run some benchmarks - using Gilles' new stuff - against the Harmony implementation in java.util.Random of this method. That led me to notice that there are no unit tests for BitstreamGenerator. I propose that we add 0) RandomGeneratorAbstractTest with an abstract makeGenerator method including fixed seed tests for all RandomGenerator methods 1) BitstreamGeneratorTest extending RandomGeneratorAbstractTest implementing makeGenerator with a BitStreamGenerator that uses the JDK generator for next(int) 2) Make the test classes for Mersenne and Weil generators extend RandomGeneratorAbstractTest, moving redundant tests up into the base class Sound reasonable? +1 Also, any recollection why we are using a different implementation in BitStreamGenerator for next(int) than Harmony and the JDK use? I don't understand what you mean. next(int) is used to generate the raw bits and is the heart of each generator. Each generator has its own implementation. Replacing next(int) by the JDK generation would imply dropping completely Mersenne twister and Well generators. I am sorry. I meant nextInt(int). It is that code that seems to be slow in BitStreamGenerator and different from the JDK and Harmony. Could you point me at some code ? There are many pitfalls in netInt(int) if one wants to make sure the generator is uniform, which explain the strange implementation, with the mask computation and the loop. By the way, even this implementation would benefit from your proposed array generation, as the mask could be computed only once. Luc Phil Mersenne twister and Well should be fast for generating large sets, but most importantly they have very good and *proven* properties (equidistribution on large dimensions, null correlation, maximal period ...). These properties are essential for example in Monte-Carlo simulations with lots of variables that must be independent or have controlled correlations. Luc The Harmony impl is almost identical to what is documented in the JDK javadoc. Phil - To unsubscribe, e-mail: dev-unsubscr...@commons.apache.org For additional commands, e-mail: dev-h...@commons.apache.org - To unsubscribe, e-mail: dev-unsubscr...@commons.apache.org For additional commands, e-mail: dev-h...@commons.apache.org - To unsubscribe, e-mail: dev-unsubscr...@commons.apache.org For additional commands, e-mail: dev-h...@commons.apache.org - To unsubscribe, e-mail: dev-unsubscr...@commons.apache.org For additional commands, e-mail: dev-h...@commons.apache.org
Re: [math] Improving tests and performance of RandomGenerator implementations
On 8/1/11 1:31 AM, luc.maison...@free.fr wrote: > Hi Phil, > > - Mail original - >> In my own applications, I noticed what appears to be poor >> performance in the nextInt(int) method of the Mersenne twister, >> which I was using to *improve* speed. I think that for small n, the >> default implementation in BistreamGenerator may be running too many >> iterations. > Mersenne twister uses a quite large pool. It creates pseudo-random bits > by twisting it and creates large bunches at a time (624 words at a time). > Hence when you ask for large sets, you should have several calls that > return fast, and one call that takes a longer time to generate another > large pool. > > So good performances are obtained for generating large sets, not small sets. > > Well generators should be faster and are preferred over Mersenne twister now, > which is now an old generator. Well generators also have large pools, but they > don't generate bits in large batches in advance, they do generate a few words > at a time. Yeah, I know. Both are faster than the JDK, though, even for just 32-bit chunks in my tests at least. One thing I have been thinking about is exposing nextInt[], nextDouble[], nextGaussian[] etc methods that take advantage of the pools. So you basically generate a large block of bits use this to fill the output arrays. > >> I am still figuring out how the code works, but I >> thought it would be good to run some benchmarks - using Gilles' new >> stuff - against the Harmony implementation in java.util.Random of >> this method. That led me to notice that there are no unit tests for >> BitstreamGenerator. I propose that we add >> 0) RandomGeneratorAbstractTest with an abstract makeGenerator >> method including fixed seed tests for all RandomGenerator methods >> 1) BitstreamGeneratorTest extending RandomGeneratorAbstractTest >> implementing makeGenerator with a BitStreamGenerator that uses the >> JDK generator for next(int) >> 2) Make the test classes for Mersenne and Weil generators extend >> RandomGeneratorAbstractTest, moving redundant tests up into the base >> class >> >> Sound reasonable? > +1 > >> Also, any recollection why we are using a >> different implementation in BitStreamGenerator for next(int) than >> Harmony and the JDK use? > I don't understand what you mean. next(int) is used to generate the raw > bits and is the heart of each generator. Each generator has its own > implementation. Replacing next(int) by the JDK generation would imply > dropping completely Mersenne twister and Well generators. I am sorry. I meant nextInt(int). It is that code that seems to be slow in BitStreamGenerator and different from the JDK and Harmony. Phil > > Mersenne twister and Well should be fast for generating large sets, but > most importantly they have very good and *proven* properties (equidistribution > on large dimensions, null correlation, maximal period ...). These properties > are essential for example in Monte-Carlo simulations with lots of variables > that > must be independent or have controlled correlations. > > Luc > >> The Harmony impl is almost identical to >> what is documented in the JDK javadoc. >> >> Phil >> >> - >> To unsubscribe, e-mail: dev-unsubscr...@commons.apache.org >> For additional commands, e-mail: dev-h...@commons.apache.org >> >> > - > To unsubscribe, e-mail: dev-unsubscr...@commons.apache.org > For additional commands, e-mail: dev-h...@commons.apache.org > > - To unsubscribe, e-mail: dev-unsubscr...@commons.apache.org For additional commands, e-mail: dev-h...@commons.apache.org
Re: [math] Improving tests and performance of RandomGenerator implementations
Hi Phil, - Mail original - > In my own applications, I noticed what appears to be poor > performance in the nextInt(int) method of the Mersenne twister, > which I was using to *improve* speed. I think that for small n, the > default implementation in BistreamGenerator may be running too many > iterations. Mersenne twister uses a quite large pool. It creates pseudo-random bits by twisting it and creates large bunches at a time (624 words at a time). Hence when you ask for large sets, you should have several calls that return fast, and one call that takes a longer time to generate another large pool. So good performances are obtained for generating large sets, not small sets. Well generators should be faster and are preferred over Mersenne twister now, which is now an old generator. Well generators also have large pools, but they don't generate bits in large batches in advance, they do generate a few words at a time. > I am still figuring out how the code works, but I > thought it would be good to run some benchmarks - using Gilles' new > stuff - against the Harmony implementation in java.util.Random of > this method. That led me to notice that there are no unit tests for > BitstreamGenerator. I propose that we add > 0) RandomGeneratorAbstractTest with an abstract makeGenerator > method including fixed seed tests for all RandomGenerator methods > 1) BitstreamGeneratorTest extending RandomGeneratorAbstractTest > implementing makeGenerator with a BitStreamGenerator that uses the > JDK generator for next(int) > 2) Make the test classes for Mersenne and Weil generators extend > RandomGeneratorAbstractTest, moving redundant tests up into the base > class > > Sound reasonable? +1 > Also, any recollection why we are using a > different implementation in BitStreamGenerator for next(int) than > Harmony and the JDK use? I don't understand what you mean. next(int) is used to generate the raw bits and is the heart of each generator. Each generator has its own implementation. Replacing next(int) by the JDK generation would imply dropping completely Mersenne twister and Well generators. Mersenne twister and Well should be fast for generating large sets, but most importantly they have very good and *proven* properties (equidistribution on large dimensions, null correlation, maximal period ...). These properties are essential for example in Monte-Carlo simulations with lots of variables that must be independent or have controlled correlations. Luc > The Harmony impl is almost identical to > what is documented in the JDK javadoc. > > Phil > > - > To unsubscribe, e-mail: dev-unsubscr...@commons.apache.org > For additional commands, e-mail: dev-h...@commons.apache.org > > - To unsubscribe, e-mail: dev-unsubscr...@commons.apache.org For additional commands, e-mail: dev-h...@commons.apache.org
[math] Improving tests and performance of RandomGenerator implementations
In my own applications, I noticed what appears to be poor performance in the nextInt(int) method of the Mersenne twister, which I was using to *improve* speed. I think that for small n, the default implementation in BistreamGenerator may be running too many iterations. I am still figuring out how the code works, but I thought it would be good to run some benchmarks - using Gilles' new stuff - against the Harmony implementation in java.util.Random of this method. That led me to notice that there are no unit tests for BitstreamGenerator. I propose that we add 0) RandomGeneratorAbstractTest with an abstract makeGenerator method including fixed seed tests for all RandomGenerator methods 1) BitstreamGeneratorTest extending RandomGeneratorAbstractTest implementing makeGenerator with a BitStreamGenerator that uses the JDK generator for next(int) 2) Make the test classes for Mersenne and Weil generators extend RandomGeneratorAbstractTest, moving redundant tests up into the base class Sound reasonable? Also, any recollection why we are using a different implementation in BitStreamGenerator for next(int) than Harmony and the JDK use? The Harmony impl is almost identical to what is documented in the JDK javadoc. Phil - To unsubscribe, e-mail: dev-unsubscr...@commons.apache.org For additional commands, e-mail: dev-h...@commons.apache.org
