# Stan on computing cluster: strange results

**URL:** <https://discourse.mc-stan.org/t/stan-on-computing-cluster-strange-results/4430>\
**Category:** CmdStan\
**Created:** [June 5, 2018, 2:19pm UTC](https://discourse.mc-stan.org/t/stan-on-computing-cluster-strange-results/4430 "2018-06-05T14:19:10Z")\
**Posts on this page:** 12\
**Page:** 1

<div class="post-metadata">

**Author:** ![fabio](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.mc-stan.org/fabio/32/935_2.png) [@fabio](https://discourse.mc-stan.org/u/fabio)\
**Post date:** [June 5, 2018, 2:19pm UTC](https://discourse.mc-stan.org/t/stan-on-computing-cluster-strange-results/4430/1 "2018-06-05T14:19:10Z")

</div>

Hi everyone,  
_DISCLAIMER: long post here_  
I made some experiments to move some very time consuming algorithms on the computing cluster available to my company.  
Just to understand how to use queues etc… I started with my simplest model an ordered logistic regression with N=6497 samples, 11 predictors, 7 different scores. This model will evolve to a hierarchical formulation, but so far it is just a straightforward copy-paste form the Stan reference manual (v.2.17.0, page 138). No priors specified: it is just as in the book.

```
data {
    int<lower=2> K;
    int<lower=0> N;
    int<lower=1> D;
    int<lower=1,upper=K> y[N];
    row_vector[D] x[N];
}

parameters {
    vector[D] beta;
    ordered[K-1] c;
}

model {
 
    for (n in 1:N)
        y[n] ~ ordered_logistic(x[n] * beta, c);
}

```

I started to try it on my own dektop an 8 processors Intel® Xeon® CPU E31245 @ 3.30GHz with 8GB RAM.  
**rstan** with just one chain, 2000 iteration with given seed: 12345 gave this results

```
Elapsed Time: 972.488 seconds (Warm-up)
               1088.72 seconds (Sampling)
               2061.21 seconds (Total)
Inference for Stan model: orderedLogistic.                                                                                                                                                                                                                                                                                                           
1 chains, each with iter=2000; warmup=1000; thin=1;                                                                                                                                                                                                                                                                                                  
post-warmup draws per chain=1000, total post-warmup draws=1000.                                                                                                                                                                                                                                                                                      
                                                                                                                                                                                                                                                                                                                                                     
             mean se_mean sd 2.5% 25% 50% 75% 97.5%                                                                                                                                                                                                                                                                          
beta[1] 0.04 0.00 0.02 -0.01 0.02 0.04 0.05 0.08                                                                                                                                                                                                                                                                          
beta[2] -3.87 0.01 0.20 -4.26 -4.00 -3.87 -3.73 -3.49
beta[3] -0.27 0.01 0.20 -0.65 -0.40 -0.27 -0.14 0.12
beta[4] 0.06 0.00 0.01 0.05 0.05 0.06 0.06 0.07
beta[5] -1.27 0.02 0.63 -2.55 -1.72 -1.26 -0.85 -0.09
beta[6] 0.02 0.00 0.00 0.01 0.02 0.02 0.02 0.02
beta[7] -0.01 0.00 0.00 -0.01 -0.01 -0.01 -0.01 0.00
beta[8] -0.10 0.04 1.00 -2.23 -0.75 -0.11 0.60 1.79
beta[9] 0.53 0.01 0.17 0.20 0.41 0.52 0.65 0.87
beta[10] 1.65 0.01 0.18 1.31 1.53 1.65 1.76 2.01
beta[11] 0.90 0.00 0.03 0.85 0.88 0.90 0.92 0.95
c[1] 4.70 0.06 1.25 2.14 3.83 4.69 5.59 7.08
c[2] 6.91 0.06 1.24 4.40 6.07 6.90 7.78 9.25
c[3] 10.08 0.06 1.24 7.55 9.25 10.09 10.96 12.43
c[4] 12.67 0.06 1.25 10.13 11.83 12.68 13.54 15.06
c[5] 15.00 0.06 1.25 12.47 14.15 14.98 15.87 17.46
c[6] 18.86 0.06 1.33 16.29 17.92 18.87 19.75 21.46
lp__ -7106.86 0.15 2.83 -7113.57 -7108.57 -7106.56 -7104.87 -7102.19
         n_eff Rhat
beta[1] 665 1
beta[2] 592 1
beta[3] 599 1
beta[4] 1000 1
beta[5] 1000 1
beta[6] 1000 1
beta[7] 1000 1
beta[8] 648 1
beta[9] 543 1
beta[10] 793 1
beta[11] 660 1
c[1] 431 1
c[2] 435 1
c[3] 434 1
c[4] 433 1
c[5] 433 1
c[6] 448 1
lp__ 381 1

Samples were drawn using NUTS(diag_e) at Tue Jun 5 13:33:19 2018.
For each parameter, n_eff is a crude measure of effective sample size,
and Rhat is the potential scale reduction factor on split chains (at 
convergence, Rhat=1).

```

Rhat is 1 for all parameters, n\_eff is quite high, no divergences.

I tried the same model ON my machine, on the same data, with the same seed with **CmdStan** gave

```
Inference for Stan model: orderedLogistic_model
1 chains: each with iter=(1000); warmup=(0); thin=(1); 1000 iterations saved.

Warmup took (1165) seconds, 19 minutes total
Sampling took (929) seconds, 15 minutes total

                    Mean MCSE StdDev 5% 50% 95% N_Eff N_Eff/s R_hat
lp__ -7.1e+03 1.3e-01 2.9e+00 -7.1e+03 -7.1e+03 -7.1e+03 493 5.3e-01 1.0e+00
accept_stat__ 9.4e-01 2.4e-03 7.7e-02 7.9e-01 9.8e-01 1.0e+00 1000 1.1e+00 1.0e+00
stepsize__ 1.3e-02 5.6e-17 4.0e-17 1.3e-02 1.3e-02 1.3e-02 0.50 5.4e-04 1.0e+00
treedepth__ 8.0e+00 6.0e-03 1.9e-01 8.0e+00 8.0e+00 8.0e+00 1000 1.1e+00 1.0e+00
n_leapfrog__ 2.8e+02 2.5e+00 7.6e+01 2.6e+02 2.6e+02 5.1e+02 946 1.0e+00 1.0e+00
divergent__ 0.0e+00 0.0e+00 0.0e+00 0.0e+00 0.0e+00 0.0e+00 1000 1.1e+00 -nan
energy__ 7.1e+03 2.2e-01 4.1e+00 7.1e+03 7.1e+03 7.1e+03 325 3.5e-01 1.0e+00
beta[1] 3.4e-02 8.6e-04 2.5e-02 -7.6e-03 3.5e-02 7.5e-02 828 8.9e-01 1.0e+00
beta[2] -3.9e+00 7.1e-03 1.9e-01 -4.2e+00 -3.9e+00 -3.5e+00 719 7.7e-01 1.0e+00
beta[3] -2.6e-01 7.5e-03 2.0e-01 -5.9e-01 -2.6e-01 8.9e-02 743 8.0e-01 1.0e+00
beta[4] 5.8e-02 1.9e-04 5.9e-03 4.9e-02 5.8e-02 6.8e-02 1000 1.1e+00 1.0e+00
beta[5] -1.4e+00 2.1e-02 6.4e-01 -2.4e+00 -1.4e+00 -2.9e-01 943 1.0e+00 1.0e+00
beta[6] 1.8e-02 6.4e-05 2.0e-03 1.5e-02 1.8e-02 2.1e-02 1000 1.1e+00 1.0e+00
beta[7] -6.3e-03 2.3e-05 7.3e-04 -7.5e-03 -6.3e-03 -5.1e-03 1000 1.1e+00 1.0e+00
beta[8] -1.4e-01 3.8e-02 1.0e+00 -1.8e+00 -1.2e-01 1.5e+00 758 8.2e-01 1.0e+00
beta[9] 5.1e-01 5.8e-03 1.7e-01 2.1e-01 5.2e-01 7.8e-01 866 9.3e-01 1.0e+00
beta[10] 1.7e+00 5.8e-03 1.8e-01 1.4e+00 1.7e+00 2.0e+00 1000 1.1e+00 1.0e+00
beta[11] 9.0e-01 9.3e-04 2.6e-02 8.6e-01 9.0e-01 9.4e-01 796 8.6e-01 1.0e+00
c[1] 4.6e+00 5.2e-02 1.2e+00 2.5e+00 4.6e+00 6.6e+00 544 5.9e-01 1.0e+00
c[2] 6.8e+00 5.1e-02 1.2e+00 4.8e+00 6.8e+00 8.8e+00 554 6.0e-01 1.0e+00
c[3] 1.0e+01 5.2e-02 1.2e+00 7.9e+00 1.0e+01 1.2e+01 546 5.9e-01 1.0e+00
c[4] 1.3e+01 5.2e-02 1.2e+00 1.1e+01 1.3e+01 1.5e+01 544 5.9e-01 1.0e+00
c[5] 1.5e+01 5.2e-02 1.2e+00 1.3e+01 1.5e+01 1.7e+01 549 5.9e-01 1.0e+00
c[6] 1.9e+01 5.5e-02 1.3e+00 1.7e+01 1.9e+01 2.1e+01 560 6.0e-01 1.0e+00

Samples were drawn using hmc with nuts.
For each parameter, N_Eff is a crude measure of effective sample size,
and R_hat is the potential scale reduction factor on split chains (at 
convergence, R_hat=1).

```

So basically same time to run (more or less), same results.

**BUT** when i run the same model on the same data, same iteration number, some seed, 8GB memory with **CmdStan** built by myself on the **cluster** , this is what I get

```
Inference for Stan model: orderedLogistic_model
1 chains: each with iter=(1000); warmup=(0); thin=(1); 1000 iterations saved.

Warmup took (2034) seconds, 34 minutes total
Sampling took (3813) seconds, 1.1 hours total
                                                                                                                                                                                                                                                                               
                    Mean MCSE StdDev 5% 50% 95% N_Eff N_Eff/s R_hat                                                                                                                                                                              
lp__ -7.1e+03 3.9e-01 3.0e+00 -7.1e+03 -7.1e+03 -7.1e+03 58 1.5e-02 1.0e+00                                                                                                                                                                              
accept_stat__ 9.3e-01 3.1e-03 9.7e-02 7.3e-01 9.7e-01 1.0e+00 1000 2.6e-01 1.0e+00                                                                                                                                                                              
stepsize__ 4.4e-04 3.1e-18 2.2e-18 4.4e-04 4.4e-04 4.4e-04 0.50 1.3e-04 1.0e+00                                                                                                                                                                              
treedepth__ 1.0e+01 1.2e-15 3.7e-14 1.0e+01 1.0e+01 1.0e+01 1000 2.6e-01 1.0e+00                                                                                                                                                                              
n_leapfrog__ 1.0e+03 7.9e-14 2.5e-12 1.0e+03 1.0e+03 1.0e+03 1000 2.6e-01 1.0e+00                                                                                                                                                                              
divergent__ 0.0e+00 0.0e+00 0.0e+00 0.0e+00 0.0e+00 0.0e+00 1000 2.6e-01 -nan                                                                                                                                                                              
energy__ 7.1e+03 4.0e-01 4.2e+00 7.1e+03 7.1e+03 7.1e+03 108 2.8e-02 1.0e+00                                                                                                                                                                              
beta[1] 2.1e-01 1.4e-02 4.1e-02 1.4e-01 2.1e-01 2.7e-01 8.8 2.3e-03 1.6e+00                                                                                                                                                                              
beta[2] -3.6e+00 4.1e-02 2.0e-01 -3.9e+00 -3.6e+00 -3.2e+00 23 6.1e-03 1.0e+00                                                                                                                                                                              
beta[3] -3.0e-01 5.3e-02 2.0e-01 -6.4e-01 -2.9e-01 1.3e-02 15 3.8e-03 1.0e+00                                                                                                                                                                              
beta[4] 1.2e-01 3.9e-03 1.2e-02 1.0e-01 1.2e-01 1.4e-01 9.2 2.4e-03 1.6e+00                                                                                                                                                                              
beta[5] -1.4e+00 2.1e-01 8.7e-01 -2.7e+00 -1.6e+00 1.4e-01 17 4.4e-03 1.1e+00                                                                                                                                                                              
beta[6] 1.8e-02 2.9e-04 2.2e-03 1.5e-02 1.8e-02 2.2e-02 56 1.5e-02 1.0e+00                                                                                                                                                                              
beta[7] -7.2e-03 9.7e-05 7.4e-04 -8.3e-03 -7.2e-03 -6.0e-03 58 1.5e-02 1.0e+00                                                                                                                                                                              
beta[8] -1.6e+02 9.8e+00 2.6e+01 -2.1e+02 -1.6e+02 -1.2e+02 7.1 1.9e-03 1.7e+00                                                                                                                                                                              
beta[9] 1.3e+00 8.0e-02 2.1e-01 9.8e-01 1.3e+00 1.7e+00 6.8 1.8e-03 1.4e+00                                                                                                                                                                              
beta[10] 2.2e+00 6.0e-02 1.8e-01 1.9e+00 2.2e+00 2.4e+00 8.6 2.3e-03 1.2e+00                                                                                                                                                                              
beta[11] 7.0e-01 1.3e-02 3.6e-02 6.5e-01 7.0e-01 7.6e-01 8.2 2.1e-03 1.3e+00                                                                                                                                                                              
c[1] -1.6e+02 9.6e+00 2.6e+01 -2.0e+02 -1.5e+02 -1.1e+02 7.2 1.9e-03 1.7e+00                                                                                                                                                                              
c[2] -1.5e+02 9.6e+00 2.6e+01 -2.0e+02 -1.5e+02 -1.1e+02 7.2 1.9e-03 1.7e+00                                                                                                                                                                              
c[3] -1.5e+02 9.6e+00 2.6e+01 -1.9e+02 -1.5e+02 -1.1e+02 7.2 1.9e-03 1.7e+00                                                                                                                                                                              
c[4] -1.5e+02 9.6e+00 2.6e+01 -1.9e+02 -1.5e+02 -1.0e+02 7.2 1.9e-03 1.7e+00
c[5] -1.5e+02 9.6e+00 2.6e+01 -1.9e+02 -1.4e+02 -1.0e+02 7.2 1.9e-03 1.7e+00
c[6] -1.4e+02 9.5e+00 2.6e+01 -1.8e+02 -1.4e+02 -9.9e+01 7.3 1.9e-03 1.7e+00

Samples were drawn using hmc with nuts.
For each parameter, N_Eff is a crude measure of effective sample size,
and R_hat is the potential scale reduction factor on split chains (at 
convergence, R_hat=1).

```

I am not concerned that on the cluster took 1h 40 mins to run and on my computer (35 min on my local machine), I am really surprise to have my cutpoints collapsing, n\_eff incredibly small, and Rhats that shows bad convergence.

I am really puzzled. What is going on on the two different machines?

---

<div class="post-metadata">

**Author:** ![bgoodri](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.mc-stan.org/bgoodri/32/4451_2.png) [@bgoodri](https://discourse.mc-stan.org/u/bgoodri)\
**Post date:** [June 5, 2018, 3:05pm UTC](https://discourse.mc-stan.org/t/stan-on-computing-cluster-strange-results/4430/2 "2018-06-05T15:05:33Z")

</div>

> [@fabio](#):
>
> What is going on on the two different machines?

Guessing different compiler options.

---

<div class="post-metadata">

**Author:** ![fabio](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.mc-stan.org/fabio/32/935_2.png) [@fabio](https://discourse.mc-stan.org/u/fabio)\
**Post date:** [June 5, 2018, 3:19pm UTC](https://discourse.mc-stan.org/t/stan-on-computing-cluster-strange-results/4430/3 "2018-06-05T15:19:06Z")

</div>

@bgoodri: Thanks!  
oooook. this is a difficult and scary subject for me. Where can I see what the compiler options are on the 2 different machines?  
It is maybe related to the **rstan** procedure that sets:

```
CXXFLAGS=-O3 -mtune=native -march=native -Wno-unused-variable -Wno-unused-function -Wno-macro-redefined

```

---

<div class="post-metadata">

**Author:** ![bgoodri](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.mc-stan.org/bgoodri/32/4451_2.png) [@bgoodri](https://discourse.mc-stan.org/u/bgoodri)\
**Post date:** [June 5, 2018, 3:53pm UTC](https://discourse.mc-stan.org/t/stan-on-computing-cluster-strange-results/4430/4 "2018-06-05T15:53:49Z")

</div>

For **rstan** , yes. Or at least that is one of the Makefiles that gets sourced. The CmdStan equivalent is under make/local, which you may need to create if it does not exist. Also, your cluster may have `-ffast-math` or similar enabled by default, which is too sloppy to be useful for NUTS.

---

<div class="post-metadata">

**Author:** ![aaronjg](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.mc-stan.org/aaronjg/32/3768_2.png) [@aaronjg](https://discourse.mc-stan.org/u/aaronjg)\
**Post date:** [June 5, 2018, 6:31pm UTC](https://discourse.mc-stan.org/t/stan-on-computing-cluster-strange-results/4430/5 "2018-06-05T18:31:32Z")

</div>

There have also been some issues where the Rstan code was using a different equation to calculate Neff. You might want to pull from github:

> <https://github.com/stan-dev/rstan/pull/531>

---

<div class="post-metadata">

**Author:** ![sakrejda](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.mc-stan.org/sakrejda/32/845_2.png) [@sakrejda](https://discourse.mc-stan.org/u/sakrejda)\
**Post date:** [June 5, 2018, 6:39pm UTC](https://discourse.mc-stan.org/t/stan-on-computing-cluster-strange-results/4430/6 "2018-06-05T18:39:32Z")

</div>

I don’t think you should seet his much differences from compiler options, you’re sure the model versions, data, and control arguments all match?

---

<div class="post-metadata">

**Author:** ![fabio](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.mc-stan.org/fabio/32/935_2.png) [@fabio](https://discourse.mc-stan.org/u/fabio)\
**Post date:** [June 7, 2018, 3:07pm UTC](https://discourse.mc-stan.org/t/stan-on-computing-cluster-strange-results/4430/7 "2018-06-07T15:07:06Z")

</div>

Thanks all.  
@sakrejda : I double checked everything. model, data and program version are the same.  
So I moved to complier flags. Following both @bgoodri 's advices I set

```
 CXXFLAGS" = -O3 -mtune=native -march=native -Wno-unused-variable -Wno-macro-redefined -fno-fast-math 

```

now both times to run CmdStan and the final results are comparable on my machine and on the cluster.  
Here are the results on the cluster:

```
 Inference for Stan model: orderedLogistic_model
1 chains: each with iter=(1000); warmup=(0); thin=(1); 1000 iterations saved.

Warmup took (1216) seconds, 20 minutes total
Sampling took (995) seconds, 17 minutes total

                    Mean MCSE StdDev 5% 50% 95% N_Eff N_Eff/s R_hat
lp__ -7.1e+03 1.4e-01 3.0e+00 -7.1e+03 -7.1e+03 -7.1e+03 447 4.5e-01 1.0e+00
accept_stat__ 9.4e-01 2.7e-03 8.4e-02 7.4e-01 9.7e-01 1.0e+00 1000 1.0e+00 1.0e+00
stepsize__ 1.4e-02 4.9e-17 3.5e-17 1.4e-02 1.4e-02 1.4e-02 0.50 5.0e-04 1.0e+00
treedepth__ 8.0e+00 6.7e-03 2.1e-01 8.0e+00 8.0e+00 8.0e+00 1000 1.0e+00 1.0e+00
n_leapfrog__ 2.6e+02 1.7e+00 5.4e+01 2.6e+02 2.6e+02 2.6e+02 1000 1.0e+00 1.0e+00
divergent__ 0.0e+00 0.0e+00 0.0e+00 0.0e+00 0.0e+00 0.0e+00 1000 1.0e+00 -nan
energy__ 7.1e+03 2.1e-01 4.2e+00 7.1e+03 7.1e+03 7.1e+03 389 3.9e-01 1.0e+00
beta[1] 3.4e-02 1.0e-03 2.6e-02 -7.0e-03 3.5e-02 7.7e-02 634 6.4e-01 1.0e+00
beta[2] -3.9e+00 5.8e-03 1.8e-01 -4.2e+00 -3.9e+00 -3.6e+00 1000 1.0e+00 1.0e+00
beta[3] -2.6e-01 6.2e-03 2.0e-01 -5.8e-01 -2.6e-01 7.5e-02 1000 1.0e+00 1.0e+00
beta[4] 5.8e-02 1.9e-04 6.0e-03 4.8e-02 5.8e-02 6.8e-02 1000 1.0e+00 1.0e+00
beta[5] -1.3e+00 2.0e-02 6.4e-01 -2.3e+00 -1.3e+00 -3.2e-01 1000 1.0e+00 1.0e+00
beta[6] 1.8e-02 6.4e-05 2.0e-03 1.5e-02 1.8e-02 2.1e-02 1000 1.0e+00 1.0e+00
beta[7] -6.3e-03 2.3e-05 7.2e-04 -7.5e-03 -6.3e-03 -5.1e-03 1000 1.0e+00 1.0e+00
beta[8] -1.5e-01 3.7e-02 9.8e-01 -1.8e+00 -1.3e-01 1.4e+00 708 7.1e-01 1.0e+00
beta[9] 5.2e-01 7.1e-03 1.8e-01 2.2e-01 5.1e-01 8.0e-01 653 6.6e-01 1.0e+00
beta[10] 1.7e+00 5.6e-03 1.8e-01 1.4e+00 1.7e+00 2.0e+00 1000 1.0e+00 1.0e+00
beta[11] 9.0e-01 9.7e-04 2.7e-02 8.5e-01 9.0e-01 9.4e-01 753 7.6e-01 1.0e+00
c[1] 4.6e+00 5.3e-02 1.2e+00 2.5e+00 4.6e+00 6.5e+00 538 5.4e-01 1.0e+00
c[2] 6.8e+00 5.3e-02 1.2e+00 4.7e+00 6.8e+00 8.7e+00 533 5.4e-01 1.0e+00
c[3] 1.0e+01 5.3e-02 1.2e+00 7.8e+00 1.0e+01 1.2e+01 529 5.3e-01 1.0e+00
c[4] 1.3e+01 5.4e-02 1.2e+00 1.0e+01 1.3e+01 1.4e+01 526 5.3e-01 1.0e+00
c[5] 1.5e+01 5.4e-02 1.2e+00 1.3e+01 1.5e+01 1.7e+01 526 5.3e-01 1.0e+00
c[6] 1.9e+01 5.5e-02 1.3e+00 1.7e+01 1.9e+01 2.1e+01 547 5.5e-01 1.0e+00

```

---

<div class="post-metadata">

**Author:** ![sakrejda](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.mc-stan.org/sakrejda/32/845_2.png) [@sakrejda](https://discourse.mc-stan.org/u/sakrejda)\
**Post date:** [June 7, 2018, 3:12pm UTC](https://discourse.mc-stan.org/t/stan-on-computing-cluster-strange-results/4430/8 "2018-06-07T15:12:04Z")

</div>

I could see the fast math flag causing that problem.

---

<div class="post-metadata">

**Author:** ![aaronjg](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.mc-stan.org/aaronjg/32/3768_2.png) [@aaronjg](https://discourse.mc-stan.org/u/aaronjg)\
**Post date:** [June 7, 2018, 6:51pm UTC](https://discourse.mc-stan.org/t/stan-on-computing-cluster-strange-results/4430/9 "2018-06-07T18:51:46Z")

</div>

Did it fix the RStan issue as well?

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<div class="post-metadata">

**Author:** ![fabio](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.mc-stan.org/fabio/32/935_2.png) [@fabio](https://discourse.mc-stan.org/u/fabio)\
**Post date:** [June 7, 2018, 7:07pm UTC](https://discourse.mc-stan.org/t/stan-on-computing-cluster-strange-results/4430/10 "2018-06-07T19:07:35Z")

</div>

I can not use R on the cluster. I am trying my way with CmdStan for the first time. Now I have similar results on my machine (with rstan) and in the cluster (with CmdStan) with the same running time. I hope to understand how to speed up things in future since I will have a multilevel model in mind.

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<div class="post-metadata">

**Author:** ![lcomm](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.mc-stan.org/lcomm/32/1238_2.png) [@lcomm](https://discourse.mc-stan.org/u/lcomm)\
**Post date:** [June 7, 2018, 7:15pm UTC](https://discourse.mc-stan.org/t/stan-on-computing-cluster-strange-results/4430/11 "2018-06-07T19:15:47Z")

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I had a similar problem with very slow Rstan code on the server when it ran fine on my laptop. I reinstalled Stan and recompiled my R package after deleting the Makevars file on the server and the performance went back to being almost equivalent. Now I wish I had spent more time identifying which flag was the problem. They can make a big difference, though.

So in this case the problem was not the _presence_ of a special compiler flag but the _absence_ of one?

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<div class="post-metadata">

**Author:** ![fabio](https://yyz2.discourse-cdn.com/flex030/user_avatar/discourse.mc-stan.org/fabio/32/935_2.png) [@fabio](https://discourse.mc-stan.org/u/fabio)\
**Post date:** [June 8, 2018, 9:57am UTC](https://discourse.mc-stan.org/t/stan-on-computing-cluster-strange-results/4430/12 "2018-06-08T09:57:55Z")

</div>

I had to add some compiler flags before compiling CmdStan (and subsequently my model) on the **cluster**.

```
CXXFLAGS = -O3 -mtune=native -march=native -Wno-unused-variable -Wno-macro-redefined -fno-fast-math

```

the result now is that rstan and CmdStan (compiled on my machine following **strictly** the [guide](https://github.com/stan-dev/rstan/wiki/Installing-RStan-on-Mac-or-Linux#config)) and the CmdStan on the server with the flags above now give similar results running at the same speed.

I hope I was clear.