Intel® MKL provides the general purpose BLAS* matrix multiply routines ?GEMM defined as follows:
C := alpha*op(A)*op(B) + beta*C
where alpha and beta are scalars, op(A) is an m-by-k matrix, op(B) is a k-by-n matrix, C is an m-by-n matrix, with op(X) being either X, or XT, or XH.
Intel® MKL 11.0 Update 2 introduced a new component called Extended Eigensolver routines. These routines solve standard and generalized Eigenvalue problems for symmetric/Hermitian and symmetric/Hermitian positive definite sparse matrices. Specifically, these routines computes all the Eigenvalues and the corresponding Eigenvectors within a given search interval [λmin, λmax]:
Процесс выявления неполадок и ошибок в коде приложений для ОС Android* весьма схож и для архитектуры Intel® Atom™, и для архитектуры ARM*. В этой статье мы сфокусируем наше внимание на методиках отладки приложений для Android* на платформах с архитектурой Intel.
В этой части описывается процесс установки драйвера Intel® USB, необходимого для поддержки удаленной отладки приложений на устройствах с Intel® Atom™ и ОС Android*.
Overview
Intel® Math Kernel Library (Intel® MKL) includes the optimized DFT transform functions on Intel® Xeon Phi™ coprocessors. These functions are carefully vectorized and threaded to take advantage of the hardware features. This article provides some performance tuning tips on running MKL DFT function on Intel Xeon Phi coprocessors. We will start with some simple example code.
Building the example code
Employing performance libraries can be a great way to streamline and unify the computational execution flow for data intensive tasks, thus minimizing the risk of data stream timing issues and heisenbugs. Here we will describe the two libraries that can be used for signal processing within Intel® System Studio.
Intel® Integrated Performance Primitives (Intel®IPP)
