Leverage the Power of AI to Generate new insight and accelerate the future.
Leverage the Power of AI to Generate new insight and accelerate the future.
Investing in AI is paramount for organizations seeking to elevate their capabilities and stay at the forefront of technological innovation. AI and HPC synergize effectively, as the computational demands of AI tasks often align with the strengths of HPC infrastructure. The parallel processing capabilities of HPC systems significantly accelerate the training and inference processes of complex AI models, enabling faster and more efficient analysis of large datasets. This collaboration between HPC and AI enhances the development and deployment of sophisticated algorithms, facilitating breakthroughs in areas like scientific research, data analytics, and simulations. By harnessing the power of AI, HPC companies can optimize their workflows, improve the accuracy of simulations, and drive advancements in fields such as weather forecasting, materials science, and drug discovery. In essence, the integration of AI into HPC not only unlocks new opportunities for innovation but also enhances performance and competitiveness in a rapidly evolving technological landscape.
Aspen Systems Inc. accelerates your future with fully custom, bleeding-edge, turnkey AI solutions.
Aspen Systems Inc. is you premier choice for AI solutions, giving you access to the state-of-the-art hardware, software, and expertise needed to drive innovation with AI. Our expert team of sales engineers understand the need to train and tune AI models faster, and the full stack implications that come along with it. Our expertise is where ambition meets reality, ensuring that no requirements – from storage, to networking, to cooling, and power consumption – are overlooked, allowing your models to continuously and autonomously train, adapt, and learn – at scale.
Our holistic approach to cluster design ensures that you have the networking, storage, and cooling needed to maximize the efficiency of these massive computing capacities. With our thorough, proprietary burn-in process, you will receive a thoroughly tested, turn-key AI solution, professionally installed (and supported), and ready to discover the unknown.
How can AI help push HPC Forward?
There are many similarities between HPC implementations and AI implementations in terms of architecture. The two processes typically process massive data sets of increasing size by using high levels of compute, storage, and bandwidth, as well as high-bandwidth fabrics. HPC’s big, multidimensional data sets are perfectly suited for deep learning.
AI promises to speed up and increase accuracy in HPC by augmenting expert analysis of data sets with AI models. There are a number of HPC use cases that can benefit from advanced AI capabilities, including:
Machine learning is a subfield of artificial intelligence that focuses on developing algorithms and models capable of learning patterns from data. Unlike traditional programming, where explicit rules are provided, machine learning systems use statistical techniques to automatically improve their performance over time as they are exposed to more information. The two main categories of machine learning are supervised learning, where the algorithm is trained on labeled data, and unsupervised learning, where the algorithm discovers patterns in unlabeled data.
Deep learning is a specialized area within machine learning that involves neural networks with multiple layers (deep neural networks). Inspired by the structure and function of the human brain, deep learning algorithms automatically learn hierarchical representations from data. These deep neural networks excel at capturing intricate patterns and complex relationships in various types of data, such as images, text, and speech. Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs) are common architectures within deep learning, contributing to significant advancements in tasks like image recognition, natural language processing, and speech recognition.
Generative AI is a subset of artificial intelligence focused on creating systems that can generate new, original content. These systems, known as generative models, are designed to produce data that was not explicitly present in their training set. Notable examples include Generative Adversarial Networks (GANs), which use a generator and a discriminator to create realistic images, and Variational Autoencoders (VAEs), which learn probabilistic mappings for generating diverse content. Generative AI finds applications in diverse fields, including image and text generation, style transfer, and even drug discovery.
AI training is a crucial process wherein artificial intelligence models, such as neural networks, learn and improve their performance through exposure to vast datasets. During training, the model adjusts its parameters based on input data, iteratively refining its ability to recognize patterns, make predictions, or perform specific tasks. This iterative learning process is fundamental to enhancing the capabilities of AI systems across diverse applications, ranging from natural language processing to image recognition.
AI inference is the phase where a trained artificial intelligence model applies its learned knowledge to make predictions or decisions based on new, unseen data. Unlike the training phase, which focuses on optimizing model parameters using extensive datasets, inference involves efficiently executing the model to provide real-time insights or responses. This deployment stage is vital for integrating AI into practical applications, enabling systems to utilize their learned knowledge to analyze and interpret information in a variety of domains, from autonomous vehicles to healthcare diagnostics.
GPU + APU Technology
Performing AI tasks, whether it’s training complex models or deploying inference systems, often requires specialized hardware to handle the computational demands. The choice of hardware depends on the specific AI workload and its scale. Here are some of the most prominent hardware offerings running AI workloads today:
Higher Performance and Faster Memory—Massive Bandwidth for Compute Efficiency.
The NVIDIA GH200 Grace Hopper™ Superchip is a breakthrough accelerated CPU designed from the ground up for giant-scale AI and high-performance computing (HPC) applications. The superchip delivers up to 10X higher performance for applications running terabytes of data, enabling scientists and researchers to reach unprecedented solutions for the world’s most complex problems.
An Order-of-Magnitude Leap for Accelerated Computing
Tap into unprecedented performance, scalability, and security for every workload with the NVIDIA® H100 Tensor Core GPU (and the upcoming NVIDIA® H200). With the NVIDIA NVLink® Switch System, up to 256 H100 GPUs can be connected to accelerate exascale workloads. The GPU also includes a dedicated Transformer Engine to solve trillion-parameter language models. The H100’s combined technology innovations can speed up large language models (LLMs) by an incredible 30X over the previous generation to deliver industry-leading conversational AI.
Unprecedented visual computing performance for the data center.
The NVIDIA L40, powered by the Ada Lovelace architecture, delivers revolutionary neural graphics, virtualization, compute, and AI capabilities for GPU-accelerated data center workloads.
The world’s most advanced accelerator for generative AI.
Though not official released as of writing, the upcoming AMD Radeon Instinct MI300A and MI300X are innovative APU offerings that are reported to boast significant 8x AI performance boosts over the previous generation.
The MI300X is based on the next-gen AMD CDNA™ 3 accelerator architecture and supports up to 192 GB of HBM3 memory to provide the compute and memory efficiency needed for large language model training and inference for generative AI workloads. With the large memory of AMD Instinct MI300X, customers can now fit large language models such as Falcon-40, a 40B parameter model on a single, MI300X accelerator. AMD also introduced the AMD Instinct™ Platform, which brings together eight MI300X accelerators into an industry-standard design for the ultimate solution for AI inference and training.
Delivering Performance Leadership for the Data Center
AMD Instinct™ accelerators are engineered from the ground up for this new era of data center computing, supercharging HPC and AI workloads to propel new discoveries. The AMD Instinct™ family of accelerators can deliver industry leading performance for the data center at any scale from single server solutions up to the world’s largest supercomputers.1 With new innovations in AMD CDNA™ 2 architecture, AMD Infinity Fabric™ technology and packaging technology, the latest AMD Instinct™ accelerators are designed to power discoveries at exascale, enabling scientists to tackle our most pressing challenges.
Intel’s accelerator is driving improved deep learning price-performance
and operational efficiency for training and running state-of-the-art models, from the largest language and multi-modal models to more basic computer vision and NLP models. Designed for efficient scalability—whether in the cloud or in your data center, Intel Gaudi2 AI accelerator brings the AI industry the choice it needs—now more than ever.
AI applications often require vast amounts of data storage to accommodate the large datasets used for training machine learning models. Here are some of our favorite solutions for handling the complex data requirements of AI workloads.
The AI Storage Platform for Your Entire AI Infrastructure
DDN’s A3I (Accelerated, Any-Scale AI) solutions break new ground for Artificial Intelligence (AI) and Deep Learning (DL), providing unmatched flexibility for your organization’s AI needs.
Engineered from the ground-up for the AI-enabled data center, A3I solutions are optimized for ingest, training, data transformations, replication, metadata and small data transfers. DDN offers flexibility in platform choice with the all-flash NVMe AI400X2, a hybrid flash and hard drive storage platform which leverages parallel access to flash and deeply expandable HDD storage. The AI400X2 supports a scale-out model with solutions starting at a few TBs yet scalable to 10s of PBs.
The Data Platform for the AI Era
The VAST Data Platform is the foundation trusted by world-leading AI research teams to deliver the scale, speed, and reliability needed to train neural networks and infer in real time. VAST enables greater statistical accuracy by removing the barriers to training on all of an organization’s data at any scale.
TensorFlow, developed by Google, is an open-source machine learning library that has become a cornerstone in AI development. Known for its flexibility and scalability, TensorFlow supports a wide range of applications, from natural language processing to computer vision. Its high-level APIs, like Keras, make it accessible for both beginners and experts. TensorFlow's ecosystem includes TensorFlow Extended (TFX) for deploying production-ready machine learning pipelines.
PyTorch, maintained by Facebook's AI Research lab, has gained popularity for its dynamic computational graph, which provides flexibility in model development and debugging. PyTorch is often praised for its intuitive syntax and is commonly used in research settings. With the introduction of TorchScript, PyTorch also supports seamless deployment in production environments. The PyTorch ecosystem includes tools like torchvision and torchaudio for computer vision and audio processing tasks.
Scikit-Learn is a widely used machine learning library in the Python ecosystem. Known for its simplicity and ease of use, Scikit-Learn provides a comprehensive set of tools for classical machine learning tasks such as classification, regression, clustering, and dimensionality reduction. Its well-documented API and consistent interface make it a go-to choice for practitioners and researchers for quick prototyping and experimentation.
Keras, initially developed as a high-level API for TensorFlow, has evolved into an independent open-source library for building neural networks. Recognized for its user-friendly interface and modular design, Keras allows developers to quickly create and experiment with deep learning models. With TensorFlow 2.0, Keras has become the official high-level API for building models, demonstrating its widespread adoption and support.
IBM Watson Studio is an enterprise-ready platform that supports end-to-end AI and machine learning workflows. It provides a collaborative environment for data scientists, developers, and business analysts to work together on AI projects. Watson Studio integrates with popular open-source tools and supports model deployment in hybrid and multicloud environments.
NVIDIA AI Enterprise is an end-to-end, cloud-native software platform that accelerates data science pipelines and streamlines development and deployment of production-grade AI applications, including generative AI. Enterprises that run their businesses on AI rely on the security, support, and stability provided by NVIDIA AI Enterprise to ensure a smooth transition from pilot to production.
Chorus.ai is a conversational analytics platform designed for sales teams. Using artificial intelligence, Chorus.ai captures and analyzes audio content from sales calls and meetings, providing valuable insights such as sentiment analysis, conversation trends, and coaching opportunities. The platform aims to enhance sales effectiveness by leveraging data-driven intelligence from real-time conversations.
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