Given its reputation as a leader in processing innovation, it's easy to overlook Intel's optimizations to advance the world of AI development. Just in time for the latest PyTorch version, the company offers unprecedented access to the many AI-tailored capabilities of its CPUs and GPUs.
PyTorch 2.0
As you know, PyTorch has come to be known as an integral AI framework for production and research. Its open-source library includes code that can optimize deep neural network training and inference processes.
In March of 2023, Meta and the Linux Foundation announced the release of PyTorch 2.0, and with it has come even greater deep neural network accelerations and support for dynamic shapes. As a result, AI developers can train AIs to be more powerful and make even better inferences.
Intel Optimization for Pytorch
Intel has long understood the importance of hardware acceleration in software performance, and AI is no exception. However, Intel Optimization for PyTorch takes the further step to provide libraries of code that developers can use for deep access to its processors with two computing libraries, including:
Intel oneAPI Deep Neural Network Library (oneDNN)
OneDNN's code can be used to optimize operators in deep learning, like computing vectorization, convolution, pooling, and multi-threading, to improve system efficiency. As a result, you can develop faster deep learning applications with building blocks that are already optimized and launch applications that can work with Intel CPUs and GPUs without writing target-specific code.
Intel oneAPI Collective Communications Library (oneCCL)
With oneCCL, AI developers can optimize the distributed training deeper and newer models across more than one node. It also streamlines the implementations of crucial operators like all-gather, all-reduce, and reduce. With it, developers can trade compute resources for enhanced communication performance allowing for greater scalability in some instances.
The Intel Optimization for PyTorch Approach
Depending on a developer's needs, Intel offers three optimizations within its Optimization for PyTorch. These include:
Operator Optimization
Within operator optimization, there are three methods, including Vectorization (which improves distribution and usage for individual calculations), Parallelization (which provides for simultaneous calculations), and Memory Layout (which specifies data storage location and, therefore, better cache locating).
Graph Optimization
Also, to provide better cache locality and to reduce the number of operations between operators, Graph Optimization, fuses common operators and employs constant folding.
Runtime Optimization
The runtime optimizations provided with Intel Optimization for PyTorch offer extensions to reduce the time lost by inefficient communication between cores. Binding computing threads achieve this benefit to specific cores.
The Intel Extension for PyTorch
The Intel Extension for PyTorch is a plug-in available open-sourced on GitHub in both a CPU and GPU version. It offers two execution modes: eager mode (in which operators in a model are executed immediately upon being encountered, making it advantageous for debugging) and graph mode (which helps to provide a view of the model's overall structure). Also, the extension will detect the hardware used during runtime and use the best operators, such as those from Intel Advanced Vector Extensions 512 (Intel AVX 512) with Vector Neural Network Instructions (VNNI) and Intel Advanced Matrix Extensions (Intel AMX).
Powering it all: Intel CPUs and GPUs
Ultimately, the key driver of all of Intel's PyTorch innovations is their ability to leverage Intel's unmatched processors. These include 4th Gen Intel Xeon Scalable Processors, Intel Xeon CPU Max Series, and Intel Data Center GPU Max Series. Each delivers significant gains over its prior generation, including several in AI processing.
With 4th Gen Intel Xeon Scalable Processors, developers can exploit a 10x higher PyTorch performance boost with real-time inference and training thanks to its Intel Advanced Matrix Extensions (Intel AMX) offering. As a result, developers can take advantage of better-than-ever performance in critical workloads like recommendation systems, NLP, media processing, and image recognition. And they can leverage similar performance gains with the Intel Xeon CPU Max Series and the Intel Data Center GPU Max Series of processors.
Leverage PyTorch to AI Success - with Intel and UNICOM Engineering
Intel understands the symbiotic relationship between expert AI development and hardware performance and continues supporting and enhancing both through its network of systems integrators.
As an Intel Technology Provider and Dell Technologies Titanium OEM partner, UNICOM Engineering stands ready to design, build, and deploy the right hardware solution for your next AI, Deep Learning, or HPC initiative. Our deep technical expertise can drive your transitions to next-gen platforms and provide the flexibility and agility required to bring your solutions to market.
Leading technology providers trust UNICOM Engineering as their system integration partner. And our global footprint allows your solutions to be built and supported worldwide by a single company. Schedule a consultation today to learn how UNICOM Engineering can assist your business.