Intel? oneAPI Perfomance Libraries: Part 1

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.

Intel? Integrated Performance Primitives

Intel? Integrated Performance Primitives (Intel? IPP) is a powerful software library that provides developers with ready-to-use, optimized functions for building high-performance applications in various domains. It supports vision, signal processing, security, and storage applications, offering multithreaded capabilities for improved performance.

Key Features:

• Optimized for Performance: Intel? IPP leverages advanced instruction sets such as SIMD, Intel? AVX2, and Intel? AVX-512 to deliver highly optimized performance on Intel architectures.
• Plug In and Go: The library provides royalty-free, pre-built functions that save development time and ensure optimal performance on current and future generations of Intel processors. It allows developers to focus on adding new features rather than low-level optimizations.
• Comprehensive Set of Primitives: With over 2,500 image processing functions, 1,300 signal processing functions, 500 computer vision functions, and 300 cryptography functions, Intel? IPP covers a wide range of fundamental algorithms used in digital media, enterprise data, embedded communications, and scientific/technical applications.

Domains and Workloads:

• Image Processing: Intel? IPP enables applications in healthcare, computer vision, e-commerce, surveillance, biometrics, printing, and more. It supports tasks like image recognition, enhancement, optical correction, and gesture recognition.
• Data Compression: The library optimizes common compression standards, allowing significant performance gains in internet portal data centers, data storage centers, databases, and enterprise data management.
• Signal Processing: Intel? IPP is ideal for applications in voice recognition, biotechnology, wearable technology, hearing aids, and speech synthesis. It provides optimized functions for tasks like discrete Fourier transform (DFT), fast Fourier transforms (FFT), convolution, filtering, and statistics.
• Cryptography: The library offers functions for security analysis, threat intelligence, mobile/cloud/IoT security, and data integrity/authentication. It supports various cryptographic algorithms, including symmetric algorithms, AES, RSA, ECC, and secure data transfer protocols.

The Intel? MPI Library

The Intel? MPI Library is a powerful message-passing library that facilitates flexible, efficient, and scalable cluster messaging. It adheres to the open source MPICH specification and supports multiple fabric interconnects, making it suitable for high-performance computing (HPC) clusters based on Intel? and compatible processors.

Key Features:

• Multiple Fabric Support: The library enables the development of applications that can run on different cluster interconnects, selected at runtime. It allows for maximum end-user performance without the need to modify the software or operating environment, reducing time to market and leveraging optimized fabrics.
• OpenFabrics Interface (OFI) Support: Intel MPI Library utilizes OFI, an optimized framework that provides communication services to HPC applications. It streamlines the communication path from application code to data transmission, allows runtime tuning for underlying fabrics, and delivers optimal performance on extreme-scale solutions.
• Scalability: The library implements the MPI 3.1 standard on multiple fabrics, enabling quick delivery of maximum application performance without requiring significant software or operating system modifications. It supports thread safety for hybrid multithreaded MPI applications and improved start scalability through the mpiexec.hydra process manager.
• Performance and Tuning Utilities: Intel MPI Library offers performance and tuning utilities to achieve top performance. It provides interconnect independence, allowing development of MPI code independent of the fabric, and offers ABI compatibility with existing MPI-1.x and MPI-2.x applications, ensuring performance improvements without recompilation.
• Application Binary Interface Compatibility: The library maintains ABI compatibility with previous MPI versions, ensuring conformity to runtime naming conventions and enabling seamless integration with existing applications.

Additionally, the library includes Intel? MPI Benchmarks, which measure performance and efficiency of cluster systems, including node performance, network latency, and throughput. The library provides default parameters that can be refined or customized using tools like mpitune for optimal performance.

The Intel? oneAPI Collective Communications Library

The Intel? oneAPI Collective Communications Library (oneCCL) is a library designed to facilitate efficient and scalable distributed training for deep neural networks. By utilizing optimized communication patterns, oneCCL enables faster training of newer and deeper models by distributing the training process across multiple nodes.

Key Features:

• Multi-Node Communication Patterns: oneCCL provides optimized communication patterns for distributing model training across multiple nodes. It integrates seamlessly into deep learning frameworks, whether you are building them from scratch or customizing existing ones.
• Support for Various Interconnects: The library is built on top of lower-level communication middleware, such as MPI and libfabrics, which transparently support a range of interconnects, including Cornelis Networks, InfiniBand, and Ethernet. This flexibility allows for efficient communication across different hardware setups.
• High Performance on Intel CPUs and GPUs: oneCCL is optimized for high performance on Intel CPUs and GPUs. It takes advantage of the underlying hardware capabilities to achieve optimal communication performance, allowing you to balance compute and communication for scalable communication patterns.
• Efficient Collective Operations: The library enables efficient implementations of collective operations that are commonly used in neural network training, such as all-gather, all-reduce, and reduce-scatter. These collective operations are essential for coordinating and synchronizing computations across distributed nodes.

Additional Features:

• Common APIs for Deep Learning Frameworks: oneCCL provides a collective API that supports commonly used collective operations found in deep learning and machine learning workloads. It also offers interoperability with SYCL, a programming model for heterogeneous computing.
• Deep Learning Optimizations: The runtime implementation of oneCCL includes several optimizations to enhance performance. These optimizations include asynchronous progress for overlapping compute and communication, dedicated cores for optimal network utilization, message prioritization, persistence, and out-of-order execution. The library also supports collectives in low-precision data types, which can be beneficial for certain deep learning scenarios.

With Intel's commitment to performance optimization and cross-platform support, these libraries offer a robust foundation for building high-performance applications that meet the demands of today's computing landscape. Whether it's accelerating data analysis, enabling complex simulations, or enhancing security, these libraries provide developers with the necessary tools to unlock the full potential of Intel architecture and deliver cutting-edge solutions.