Intel? oneAPI Perfomance Libraries- Part 2

Intel? provides a suite of powerful software libraries that empower developers to optimize the performance of their applications across various domains. These libraries offer ready-to-use, highly optimized functions for tasks ranging from image processing and signal processing to cryptography and distributed training for deep neural networks. By leveraging these libraries, developers can accelerate application development, achieve maximum calculation performance, and harness the capabilities of Intel CPUs and GPUs. In this era of data-driven applications, these libraries play a crucial role in enabling high-performance computing and efficient utilization of hardware resources.

The Intel? oneAPI Deep Neural Network Library

The Intel? oneAPI Deep Neural Network Library (oneDNN) is a powerful tool for increasing the performance of deep learning frameworks and applications on CPUs and GPUs. It provides highly optimized implementations of deep learning building blocks, allowing developers to improve the performance of existing frameworks or develop faster deep learning applications from scratch.

Key Features:

• Cross-platform Support: oneDNN is an open-source, cross-platform library that provides a consistent API for deep learning application and framework developers. It abstracts away the complexities of performance optimization, allowing developers to use the same API for CPUs, GPUs, or both. This simplifies the development process and enables deployment of applications optimized for Intel CPUs and GPUs without writing target-specific code.
• Automatic Optimization: The library automatically detects and optimizes the instruction set architecture (ISA) for the underlying hardware. It works seamlessly with existing deep learning frameworks, such as OpenVINO? toolkit, Intel AI Analytics Toolkit, Intel Distribution for PyTorch, and Intel Distribution for TensorFlow. This enables developers to leverage the performance benefits of oneDNN without modifying their existing frameworks.
• Network Optimization: oneDNN provides tools for identifying performance bottlenecks using Intel VTune Profiler, allowing developers to optimize their deep learning networks. It also includes features like automatic memory format selection and propagation based on hardware and convolutional parameters, fusion of primitives with operations applied to the primitive's result (e.g., Conv+ReLU), and support for quantization from FP32 to lower precision formats like FP16, bf16, or int8 using Intel Neural Compressor.
• Optimized Implementations of Building Blocks: oneDNN includes highly optimized implementations of key deep learning building blocks, such as convolution, matrix multiplication, pooling, batch normalization, activation functions, recurrent neural network (RNN) cells, and long short-term memory (LSTM) cells. These optimized implementations ensure efficient execution of deep learning operations and improve overall performance.
• Abstract Programming Model: oneDNN introduces a programming model based on primitives, memory, engines, and streams. Primitives represent low-level operations, while memory handles the allocated memory with specific dimensions, data types, and formats. Engines represent hardware processing units like CPUs or GPUs, and streams manage queues of primitive operations on the engines. This abstract programming model provides flexibility and allows for efficient execution of deep learning computations.

The Intel? oneAPI Data Analytics Library 

The Intel? oneAPI Data Analytics Library (oneDAL) is a powerful tool for deploying high-performance data science applications on CPUs and GPUs. It provides optimized algorithms and functions for analysis, math, training, and prediction, allowing you to build compute-intensive applications that run efficiently on Intel architecture.

Key Features:

• Maximum Calculation Performance: oneDAL is optimized for both CPUs and GPUs, leveraging the capabilities of Intel architecture to deliver high-performance computing. Each function in the library is tuned to the specific instruction set, vector width, core count, and memory architecture of the target CPU or GPU, ensuring maximum calculation speed.
• High-Speed Algorithms: The library includes a wide range of algorithms for analysis functions, math functions, and training and prediction functions. These algorithms are available in C++ and Java, and they can be used to optimize algorithms from popular machine learning Python libraries, such as XGBoost (part of the Intel AI Analytics Toolkit).
• XGBoost Optimized for Intel Architecture: oneDAL provides optimized support for XGBoost, a popular gradient boosting framework used for machine learning tasks. With oneDAL, you can leverage the power of Intel architecture to analyze large datasets, make faster predictions, and optimize data ingestion and algorithmic compute simultaneously.

Additional Features:

• Performance and Portability: The functions in oneDAL are designed for maximum calculation speed and portability. Each function is tuned to the specific hardware characteristics of the target CPU or GPU, resulting in optimized performance. The library supports multiple programming languages, including Python, Java, C, and C++, allowing you to work in the language you are most familiar with while achieving maximum performance.
• In-Depth Algorithm Support: oneDAL provides comprehensive algorithm support for various data analytics tasks. Supported algorithms include association rules mining, correlation and variance-covariance matrices, decision forest for classification and regression, Gaussian mixture model (EM-GMM) using expectation-maximization, gradient boosted trees (GBT), collaborative filtering with alternating least squares (ALS), multinomial Na?ve Bayes classifier, multiclass classification, logistic regression with L1 and L2 regularization, linear regression, and more. Additionally, the library offers SYCL interfaces for CPU and GPU algorithms like K-means clustering, K-nearest neighbor (KNN), support vector machines (SVM) with linear and radial basis function (RBF) kernels, principal components analysis (PCA), and density-based special clustering of applications with noise (DBSCAN).

The Intel? oneAPI DPC++ Library 

The Intel? oneAPI DPC++ Library (oneDPL) is a performance and productivity library designed for accelerated computing using DPC++ (SYCL*) programming model. Here are some key features and capabilities of the library:

• Inline Accelerator Targeting: With oneDPL, you can use device and host containers to target GPUs, FPGAs, or run your code across multi-node CPUs. This allows you to leverage the compute capabilities of different hardware architectures.
• Optimized C++ Standard Algorithms: The library provides access to parallelized C++17 algorithms and utilities, enabling efficient application development and deployment on a variety of hardware. These optimized algorithms help you achieve better performance in your accelerated computing tasks.
• Integration with Intel? DPC++ Compatibility Tool: oneDPL works seamlessly with other components of the Intel oneAPI DPC++ toolkit. It simplifies the migration of CUDA* applications to SYCL code by providing compatibility and support for existing CUDA applications.
• Streamlined Cross-Architecture Programming: oneDPL leverages familiar APIs such as C++ STL, Parallel STL (PSTL), and Boost.Compute. This streamlines the development process and allows developers to write code that can be executed efficiently across CPUs, GPUs, and FPGAs.
• Custom Iterators and Extensions: The library enables the successful application of parallel algorithms by providing custom iterators and extensions. This allows you to take advantage of the full power of parallel computing and optimize your code further.