Get what you need to build and optimize your oneAPI projects for free. With an Intel® Developer Cloud account, you get 120 days of access to the latest Intel® hardware—CPUs, GPUs, FPGAs—and Intel® oneAPI tools and frameworks. No software downloads. No configuration steps. No installations.
Speed up linear algebra computations with low-level routines that operate on vectors and matrices, and are compatible with these industry-standard BLAS and LAPACK operations:
Level 1: Vector-vector operations
Level 2: Matrix-vector operations
Level 3: Matrix-matrix operations
Sparse Linear Algebra Functions
Perform various operations on sparse matrices with low-level and inspector-executor routines including the following:
Multiply sparse matrix with dense vector
Multiply sparse matrix with dense matrix
Solve linear systems with triangular sparse matrices
Solve linear systems with general sparse matrices
Fast Fourier Transforms (FFT)
Transform a signal from its original domain (typically time or space) into a representation in the frequency domain and back. Use FFT functions in one, two, or three dimensions with support for mixed radices. The supported functions include complex-to-complex and real-to-complex transforms of arbitrary length in single-precision and double-precision.
Random Number Generator Functions (RNG) Use common pseudorandom, quasi-random, and nondeterministic random number engines to solve continuous and discrete distributions.
Provide spline-based interpolation capabilities that you can use to approximate functions, function derivatives or integrals, and perform cell search operations.
Vector Math Balance accuracy and performance with vector-based elementary functions. Manipulate values with traditional algebraic and trigonometric functions.
Summary Statistics Compute basic statistical estimates (such as raw or central sums and moments) for single- and double-precision multidimensional datasets.
These benchmarks are offered to help you make informed decisions about which routines to use in your applications, including performance for each major function domain in oneMKL by processor family. Some benchmark charts only include absolute performance measurements for specific problem sizes. Others compare previous versions, popular alternate open-source libraries, and other functions for oneMKL.