BLAS Levels 1, 2, & 3
Applications in high-performance computing, machine learning, and other domains can benefit from highly optimized BLAS and BLAS-like extensions in Intel® Math Kernel Library (Intel® MKL). These low-level linear algebra routines operate on vectors and matrices, and are compatible with industry-standard BLAS operations that include:
- Level 1: Vector-vector operations
- Level 2: Matrix-vector operations
- Level 3: Matrix-matrix operations
Intel MKL also contains a number of BLAS-like extensions:
- Triangular GEMM routines: Compute a matrix-matrix product but update only the upper or lower triangular part of the result matrix
- Batched GEMM routines: Perform multiple GEMM operations in parallel
- Packed GEMM routines: Amortize internal packing costs across multiple GEMM operations
This package includes highly optimized LU, Cholesky, and QR factorization and driver routines that are compatible with industry-standard APIs. These routines handle real and complex matrices in single and double precision. Eigenvalue and least squares solvers, as well as the latest Netlib LAPACK interfaces and enhancements, are also included.
For cluster computation, Intel MKL implements routines from the industry-standard ScaLAPACK for distributed-memory architectures. The routines include solvers for linear least squares, eigenvalue, and singular value problems for real, complex, dense, and band matrices.
1 Benchmark results were obtained prior to implementation of recent software patches and firmware updates that are intended to address exploits referred to as "Spectre" and "Meltdown." Implementation of these updates may make these results inapplicable to your device or system.
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