blas

问题 I wanted to install BLAS, CBLAS, LAPACK and OpenBLAS libraries from source using available packages you can download here openblas and lapack, blas/cblas. Firstly I removed my system blas/cblas and lapack libraries, but unfortunately atlas library couldn't be uninstalled (I can either have both blas and lapack or atlas - can't remove them all). I didn't bother and started compiling downloaded libraries cause I thought that after installation I would be able to remove atlas. Building process

问题 Has anyone had experience using prefetch instructions for the Core 2 Duo processor? I've been using the (standard?) prefetch set ( prefetchnta , prefetcht1 , etc) with success for a series of P4 machines, but when running the code on a Core 2 Duo it seems that the prefetcht(i) instructions do nothing, and that the prefetchnta instruction is less effective. My criteria for assessing performance is the timing results for a BLAS 1 vector-vector (axpy) operation, when the vector size is large

问题 Comparing the results of a floating point computation across a couple of different machines, they are consistently producing different results. Here is a stripped down example that reproduces the behavior: import numpy as np from numpy.random import randn as rand M = 1024 N = 2048 np.random.seed(0) a = rand(M,N).astype(dtype=np.float32) w = rand(N,M).astype(dtype=np.float32) b = np.dot(a, w) for i in range(10): b = b + np.dot(b, a)[:, :1024] np.divide(b, 100., out=b) print b[0,:3] Different

问题 Comparing the results of a floating point computation across a couple of different machines, they are consistently producing different results. Here is a stripped down example that reproduces the behavior: import numpy as np from numpy.random import randn as rand M = 1024 N = 2048 np.random.seed(0) a = rand(M,N).astype(dtype=np.float32) w = rand(N,M).astype(dtype=np.float32) b = np.dot(a, w) for i in range(10): b = b + np.dot(b, a)[:, :1024] np.divide(b, 100., out=b) print b[0,:3] Different

问题 Is there a way of detecting the version of BLAS that R is using from inside R? I am using Ubuntu, and I have a couple of BLAS versions installed - I just don't know which one is "active" from R's point of view! I am aware of http://r.789695.n4.nabble.com/is-Rs-own-BLAS-td911515.html where Brian Ripley said in June 2006 that it was not possible - but have things changed? 回答1: I think you cannot. R will be built against the BLAS interface , and R itself does not which package supplies the

问题 Is there a way of detecting the version of BLAS that R is using from inside R? I am using Ubuntu, and I have a couple of BLAS versions installed - I just don't know which one is "active" from R's point of view! I am aware of http://r.789695.n4.nabble.com/is-Rs-own-BLAS-td911515.html where Brian Ripley said in June 2006 that it was not possible - but have things changed? 回答1: I think you cannot. R will be built against the BLAS interface , and R itself does not which package supplies the

问题 Is there a way of detecting the version of BLAS that R is using from inside R? I am using Ubuntu, and I have a couple of BLAS versions installed - I just don't know which one is "active" from R's point of view! I am aware of http://r.789695.n4.nabble.com/is-Rs-own-BLAS-td911515.html where Brian Ripley said in June 2006 that it was not possible - but have things changed? 回答1: I think you cannot. R will be built against the BLAS interface , and R itself does not which package supplies the

问题 In my code I have loop in which I construct and over determined linear system and try to solve it: #pragma omp parallel for for (int i = 0; i < n[0]+1; i++) { for (int j = 0; j < n[1]+1; j++) { for (int k = 0; k < n[2]+1; k++) { arma::mat A(max_points, 2); arma::mat y(max_points, 1); // initialize A and y arma::vec solution = solve(A,y); } } } Sometimes, quite randomly the program hangs or the results in the solution vector are NaN. And if I put do this: arma::vec solution; #pragma omp

问题 The BLAS section in R installation and administration manual says that when R is built from source , with configuration parameter --without-blas , it will build Netlib's reference BLAS into a standalone shared library at R_HOME/lib/libRblas.so , along side the standard R shared library R_HOME/lib/libR.so . This makes it easier for user to switch and benchmark different tuned BLAS in R environment. The guide suggests that researcher might use symbolic link to libRblas.so to achieve this, and

问题 I'd like to know if it's possible to change at (Python) runtime the maximum number of threads used by OpenBLAS behind numpy? I know it's possible to set it before running the interpreter through the environment variable OMP_NUM_THREADS , but I'd like to change it at runtime. Typically, when using MKL instead of OpenBLAS, it is possible: import mkl mkl.set_num_threads(n) 回答1: You can do this by calling the openblas_set_num_threads function using ctypes . I often find myself wanting to do this,