我写了一个(可能效率低下，但无论如何..)Rcpp 代码使用内联来模拟随机 SEIR model .
串行版本编译和工作完美，但因为我需要从它模拟很多次，因为在我看来它像一个令人尴尬的并行问题(只需要再次模拟其他参数值并返回一个矩阵与结果)我尝试添加 #pragma omp parallel for 并使用 -fopenmp -lgomp 进行编译，但是……砰!
即使是非常小的例子，我也会遇到段错误!
我尝试添加 setenv("OMP_STACKSIZE","24M",1); 并且值远远超过 24M，但仍然会发生段错误。
我将简要解释一下代码，因为它有点长(我试图缩短它，但结果改变了，我无法重现它..): 我有两个嵌套循环，内部循环针对给定参数集执行模型，外部循环更改参数。
可能发生竞争条件的唯一原因是代码试图在内部循环中并行执行一组指令(由于模型结构，在迭代时无法完成t 它取决于迭代 t-1) 而不是并行化 outer，但如果我没记错的话，这就是 parallel for 构造函数默认情况下如果放在外部...
这基本上是我要运行的代码的形式:
mat result(n_param,T_MAX);
#pragma omp parallel for
for(int i=0,i<n_param_set;i++){
t=0;
rowvec jnk(T_MAX);
while(t < T_MAX){
...
jnk(t) = something(jnk(t-1));
...
t++;
}
result.row(i)=jnk;
}
return wrap(result);
我的问题是:我如何告诉编译器我只想并行计算外部循环(甚至像每个线程的 n_loops/n_threads 一样静态分配它们)而不是内部循环(实际上是不可并行化的) ？
真正的代码有点复杂，如果您真的愿意，我会在这里展示它以实现可重现性，但我只是询问 OpenMP 的行为。请注意，唯一的 OpenMP 指令出现在 122 行。
library(Rcpp);library(RcppArmadillo);library(inline)
misc='
#include <math.h>
#define _USE_MATH_DEFINES
#include <omp.h>
using namespace arma;
template <typename T> int sgn(T val) {
return (T(0) < val) - (val < T(0));
}
uvec rmultinomial(int n,vec prob)
{
int K = prob.n_elem;
uvec rN = zeros<uvec>(K);
double p_tot = sum(prob);
double pp;
for(int k = 0; k < K-1; k++) {
if(prob(k)>0) {
pp = prob[k] / p_tot;
rN(k) = ((pp < 1.) ? (rbinom(1,(double) n, pp))(0) : n);
n -= rN[k];
} else
rN[k] = 0;
if(n <= 0) /* we have all*/
return rN;
p_tot -= prob[k]; /* i.e. = sum(prob[(k+1):K]) */
}
rN[K-1] = n;
return rN;
}
'
model_and_summary='
mat SEIR_sim_plus_summaries()
{
vec alpha;
alpha << 0.002 << 0.0045;
vec beta;
beta << 0.01 << 0.01;
vec gamma;
gamma << 1.0/14.0 << 1.0/14.0;
vec sigma;
sigma << 1.0/(3.5) << 1.0/(3.5);
vec phi;
phi << 0.8 << 0.8;
int S_0 = 800;
int E_0 = 100;
int I_0 = 100;
int R_0 = 0;
int pop = 1000;
double tau = 0.01;
double t_0 = 0;
vec obs_time;
obs_time << 1 << 2 << 3 << 4 << 5 << 6 << 7 << 8 << 9 << 10 << 11 << 12 << 13 << 14 << 15 << 16 << 17 << 18 << 19 << 20 << 21 << 22 << 23 << 24;
const int n_obs = obs_time.n_elem;
const int n_part = alpha.n_elem;
mat stat(n_part,6);
//#pragma omp parallel for
for(int k=0;k<n_part;k++) {
ivec INC_i(n_obs);
ivec INC_o(n_obs);
// Event variables
double alpha_t;
int nX; //current number of people moving
vec rates(8);
uvec trans(4); // current transitions, e.g. from S to E,I,R,Universe
vec r(4); // rates e.g. from S to E, I, R, Univ.
/*********************** Initialize **********************/
int S_curr = S_0;
int S_prev = S_0;
int E_curr = E_0;
int E_prev = E_0;
int I_curr = I_0;
int I_prev = I_0;
int R_curr = R_0;
int R_prev = R_0;
int IncI_curr = 0;
int IncI_prev = 0;
int IncO_curr = 0;
int IncO_prev = 0;
double t_curr = t_0;
int t_idx =0;
while( t_idx < n_obs ) {
// next time preparation
t_curr += tau;
S_prev = S_curr;
E_prev = E_curr;
I_prev = I_curr;
R_prev = R_curr;
IncI_prev = IncI_curr;
IncO_prev = IncO_curr;
/*********************** description (rates) of the events **********************/
alpha_t = alpha(k)*(1+phi(k)*sin(2*M_PI*(t_curr+0)/52)); //real contact rate, time expressed in weeks
rates(0) = (alpha_t * ((double)I_curr / (double)pop ) * ((double)S_curr)); //e+1, s-1, r,i one s get infected (goes in E, not yey infectous)
rates(1) = (sigma(k) * E_curr); //e-1, i+1, r,s one exposed become infectous (goes in I) INCIDENCE!!
rates(2) = (gamma(k) * I_curr); //i-1, s,e, r+1 one i recover
rates(3) = (beta(k) * I_curr); //i-1, s, r,e one i dies
rates(4) = (beta(k) * R_curr); //i,e, s, r-1 one r dies
rates(5) = (beta(k) * E_curr); //e-1, s, r,i one e dies
rates(6) = (beta(k) * S_curr); //s-1 e, i ,r one s dies
rates(7) = (beta(k) * pop); //s+1 one susc is born
// Let the events occour
/*********************** S compartement **********************/
if((rates(0)+rates(6))>0){
nX = rbinom(1,S_prev,1-exp(-(rates(0)+rates(6))*tau))(0);
r(0) = rates(0)/(rates(0)+rates(6)); r(1) = 0.0; r(2) = 0; r(3) = rates(6)/(rates(0)+rates(6));
trans = rmultinomial(nX, r);
S_curr -= nX;
E_curr += trans(0);
I_curr += trans(1);
R_curr += trans(2);
//trans(3) contains dead individual, who disappear...we could avoid this using sequential conditional binomial
}
/*********************** E compartement **********************/
if((rates(1)+rates(5))>0){
nX = rbinom(1,E_prev,1-exp(-(rates(1)+rates(5))*tau))(0);
r(0) = 0.0; r(1) = rates(1)/(rates(1)+rates(5)); r(2) = 0.0; r(3) = rates(5)/(rates(1)+rates(5));
trans = rmultinomial(nX, r);
S_curr += trans(0);
E_curr -= nX;
I_curr += trans(1);
R_curr += trans(2);
IncI_curr += trans(1);
}
/*********************** I compartement **********************/
if((rates(2)+rates(3))>0){
nX = rbinom(1,I_prev,1-exp(-(rates(2)+rates(3))*tau))(0);
r(0) = 0.0; r(1) = 0.0; r(2) = rates(2)/(rates(2)+rates(3)); r(3) = rates(3)/(rates(2)+rates(3));
trans = rmultinomial(nX, r);
S_curr += trans(0);
E_curr += trans(1);
I_curr -= nX;
R_curr += trans(2);
IncO_curr += trans(2);
}
/*********************** R compartement **********************/
if(rates(4)>0){
nX = rbinom(1,R_prev,1-exp(-rates(4)*tau))(0);
r(0) = 0.0; r(1) = 0.0; r(2) = 0.0; r(3) = rates(4)/rates(4);
trans = rmultinomial(nX, r);
S_curr += trans(0);
E_curr += trans(1);
I_curr += trans(2);
R_curr -= nX;
}
/*********************** Universe **********************/
S_curr += pop - (S_curr+E_curr+I_curr+R_curr); //it should be poisson, but since the pop is fixed...
/*********************** Save & Continue **********************/
// Check if the time is interesting for us
if(t_curr > obs_time[t_idx]){
INC_i(t_idx) = IncI_curr;
INC_o(t_idx) = IncO_curr;
IncI_curr = IncI_prev = 0;
IncO_curr = IncO_prev = 0;
t_idx++;
}
//else just go on...
}
/*********************** Finished - Starting w/ stats **********************/
// INC_i is the useful variable, how can I change its reference withour copying it?
ivec incidence = INC_i; //just so if I want to use INC_o i have to change just this...
//Scan the epidemics to recover the summary stats (naively divide the data each 52 weeks)
double n_years = ceil((double)obs_time(n_obs-1)/52.0);
vec mu_attack(n_years);
vec ratio_attack(n_years-1);
vec peak(n_years);
vec atk(52);
peak(0)=0.0;
vec tmpExplo(52); //explosiveness
vec explo(n_years);
int year=0;
int week;
for(week=0 ; week<n_obs ; week++){
if(week - 52*year > 51){
mu_attack(year) = sum( atk )/(double)pop;
if(year>0)
ratio_attack(year-1) = mu_attack(year)/mu_attack(year-1);
for(int i=0;i<52;i++){
if(atk(i)>(peak(year)/2.0)){
tmpExplo(i) = 1.0;
} else {
tmpExplo(i) = 0.0;
}
}
explo(year) = sum(tmpExplo);
year++;
peak(year)=0.0;
}
atk(week-52*year) = incidence(week);
if( peak(year) < incidence(week) )
peak(year)=incidence(week);
}
if(week - 52*year > 51){
mu_attack(year) = sum( atk )/(double)pop;
} else {
ivec idx(52);
for(int i=0;i<52;i++)
{ idx(i) = i; } //take just the updated ones...
vec tmp = atk.elem(find(idx<(week - 52*year)));
mu_attack(year) = sum( tmp )/((double)pop * (tmp.n_elem/52.0));
ratio_attack(year-1) = mu_attack(year)/mu_attack(year-1);
for(int i=0;i<tmp.n_elem;i++){
if(tmp(i)>(peak(year)/2.0)){
tmpExplo(i) = 1.0;
} else {
tmpExplo(i) = 0.0;
}
}
for(int i=tmp.n_elem;i<52;i++)
tmpExplo(i) = 0.0; //to reset the others
explo(year) = sum(tmpExplo);
}
double correlation2;
double correlation4;
vec autocorr = acf(peak);
/***** ACF *****/
if(n_years<3){
correlation2=0.0;
correlation4=0.0;
} else {
if(n_years<5){
correlation2 = autocorr(1);
correlation4 = 0.0;
} else {
correlation2 = autocorr(1);
correlation4 = autocorr(3);
}
}
rowvec jnk(6);
jnk << sum(mu_attack)/(year+1.0)
<< (sum( log(ratio_attack)%log(ratio_attack) )/(n_years-1)) - (pow(sum( log(ratio_attack) )/(n_years-1),2))
<< correlation2 << correlation4 << max(peak) << sum(explo)/n_years;
stat.row(k) = jnk;
}
return stat;
}
'
main='
std::cout << "max_num_threads " << omp_get_max_threads() << std::endl;
RNGScope scope;
mat summaries = SEIR_sim_plus_summaries();
return wrap(summaries);
'
plug = getPlugin("RcppArmadillo")
## modify the plugin for Rcpp to support OpenMP
plug$env$PKG_CXXFLAGS <- paste('-fopenmp', plug$env$PKG_CXXFLAGS)
plug$env$PKG_LIBS <- paste('-fopenmp -lgomp', plug$env$PKG_LIBS)
SEIR_sim_summary = cxxfunction(sig=signature(),main,settings=plug,inc = paste(misc,model_and_summary),verbose=TRUE)
SEIR_sim_summary()
感谢您的帮助!
注意:在你问之前，我稍微修改了 Rcpp 多项式采样函数只是因为我更喜欢这种方式而不是使用指针的方式......没有任何其他特殊原因! :)
最佳答案
R 中的核心伪随机数生成器 (PRNG) 并非设计用于多线程环境。也就是说，它们的状态存储在一个静态数组中(src/main/PRNG.c 中的dummy)，因此在所有线程之间共享。此外，其他几个静态结构用于存储核心 PRNG 的更高级别接口(interface)的状态。
一个可能的解决方案是将对 rnorm() 或其他采样函数的每次调用都放在命名的关键部分中，并且所有调用都具有相同的名称，例如:
...
#pragma omp critical(random)
rN(k) = ((pp < 1.) ? (rbinom(1,(double) n, pp))(0) : n);
...
if((rates(0)+rates(6))>0){
#pragma omp critical(random)
nX = rbinom(1,S_prev,1-exp(-(rates(0)+rates(6))*tau))(0);
...
请注意，critical 结构对其后的结构化 block 进行操作，因此会锁定整个语句。如果在对耗时函数的调用中内联绘制随机数，例如
#pragma omp critical(random)
x = slow_computation(rbinom(...));
这更好地转换为:
#pragma omp critical(random)
rb = rbinom(...);
x = slow_computation(rb);
这样只有 rb = rbinom(...); 语句会受到保护。
关于c++ - OpenMP 在 Rcpp 代码中为 SEIR 模型生成段错误，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/19414416/