Luc,
There can be many reasons explaining the difference in compiled code
performances. Tuning such code to achieve a pick performance is
generally a fine art.
Optimizations techniques can include but are not limited to:
- SIMD instructions (and memory alignment for their optimal use);
- instruction level parallelism;
- unrolling loops;
- cache level (mis-)hits;
- multi-thread parallelism;
- ...
Approaches in optimization are not the same depending on kind of
application: CPU-bound, memory-bound or IO-bound.
Many of this techniques can be directly used (or not) by compiler
depending on chosen options. Are you sure to use the same options and
compiler that were used during R compilation?
And finally, the compared code could be plainly not the same. R can use
BLAS call, e.g. OpenBLAS to multiply two matrices. This latter is
heavily optimized for such operations and can achieve x10 acceleration
compared to plain "naive" BLAS.
The R code you cite can be just the code for a fallback in case no BLAS
was found during R compilation.
Look at what your sessionInfo() says about used BLAS.
Best,
Serguei.
Le 05/12/2024 à 14:21, Luc De Wilde a écrit :
Dear package developers,
in creating a package lavaanC for use in lavaan, I need to perform some matrix
computations involving matrix products and crossproducts. As far as I see I
cannot directly call the C code in the R core. So I copied the code in the R
core, but the same C/C++ code in a package is 2.5 à 3 times slower than
executed directly in R :
C code in package :
SEXP prod0(SEXP mat1, SEXP mat2) {
SEXP u1 = Rf_getAttrib(mat1, R_DimSymbol);
int m1 = INTEGER(u1)[0];
int n1 = INTEGER(u1)[1];
SEXP u2 = Rf_getAttrib(mat2, R_DimSymbol);
int m2 = INTEGER(u2)[0];
int n2 = INTEGER(u2)[1];
if (n1 != m2) Rf_error("matrices not conforming");
SEXP retval = PROTECT(Rf_allocMatrix(REALSXP, m1, n2));
double* left = REAL(mat1);
double* right = REAL(mat2);
double* ret = REAL(retval);
double werk = 0.0;
for (int j = 0; j < n2; j++) {
for (int i = 0; i < m1; i++) {
werk = 0.0;
for (int k = 0; k < n1; k++) werk += (left[i + m1 * k] * right[k + m2
* j]);
ret[j * m1 + i] = werk;
}
}
UNPROTECT(1);
return retval;
}
Test script :
m1 <- matrix(rnorm(300000), nrow = 60)
m2 <- matrix(rnorm(300000), ncol = 60)
print(microbenchmark::microbenchmark(
m1 %*% m2, .Call("prod0", m1, m2), times = 100
))
Result on my pc:
Unit: milliseconds
expr min lq mean median uq max neval
m1 %*% m2 10.5650 10.8967 11.13434 10.9449 11.02965 15.8397 100
.Call("prod0", m1, m2) 29.3336 30.7868 32.05114 31.0408 33.85935 45.5321 100
Can anyone explain why the compiled code in the package is so much slower than
in R core?
and
Is there a way to improve the performance in R package?
Best regards,
Luc De Wilde
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