AMD vs Intel

-----Original Message----- From: [email protected] <[email protected]> On Behalf Of Leonid Gibiansky Sent: Monday, November 18, 2019 11:05 AM To: nmusers <[email protected]> Subject: [NMusers] AMD vs Intel Dear All, I am testing the new Epyc processors from AMD (comparing with Intel Xeon), and getting different results. Just wondering whether anybody faced the problem of differences between AMD and Intel processors and knows how to solve it. I am using Intel compiler but ready to switch to gfortran or anything else if this would help to get identical results. There were reports of Intel slowing the AMD execution in the past, but in my tests, speed is comparable but the results differ. Thanks Leonid

On 18/11/19 6:34 pm, Leonid Gibiansky wrote: > Thanks Bob and Peter! > > The model is quite stable, but this is LAPLACIAN, so requires second > derivatives. At iteration 0, gradients differ by about 50 to 100% > between Intel and AMD. This leads to differences in minimization path, > and slightly different results. Not that different to change the > recommended dose, but sufficiently different to notice (OF difference > of 6 points; 50% more model evaluations to get to convergence). > Thanks > Leonid > > > > On 11/18/2019 12:15 PM, Bonate, Peter wrote: >> Leonid - when you say different. What do you mean? Fixed effect and >> random effects? Different OFV? >> >> We did a poster at AAPS a decade or so ago comparing results across >> different platforms using the same data and model. We got different >> results on the standard errors (which related to matrix inversion and >> how those are done using software-hardware configurations). And with >> overparameterized models we got different error messages - some >> platforms converged with no problem while some did not converge and >> gave R matrix singularity. >> >> Did your problems go beyond this? >> >> pete >> >> >> >> Peter Bonate, PhD >> Executive Director >> Pharmacokinetics, Modeling, and Simulation >> Astellas >> 1 Astellas Way, N3.158 >> Northbrook, IL 60062 >> Peter.bonate >> (224) 205-5855 >> >> >> >> Details are irrelevant in terms of decision making - Joe Biden. >> >> >> >> >> >> >> -----Original Message----- >> From: owner-nmusers >> Behalf Of Leonid Gibiansky >> Sent: Monday, November 18, 2019 11:05 AM >> To: nmusers <nmusers >> Subject: [NMusers] AMD vs Intel >> >> Dear All, >> >> I am testing the new Epyc processors from AMD (comparing with Intel >> Xeon), and getting different results. Just wondering whether anybody >> faced the problem of differences between AMD and Intel processors and >> knows how to solve it. I am using Intel compiler but ready to switch >> to gfortran or anything else if this would help to get identical >> results. >> There were reports of Intel slowing the AMD execution in the past, >> but in my tests, speed is comparable but the results differ. >> >> Thanks >> Leonid >> >> >> >

-----Original Message----- From: [email protected] <[email protected]> On Behalf Of Leonid Gibiansky Sent: Monday, November 18, 2019 12:05 PM To: nmusers <[email protected]> Subject: [NMusers] AMD vs Intel Dear All, I am testing the new Epyc processors from AMD (comparing with Intel Xeon), and getting different results. Just wondering whether anybody faced the problem of differences between AMD and Intel processors and knows how to solve it. I am using Intel compiler but ready to switch to gfortran or anything else if this would help to get identical results. There were reports of Intel slowing the AMD execution in the past, but in my tests, speed is comparable but the results differ. Thanks Leonid

> On 11/18/2019 12:15 PM, Bonate, Peter wrote: > > Leonid - when you say different. What do you mean? Fixed effect and > random effects? Different OFV? > > > > We did a poster at AAPS a decade or so ago comparing results across > different platforms using the same data and model. We got different > results on the standard errors (which related to matrix inversion and how > those are done using software-hardware configurations). And with > overparameterized models we got different error messages - some platforms > converged with no problem while some did not converge and gave R matrix > singularity. > > > > Did your problems go beyond this? > > > > pete > > > > > > > > Peter Bonate, PhD > > Executive Director > > Pharmacokinetics, Modeling, and Simulation > > Astellas > > 1 Astellas Way, N3.158 > > Northbrook, IL 60062 > > [email protected] > > (224) 205-5855 > > > > > > > > Details are irrelevant in terms of decision making - Joe Biden. > > > > > > > > > > > > > > -----Original Message----- > > From: [email protected] <[email protected]> On > Behalf Of Leonid Gibiansky > > Sent: Monday, November 18, 2019 11:05 AM > > To: nmusers <[email protected]> > > Subject: [NMusers] AMD vs Intel > > > > Dear All, > > > > I am testing the new Epyc processors from AMD (comparing with Intel > Xeon), and getting different results. Just wondering whether anybody faced > the problem of differences between AMD and Intel processors and knows how > to solve it. I am using Intel compiler but ready to switch to gfortran or > anything else if this would help to get identical results. > > There were reports of Intel slowing the AMD execution in the past, but > in my tests, speed is comparable but the results differ. > > > > Thanks > > Leonid > > > > > > > >

On 18/11/19 6:34 pm, Leonid Gibiansky wrote: > Thanks Bob and Peter! > > The model is quite stable, but this is LAPLACIAN, so requires second derivatives. At iteration 0, gradients differ by about 50 to 100% between Intel and AMD. This leads to differences in minimization path, and slightly different results. Not that different to change the recommended dose, but sufficiently different to notice (OF difference of 6 points; 50% more model evaluations to get to convergence). > > Thanks > Leonid > > On 11/18/2019 12:15 PM, Bonate, Peter wrote: > > > Leonid - when you say different. What do you mean? Fixed effect and random effects? Different OFV? > > > > We did a poster at AAPS a decade or so ago comparing results across different platforms using the same data and model. We got different results on the standard errors (which related to matrix inversion and how those are done using software-hardware configurations). And with overparameterized models we got different error messages - some platforms converged with no problem while some did not converge and gave R matrix singularity. > > > > Did your problems go beyond this? > > > > pete > > > > Peter Bonate, PhD > > Executive Director > > Pharmacokinetics, Modeling, and Simulation > > Astellas > > 1 Astellas Way, N3.158 > > Northbrook, IL 60062 > > [email protected] > > (224) 205-5855 > > > > Details are irrelevant in terms of decision making - Joe Biden. > > > > -----Original Message----- > > > > From: [email protected] < [email protected] > On Behalf Of Leonid Gibiansky > > > > Sent: Monday, November 18, 2019 11:05 AM > > To: nmusers <[email protected]> > > Subject: [NMusers] AMD vs Intel > > > > Dear All, > > > > I am testing the new Epyc processors from AMD (comparing with Intel Xeon), and getting different results. Just wondering whether anybody faced the problem of differences between AMD and Intel processors and knows how to solve it. I am using Intel compiler but ready to switch to gfortran or anything else if this would help to get identical results. There were reports of Intel slowing the AMD execution in the past, but in my tests, speed is comparable but the results differ. > > > > Thanks > > Leonid

> Op 18 nov. 2019 om 23:54 heeft Leonid Gibiansky <[email protected]> > het volgende geschreven: > > Hi Jeroin, > > Thanks for your input, very interesting. As far as the goal is concerned, I > am mostly interested to find options that would give identical results on two > platform rather than in speed. So far no luck: 4 combinations of gfortran / > Intel compilers on Xeon / AMD processors give 4 sets of results that are > close but not identical. > > Related question to the group: have anybody experimented with gfortran > options (rather than using default provided by Nonmem distribution)? Any > recommendations? Same goal: maximum reproducibility across different OSs, > parallelization options, and processor types. > > Thanks > Leonid > > > > >> On 11/18/2019 5:28 PM, Jeroen Elassaiss-Schaap (PD-value B.V.) wrote: >> Hi Leonid, >> "A while" back we compared model development trajectories and results >> between two computational platforms, Itanium and Xeon, see >> https://www.page-meeting.org/?abstract=1188. The results roughly were: 1/3 >> equal, 1/3 rounding differences and 1/3 real different results. From >> discussions with the technical knowledgeable people I worked with at the >> time, I recall that there are three levels/sources for those differences: >> 1) computational (hardware) platform >> 2) compilers (+ optimization settings) >> 3) libraries (floating point handling does matter) >> Assuming you would like to compare the speed of the platforms wrt NONMEM, my >> advice would be to test a large series of different models, from simple >> ADVAN1 or 2 to complex ODE, ranging from FO to LAPLACIAN INT NUMERICAL, >> while keeping compilers and libraries the same. Also small and large >> datasets, as in some instances you might be testing only the L1/L2/L3 cache >> strategies and Turbo settings. And with and without parallelization - as >> that might determine runtime bottlenecks in practice. >> Just having a peek at Epyc - seems interesting (noticed results w gcc7.4 >> compilation). As long as you are able to hold the computation in cache, a >> big if for the 64-core, there might be an advantage. >> All in all I am not sure that it is worth the trouble. For any given PK-PD >> model there is a lot you can tune to gain speed, but the optimal settings >> might be very different for the next and overrule any platform differences. >> Hope this helps, >> Jeroen >> http://pd-value.com >> [email protected] >> @PD_value >> +31 6 23118438 >> -- More value out of your data! >>> On 18/11/19 6:34 pm, Leonid Gibiansky wrote: >>> Thanks Bob and Peter! >>> >>> The model is quite stable, but this is LAPLACIAN, so requires second >>> derivatives. At iteration 0, gradients differ by about 50 to 100% between >>> Intel and AMD. This leads to differences in minimization path, and slightly >>> different results. Not that different to change the recommended dose, but >>> sufficiently different to notice (OF difference of 6 points; 50% more model >>> evaluations to get to convergence). >>> Thanks >>> Leonid >>> >>> >>> >>> On 11/18/2019 12:15 PM, Bonate, Peter wrote: >>>> Leonid - when you say different. What do you mean? Fixed effect and >>>> random effects? Different OFV? >>>> >>>> We did a poster at AAPS a decade or so ago comparing results across >>>> different platforms using the same data and model. We got different >>>> results on the standard errors (which related to matrix inversion and how >>>> those are done using software-hardware configurations). And with >>>> overparameterized models we got different error messages - some platforms >>>> converged with no problem while some did not converge and gave R matrix >>>> singularity. >>>> >>>> Did your problems go beyond this? >>>> >>>> pete >>>> >>>> >>>> >>>> Peter Bonate, PhD >>>> Executive Director >>>> Pharmacokinetics, Modeling, and Simulation >>>> Astellas >>>> 1 Astellas Way, N3.158 >>>> Northbrook, IL 60062 >>>> [email protected] >>>> (224) 205-5855 >>>> >>>> >>>> >>>> Details are irrelevant in terms of decision making - Joe Biden. >>>> >>>> >>>> >>>> >>>> >>>> >>>> -----Original Message----- >>>> From: [email protected] <[email protected]> On >>>> Behalf Of Leonid Gibiansky >>>> Sent: Monday, November 18, 2019 11:05 AM >>>> To: nmusers <[email protected]> >>>> Subject: [NMusers] AMD vs Intel >>>> >>>> Dear All, >>>> >>>> I am testing the new Epyc processors from AMD (comparing with Intel Xeon), >>>> and getting different results. Just wondering whether anybody faced the >>>> problem of differences between AMD and Intel processors and knows how to >>>> solve it. I am using Intel compiler but ready to switch to gfortran or >>>> anything else if this would help to get identical results. >>>> There were reports of Intel slowing the AMD execution in the past, but in >>>> my tests, speed is comparable but the results differ. >>>> >>>> Thanks >>>> Leonid >>>> >>>> >>>> >>>

On 2019-11-18 23:54, Leonid Gibiansky wrote: Hi Jeroin, Thanks for your input, very interesting. As far as the goal is concerned, I am mostly interested to find options that would give identical results on two platform rather than in speed. So far no luck: 4 combinations of gfortran / Intel compilers on Xeon / AMD processors give 4 sets of results that are close but not identical. Related question to the group: have anybody experimented with gfortran options (rather than using default provided by Nonmem distribution)? Any recommendations? Same goal: maximum reproducibility across different OSs, parallelization options, and processor types. Thanks Leonid On 11/18/2019 5:28 PM, Jeroen Elassaiss-Schaap (PD-value B.V.) wrote: Hi Leonid, "A while" back we compared model development trajectories and results between two computational platforms, Itanium and Xeon, see https://www.page-meeting.org/?abstract=1188. The results roughly were: 1/3 equal, 1/3 rounding differences and 1/3 real different results. From discussions with the technical knowledgeable people I worked with at the time, I recall that there are three levels/sources for those differences: 1) computational (hardware) platform 2) compilers (+ optimization settings) 3) libraries (floating point handling does matter) Assuming you would like to compare the speed of the platforms wrt NONMEM, my advice would be to test a large series of different models, from simple ADVAN1 or 2 to complex ODE, ranging from FO to LAPLACIAN INT NUMERICAL, while keeping compilers and libraries the same. Also small and large datasets, as in some instances you might be testing only the L1/L2/L3 cache strategies and Turbo settings. And with and without parallelization - as that might determine runtime bottlenecks in practice. Just having a peek at Epyc - seems interesting (noticed results w gcc7.4 compilation). As long as you are able to hold the computation in cache, a big if for the 64-core, there might be an advantage. All in all I am not sure that it is worth the trouble. For any given PK-PD model there is a lot you can tune to gain speed, but the optimal settings might be very different for the next and overrule any platform differences. Hope this helps, Jeroen http://pd-value.com [email protected]<mailto:[email protected]> @PD_value +31 6 23118438 -- More value out of your data! On 18/11/19 6:34 pm, Leonid Gibiansky wrote: Thanks Bob and Peter! The model is quite stable, but this is LAPLACIAN, so requires second derivatives. At iteration 0, gradients differ by about 50 to 100% between Intel and AMD. This leads to differences in minimization path, and slightly different results. Not that different to change the recommended dose, but sufficiently different to notice (OF difference of 6 points; 50% more model evaluations to get to convergence). Thanks Leonid On 11/18/2019 12:15 PM, Bonate, Peter wrote: Leonid - when you say different. What do you mean? Fixed effect and random effects? Different OFV? We did a poster at AAPS a decade or so ago comparing results across different platforms using the same data and model. We got different results on the standard errors (which related to matrix inversion and how those are done using software-hardware configurations). And with overparameterized models we got different error messages - some platforms converged with no problem while some did not converge and gave R matrix singularity. Did your problems go beyond this? pete Peter Bonate, PhD Executive Director Pharmacokinetics, Modeling, and Simulation Astellas 1 Astellas Way, N3.158 Northbrook, IL 60062 [email protected]<mailto:[email protected]> (224) 205-5855 Details are irrelevant in terms of decision making - Joe Biden. -----Original Message----- From: [email protected]<mailto:[email protected]> <[email protected]><mailto:[email protected]> On Behalf Of Leonid Gibiansky Sent: Monday, November 18, 2019 11:05 AM To: nmusers <[email protected]><mailto:[email protected]> Subject: [NMusers] AMD vs Intel Dear All, I am testing the new Epyc processors from AMD (comparing with Intel Xeon), and getting different results. Just wondering whether anybody faced the problem of differences between AMD and Intel processors and knows how to solve it. I am using Intel compiler but ready to switch to gfortran or anything else if this would help to get identical results. There were reports of Intel slowing the AMD execution in the past, but in my tests, speed is comparable but the results differ. Thanks Leonid När du har kontakt med oss på Uppsala universitet med e-post så innebär det att vi behandlar dina personuppgifter. För att läsa mer om hur vi gör det kan du läsa här: http://www.uu.se/om-uu/dataskydd-personuppgifter/ E-mailing Uppsala University means that we will process your personal data. For more information on how this is performed, please read here: http://www.uu.se/en/about-uu/data-protection-policy

On 11/19/2019 4:32 AM, Rikard Nordgren wrote: > Hi Leonid, > > When upgrading from gfortran 4.4.7 to 5.1.1 we ran around 20 models with > both compilers and turning off the -ffast-math. The runs where on the > same hardware. The differences in the parameter estimates and OFV were > in general small. One big difference we could see was that the success > of the covariance step was seemingly random. It could succeed on one > compiler version, but not the other and it could also start failing when > the option was turned off. I have kept the runs, so let me know if you > would be interested. I also started some experiments using machine > dependent compiler flags, but as our cluster is heterogeneous I > abandoned this testing. > > I think that getting identical results could be possible, but that it > would be quite a challenge. There are many components that affect the > results. The compiler, the compiler flags, the libc implementation, the > hardware and sometimes the operating system. To see for example where > the standard libraries comes into play you can do nm nonmem on the > nonmem executable (in linux) to list all symbols compiled in. Some are > function from external libraries, for example my exponential function is > from libc: exp > from text strings could introduce rounding errors since the text > representation is decimal and the internal floating point number is binary. > > Best regards, > Rikard Nordgren > > -- > Rikard Nordgren > Systems developer > > Dept of Pharmaceutical Biosciences > Faculty of Pharmacy > Uppsala University > Box 591 > 75124 Uppsala > > Phone: +46 18 4714308 > www.farmbio.uu.se/research/researchgroups/pharmacometrics/ > > > > > On 2019-11-18 23:54, Leonid Gibiansky wrote: >> Hi Jeroin, >> >> Thanks for your input, very interesting. As far as the goal is >> concerned, I am mostly interested to find options that would give >> identical results on two platform rather than in speed. So far no >> luck: 4 combinations of gfortran / Intel compilers on Xeon / AMD >> processors give 4 sets of results that are close but not identical. >> >> Related question to the group: have anybody experimented with gfortran >> options (rather than using default provided by Nonmem distribution)? >> Any recommendations? Same goal: maximum reproducibility across >> different OSs, parallelization options, and processor types. >> >> Thanks >> Leonid >> >> >> >> >> On 11/18/2019 5:28 PM, Jeroen Elassaiss-Schaap (PD-value B.V.) wrote: >>> Hi Leonid, >>> >>> "A while" back we compared model development trajectories and results >>> between two computational platforms, Itanium and Xeon, see >>> https://www.page-meeting.org/?abstract=1188. The results roughly >>> were: 1/3 equal, 1/3 rounding differences and 1/3 real different >>> results. From discussions with the technical knowledgeable people I >>> worked with at the time, I recall that there are three levels/sources >>> for those differences: >>> >>> 1) computational (hardware) platform >>> >>> 2) compilers (+ optimization settings) >>> >>> 3) libraries (floating point handling does matter) >>> >>> Assuming you would like to compare the speed of the platforms wrt >>> NONMEM, my advice would be to test a large series of different >>> models, from simple ADVAN1 or 2 to complex ODE, ranging from FO to >>> LAPLACIAN INT NUMERICAL, while keeping compilers and libraries the >>> same. Also small and large datasets, as in some instances you might >>> be testing only the L1/L2/L3 cache strategies and Turbo settings. And >>> with and without parallelization - as that might determine runtime >>> bottlenecks in practice. >>> >>> Just having a peek at Epyc - seems interesting (noticed results w >>> gcc7.4 compilation). As long as you are able to hold the computation >>> in cache, a big if for the 64-core, there might be an advantage. >>> >>> All in all I am not sure that it is worth the trouble. For any given >>> PK-PD model there is a lot you can tune to gain speed, but the >>> optimal settings might be very different for the next and overrule >>> any platform differences. >>> >>> Hope this helps, >>> >>> Jeroen >>> >>> http://pd-value.com >>> jeroen >>> >>> +31 6 23118438 >>> -- More value out of your data! >>> >>> On 18/11/19 6:34 pm, Leonid Gibiansky wrote: >>>> Thanks Bob and Peter! >>>> >>>> The model is quite stable, but this is LAPLACIAN, so requires second >>>> derivatives. At iteration 0, gradients differ by about 50 to 100% >>>> between Intel and AMD. This leads to differences in minimization >>>> path, and slightly different results. Not that different to change >>>> the recommended dose, but sufficiently different to notice (OF >>>> difference of 6 points; 50% more model evaluations to get to >>>> convergence). >>>> Thanks >>>> Leonid >>>> >>>> >>>> >>>> On 11/18/2019 12:15 PM, Bonate, Peter wrote: >>>>> Leonid - when you say different. What do you mean? Fixed effect >>>>> and random effects? Different OFV? >>>>> >>>>> We did a poster at AAPS a decade or so ago comparing results across >>>>> different platforms using the same data and model. We got >>>>> different results on the standard errors (which related to matrix >>>>> inversion and how those are done using software-hardware >>>>> configurations). And with overparameterized models we got different >>>>> error messages - some platforms converged with no problem while >>>>> some did not converge and gave R matrix singularity. >>>>> >>>>> Did your problems go beyond this? >>>>> >>>>> pete >>>>> >>>>> >>>>> >>>>> Peter Bonate, PhD >>>>> Executive Director >>>>> Pharmacokinetics, Modeling, and Simulation >>>>> Astellas >>>>> 1 Astellas Way, N3.158 >>>>> Northbrook, IL 60062 >>>>> Peter.bonate >>>>> (224) 205-5855 >>>>> >>>>> >>>>> >>>>> Details are irrelevant in terms of decision making - Joe Biden. >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> >>>>> -----Original Message----- >>>>> From: owner-nmusers >>>>> On Behalf Of Leonid Gibiansky >>>>> Sent: Monday, November 18, 2019 11:05 AM >>>>> To: nmusers <nmusers >>>>> Subject: [NMusers] AMD vs Intel >>>>> >>>>> Dear All, >>>>> >>>>> I am testing the new Epyc processors from AMD (comparing with Intel >>>>> Xeon), and getting different results. Just wondering whether >>>>> anybody faced the problem of differences between AMD and Intel >>>>> processors and knows how to solve it. I am using Intel compiler but >>>>> ready to switch to gfortran or anything else if this would help to >>>>> get identical results. >>>>> There were reports of Intel slowing the AMD execution in the past, >>>>> but in my tests, speed is comparable but the results differ. >>>>> >>>>> Thanks >>>>> Leonid >>>>> >>>>> >>>>> >>>> >> > > Page Title > > > > > > > > När du har kontakt med oss på Uppsala universitet med e-post så innebär > det att vi behandlar dina personuppgifter. För att läsa mer om hur vi > gör det kan du läsa här: http://www.uu.se/om-uu/dataskydd-personuppgifter/ > > E-mailing Uppsala University means that we will process your personal > data. For more information on how this is performed, please read here: > http://www.uu.se/en/about-uu/data-protection-policy

On 11/19/2019 4:32 AM, Rikard Nordgren wrote: > Hi Leonid, > > When upgrading from gfortran 4.4.7 to 5.1.1 we ran around 20 models with both compilers and turning off the -ffast-math. The runs where on the same hardware. The differences in the parameter estimates and OFV were in general small. One big difference we could see was that the success of the covariance step was seemingly random. It could succeed on one compiler version, but not the other and it could also start failing when the option was turned off. I have kept the runs, so let me know if you would be interested. I also started some experiments using machine dependent compiler flags, but as our cluster is heterogeneous I abandoned this testing. > > I think that getting identical results could be possible, but that it would be quite a challenge. There are many components that affect the results. The compiler, the compiler flags, the libc implementation, the hardware and sometimes the operating system. To see for example where the standard libraries comes into play you can do nm nonmem on the nonmem executable (in linux) to list all symbols compiled in. Some are function from external libraries, for example my exponential function is from libc: exp@@GLIBC_2.2.5 . Even the functions that read in numbers from text strings could introduce rounding errors since the text representation is decimal and the internal floating point number is binary. > > Best regards, > Rikard Nordgren > > -- > Rikard Nordgren > Systems developer > > Dept of Pharmaceutical Biosciences > Faculty of Pharmacy > Uppsala University > Box 591 > 75124 Uppsala > > Phone: +46 18 4714308 > www.farmbio.uu.se/research/researchgroups/pharmacometrics/ > > On 2019-11-18 23:54, Leonid Gibiansky wrote: > > > Hi Jeroin, > > > > Thanks for your input, very interesting. As far as the goal is concerned, I am mostly interested to find options that would give identical results on two platform rather than in speed. So far no luck: 4 combinations of gfortran / Intel compilers on Xeon / AMD processors give 4 sets of results that are close but not identical. > > > > Related question to the group: have anybody experimented with gfortran options (rather than using default provided by Nonmem distribution)? Any recommendations? Same goal: maximum reproducibility across different OSs, parallelization options, and processor types. > > > > Thanks > > Leonid > > > > On 11/18/2019 5:28 PM, Jeroen Elassaiss-Schaap (PD-value B.V.) wrote: > > > > > Hi Leonid, > > > > > > "A while" back we compared model development trajectories and results between two computational platforms, Itanium and Xeon, see https://www.page-meeting.org/?abstract=1188 . The results roughly were: 1/3 equal, 1/3 rounding differences and 1/3 real different results. From discussions with the technical knowledgeable people I worked with at the time, I recall that there are three levels/sources for those differences: > > > > > > 1) computational (hardware) platform > > > > > > 2) compilers (+ optimization settings) > > > > > > 3) libraries (floating point handling does matter) > > > > > > Assuming you would like to compare the speed of the platforms wrt NONMEM, my advice would be to test a large series of different models, from simple ADVAN1 or 2 to complex ODE, ranging from FO to LAPLACIAN INT NUMERICAL, while keeping compilers and libraries the same. Also small and large datasets, as in some instances you might be testing only the L1/L2/L3 cache strategies and Turbo settings. And with and without parallelization - as that might determine runtime bottlenecks in practice. > > > > > > Just having a peek at Epyc - seems interesting (noticed results w gcc7.4 compilation). As long as you are able to hold the computation in cache, a big if for the 64-core, there might be an advantage. > > > > > > All in all I am not sure that it is worth the trouble. For any given PK-PD model there is a lot you can tune to gain speed, but the optimal settings might be very different for the next and overrule any platform differences. > > > > > > Hope this helps, > > > > > > Jeroen > > > > > > http://pd-value.com > > > [email protected] > > > @PD_value > > > +31 6 23118438 > > > -- More value out of your data! > > > > > > On 18/11/19 6:34 pm, Leonid Gibiansky wrote: > > > > > > > Thanks Bob and Peter! > > > > > > > > The model is quite stable, but this is LAPLACIAN, so requires second derivatives. At iteration 0, gradients differ by about 50 to 100% between Intel and AMD. This leads to differences in minimization path, and slightly different results. Not that different to change the recommended dose, but sufficiently different to notice (OF difference of 6 points; 50% more model evaluations to get to convergence). > > > > > > > > Thanks > > > > Leonid > > > > > > > > On 11/18/2019 12:15 PM, Bonate, Peter wrote: > > > > > > > > > Leonid - when you say different. What do you mean? Fixed effect and random effects? Different OFV? > > > > > > > > > > We did a poster at AAPS a decade or so ago comparing results across different platforms using the same data and model. We got different results on the standard errors (which related to matrix inversion and how those are done using software-hardware configurations). And with overparameterized models we got different error messages - some platforms converged with no problem while some did not converge and gave R matrix singularity. > > > > > > > > > > Did your problems go beyond this? > > > > > > > > > > pete > > > > > > > > > > Peter Bonate, PhD > > > > > Executive Director > > > > > Pharmacokinetics, Modeling, and Simulation > > > > > Astellas > > > > > 1 Astellas Way, N3.158 > > > > > Northbrook, IL 60062 > > > > > [email protected] > > > > > (224) 205-5855 > > > > > > > > > > Details are irrelevant in terms of decision making - Joe Biden. > > > > > > > > > > -----Original Message----- > > > > > > > > > > From: [email protected] < [email protected] > On Behalf Of Leonid Gibiansky > > > > > > > > > > Sent: Monday, November 18, 2019 11:05 AM > > > > > To: nmusers <[email protected]> > > > > > Subject: [NMusers] AMD vs Intel > > > > > > > > > > Dear All, > > > > > > > > > > I am testing the new Epyc processors from AMD (comparing with Intel Xeon), and getting different results. Just wondering whether anybody faced the problem of differences between AMD and Intel processors and knows how to solve it. I am using Intel compiler but ready to switch to gfortran or anything else if this would help to get identical results. There were reports of Intel slowing the AMD execution in the past, but in my tests, speed is comparable but the results differ. > > > > > > > > > > Thanks > > > > > Leonid > > Page Title > > När du har kontakt med oss på Uppsala universitet med e-post så innebär det att vi behandlar dina personuppgifter. För att läsa mer om hur vi gör det kan du läsa här: http://www.uu.se/om-uu/dataskydd-personuppgifter/ > > E-mailing Uppsala University means that we will process your personal data. For more information on how this is performed, please read here: http://www.uu.se/en/about-uu/data-protection-policy