openblas

I found that using one of BLAS/ATLAS/MKL/OPENBLAS will give improvement on speed in R. However, will it still improve the R Package that is written in C or C++? for example, R package Glmnet is implemented in FORTRAN and R package rpart is implemented in C++. Will it just installing BLAS/...etc will improve the execution time? or do we have to rebuild (building new C code) the package based on BLAS/...etc? It is frequently stated, including in a comment here, that "you have to recompile R" to use different BLAS or LAPACK library. That is wrong. You do not have to recompile R provided it is

I am running a Python 3.6 script as multiple separate processes on different processors of a parallel computing cluster. Up to 35 processes run simultaneously with no problem, but the 36th (and any more) crashes with a segmentation fault on the second line which is import pandas as pd . Interestingly, the first line import os does not cause an issue. The full error message is: OpenBLAS blas_thread_init: pthread_create: Resource temporarily unavailable OpenBLAS blas_thread_init: RLIMIT_NPROC 1024 current, 2067021 max OpenBLAS blas_thread_init: pthread_create: Resource temporarily unavailable

I'm running an algorithm that is implemented in Python and uses NumPy. The most computationally expensive part of the algorithm involves solving a set of linear systems (i.e. a call to numpy.linalg.solve() . I came up with this small benchmark: import numpy as np import time # Create two large random matrices a = np.random.randn(5000, 5000) b = np.random.randn(5000, 5000) t1 = time.time() # That's the expensive call: np.linalg.solve(a, b) print time.time() - t1 I've been running this on: My laptop, a late 2013 MacBook Pro 15" with 4 cores at 2GHz ( sysctl -n machdep.cpu.brand_string gives me