[R-SIG-Finance] Matlab vs. R performance/convenience benchmarking for quantitative analysis business

Wed Oct 6 21:24:44 CEST 2010

Eclipse+StatEt is probably the best one, but unfortunately it is still missing a debugger, profiler, and even object inspection inside functions.

I tested ATLAS and Goto on Linux 64 and I couldn't match the performance of Revolution-R.  I guess credit shall go to Intel's compiler suite and Mkl, but that said, I once tried to compile R using Intel's suite and I gave up out of boredom after a day trying to get compiler suite installing correctly. I can easily imagine that budget-insensitive people may be more than willing to pay for the support.

On Oct 6, 2010, at 2:28 PM, Tobias Verbeke <tobias.verbeke at openanalytics.eu> wrote:

> L.S.
> 
> 1) For a good IDE one can use Eclipse with the StatET plug-ins
> for R development (which, moreover, is cross-platform);
> 
> 2) It is perfectly possible to use an optimized open source BLAS with
> R such as Atlas, so there are many more Optimized R versions than the
> Revolution marketing wants one to believe (and it is not at all
> necessary to get trapped in closed source environments to have
> equivalent tools).
> 
> Best,
> Tobias
> 
> On 10/06/2010 08:02 PM, Eric Zivot wrote:
>> Something in between free R and Matlab to consider is RevolutionR (I don't
>> work for them, please don't flame me). It uses optimized math libraries, has
>> a very nice IDE and is set-up for parallel computing out-of-the box. I don't
>> know what their corporate pricing is but it has to be cheaper than Matlab.
>> Below is a comparison (taken from the Revolution documentation) between the
>> optimized math libraries and the standard R libraries for certain
>> computations:
>> 
>> Calculation            Regular R            Optimized
>> libraries
>> Eigen                11.45                3.63
>> Svd                7.34                1.19
>> Qr                1.17                0.93
>> Lm                1.47                1.23
>> Matrix mult            1.75                0.09
>> 
>> Note: Mean times for various calculations below
>> 
>> set.seed(14)
>> x<- matrix(rnorm(1000000),nrow=1000)
>> xout<- numeric(20)
>> for (i in 1:20) xout[i]<- system.time(eigen(x))[3]
>> xout2<- numeric(20)
>> for (i in 1:20) xout2[i]<- system.time(svd(x))[3]
>> xout3<- numeric(20)
>> for (i in 1:20) xout3[i]<- system.time(qr(x))[3]
>> xout4<- numeric(20)
>> for (i in 1:20) xout4[i]<- system.time(lm(x[,i]~x[,-i]))[3]
>> xout5<- numeric(20)
>> for (i in 1:20) xout5[i]<- system.time(t(x)%*%x)[3]
>> 
>> 
>> 
>> Eric Zivot                              
>> Robert Richards Chaired Professor of Economics
>> Adjunct Professor of Finance
>> Adjunct Professor of Statistics
>> Department of Economics
>> Box 353330                  email:  ezivot at u.washington.edu
>> University of Washington    phone:  206-543-6715
>> Seattle, WA 98195-3330
>> www:  http://faculty.washington.edu/ezivot
>> 
>> 
>> -----Original Message-----
>> From: r-sig-finance-bounces at stat.math.ethz.ch
>> [mailto:r-sig-finance-bounces at stat.math.ethz.ch] On Behalf Of
>> fjpcaballero at gmail.com
>> Sent: Wednesday, October 06, 2010 9:38 AM
>> To:<Samuel.Meichtry at bkw-fmb.ch>
>> Cc:<r-sig-finance at stat.math.ethz.ch>
>> Subject: Re: [R-SIG-Finance] Matlab vs. R performance/convenience
>> benchmarking for quantitative analysis business
>> 
>> Note that this benchmark appears to be using non-optimized BLAS libraries.
>> Speed of matrix operations should be comparable to Matlab when using ATLAS,
>> Intel mkl, vecLib, or Goto.  Loops are a different story.  Even with Ra and
>> JIT, R is a far cry from Matlab.
>> 
>> My personal take:
>> 
>> Pro Matlab: compact and clean syntax, good IDE and tooling, more efficient
>> (e.g. pass by reference, loop optimization), some very good toolboxes not
>> available elsewhere (eg CVX, Murphy's BBN toolbox)
>> 
>> Against Matlab: it is a pain to work with anything that doesn't look like a
>> matrix; lags behind R in methods available, licensing costs
>> 
>> Pro R: (much) broader selection of methods -- at least in machine learning,
>> survival analysis, and finance; named arguments in functions, easier to work
>> with mixed data (eg dataframes), better statistics-oriented graphics (e.g.
>> Lattice, ggplot), better interactive graphics [for my taste] (rggobi,
>> iplots), licensing costs
>> 
>> Against R: performance issues previously discussed, pretty ugly code, lack
>> of a good IDE -- all the ones I have tested lack an integrated debugger and
>> profiler, steep learning curve.
>> 
>> 
>> 
>> On Oct 6, 2010, at 9:08 AM,<Samuel.Meichtry at bkw-fmb.ch>  wrote:
>> 
>>> Hello everyone,
>>> 
>>> At the moment we are trying to decide which software to use for
>> quantitative analysis and the following Link about performance doesn't
>> really vote for R (http://mlg.eng.cam.ac.uk/dave/rmbenchmark.php).
>>> 
>>> I am not against Matlab and cannot say anything about its convenience
>> because I haven't worked with it for a long time. On the other hand Matlab
>> is already heavily used in other departments in our company and we have to
>> tell our project committee why we would like to use R instead.
>>> 
>>> Now I am very interested to know why you are using R in your business and
>> not Matlab?
>>> Or do you use both of them?
>>> 
>>> Thank you very much for your feedback!
>>> 
>>> 
>>> Kind regards,
>>> Samuel Meichtry
>>> 
>>> 
>>> ____________________________________________
>>> BKW FMB Energie AG
>>> Energy Trading
>>> Samuel Meichtry
>>> Analyst Energy Trading
>>> Tel +41 31 330 53 99
>>> Fax +41 31 330 56 16
>>> e-mail samuel.meichtry at bkw-fmb.ch
>>> 
>>> *  Sie drucken dieses E-Mail nicht aus? Die Umwelt dankt!
>>> 
>>> 
>>> 
>>> 
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> 