Level 3: Matrix-matrix operations
There are many different implementations of these subprograms available. These different implementations are created with different purposes or platforms in mind. Intel's oneAPI implementation heavily focuses on performance, specifically with x86 and x64 in mind.
[[File:BLAS.png]]
==== Sparse Linear Algebra Functions ====
Able to perform low-level inspector-executor routines on sparse matrices, such as:
- * Multiply sparse matrix with dense vector * Multiply sparse matrix with dense matrix
- Multiply * Solve linear systems with triangular sparse matrix with dense matrixmatrices
- * Solve linear systems with triangular general sparse matrices
- Solve A sparse matrix is matrix that is mostly empty, these are common in machine learning applications. Using standard linear systems with general algebra functions would lead to poor performance and would require greater amounts of storage. Specially written sparse linear algebra functions have better performance and can better compress matricesto save space.
A sparse matrix is matrix that is mostly empty, these are common in machine learning applications. Using standard linear algebra functions would lead to poor performance and would require greater amounts of storage. Specially written sparse linear algebra functions have better performance and can better compress matrices to save space[[File:Sparse. png]]
==== Fast Fourier Transforms ====
Enabling technology today such as most digital communications, audio compression, image compression, satellite tv, FFT is at the heart of it.
A fast Fourier transform (FFT) is an algorithm that computes the discrete Fourier transform (DFT) of a sequence, or its inverse (IDFT).
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==== Random Number Generator ====
All RNG routines can be categorized in several different categories.
- * Engines - hold the state of a generator
- * Transformation classes - holds different types of statistical distribution
- * Generate function - the routine that obtains the random number from the statistical distribution
- * Services - using routines that can modify the state of the engine
The generation of numbers is done in 2 steps:
Data Fitting routines use the following workflow to process a task:
- * Create a task or multiple tasks.
- * Modify the task parameters.
- * Perform a Data Fitting computation.
- * Destroy the task or tasks.
Data Fitting functions:
- * Task Creation and Initialization Routines.
- * Task Configuration Routines.
- * Computational Routines.
- * Task Destructors.
==== Summary Statistics ====
Summary Statistics calculate:
- * Raw and central sums/moments up to the fourth order.
- * Variation coefficient.
- * Skewness and excess kurtosis.
- * Minimum and maximum.
Additional Features:
- * Detect outliers in datasets.
- * Support missing values in datasets.
- * Parameterize correlation matrices. - Compute quantiles for streaming data.
* Compute quantiles for streaming data.
==== Vector Math ====
There are two main set of functions for the Vector Math library that the intel MKL uses they are:
- * VM Mathematical Functions Which allows for it to compute values of mathematical functions e.g. sine, cosine, exponential, or logarithm on vectors that are stored in contiguous memory.
- * VM Service Functions are used for showing when catching errors made in the calculations or accuracy. Such as catching error codes or error messages from improper calculations.
=== Code Samples ===
The Intel oneAPI Math Kernel Library is available from the oneAPI base toolkit and it supports programming languages like C, C++, C#, DPC++ and Fortran. These features will help any financial, science or engineering applications run at an optimized level. The MKL is constantly updated on the Intel oneAPI website with lots of examples and tutorials available on their github. If there's any questions, feel free to ask us or refer to the Intel oneAPI website.
== Presentation ==
Animated GIF of the Presentation
[[File:Intel Math Kernel Library.gif]]
PDF File:
[File:https://wiki.cdot.senecacollege.ca/w/imgs/Intel_Math_Kernel_Library.pdf]
