HPC refers to high-speed parallel processing of complex computations on multiple servers. A group of these servers is called a cluster, which consists of hundreds or thousands of computing servers connected through a network. In an HPC cluster, each computer performing computing operations is called a node.
HPC clusters commonly run batch calculations. At the heart of an HPC cluster is a scheduler used to keep track of available resources. This allows for efficient allocation of job requests across different compute resources (CPUs and GPUs) over high-speed networks.
Modern HPC solutions can run in an on-premises data center, at the edge, or in the cloud. They can solve large-scale computing problems at a reasonable time and cost, making them suitable for a wide range of problems.
High Performance Data Analytics (HPDA) is a new field that applies HPC resources to big data to address increasingly complex problems. One of the primary areas of focus for HPDA is the advancement of artificial intelligence (AI), in particular large-scale deep learning models.
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HPC predated AI, and so the software and infrastructure used in these two fields are quite different. The integration of these two areas requires certain changes to workload management and tools. Here are a few ways HPC is evolving to address AI challenges.
HPC programs are usually written in Fortran, C, or C++. HPC processes are supported by legacy interfaces, libraries, and extensions written in these languages. However, AI relies heavily on languages like Python and Julia.
For both to successfully use the same infrastructure, the interface and software must be compatible with both. In most cases, this means that AI frameworks and languages will be overlaid on existing applications that continue to run as before. This allows AI and HPC programmers to continue using their favorite tools without migrating to a different language.
Containers provide tremendous benefits for HPC and AI applications. These tools allow you to easily adapt your infrastructure to the changing needs of workloads and deploy them anywhere in a consistent manner.
For AI, containers also help make Python or Julia applications more scalable. This is because containerization allows the configuration of an isolated environment that does not depend on host infrastructure.
Containerization also applies to cloud-based HPC and is making HPC more accessible and cost-effective. Containers allow teams to create HPC configurations that can be deployed quickly and easily, adding and removing resources as needed without time-consuming configuration work.
Big data plays an important role in artificial intelligence, and data sets are getting bigger. Collecting and processing these datasets requires a large amount of memory to maintain the speed and efficiency that HPC can provide.
HPC systems address this problem with new technologies that support larger amounts of RAM, both persistent and ephemeral. For example, single-node and distributed memory can be increased using non-volatile memory (NVRAM).
HPC systems typically have between 16 and 64 nodes, with each node running two or more CPUs. This provides significantly higher processing power compared to traditional systems. Additionally, each node in an HPC system provides fast memory and storage resources, enabling larger capacity and higher speed than traditional systems.
To further increase processing power, many HPC systems incorporate graphical processing units (GPUs). A GPU is a dedicated processor used as a coprocessor with the CPU. The combination of CPU and GPU is called hybrid computing.
Hybrid computing HPC systems can benefit AI projects in several ways:
The HPC industry is eager to integrate AI and HPC, improving support for AI use cases. HPC has been successfully used to run large-scale AI models in fields like cosmic theory, astrophysics, high-energy physics, and data management for unstructured data sets.
However, it is important to realize that the methods used to accelerate training of AI models on HPC are still experimental. It is not well understood how to optimize hyperparameters as the number of GPUs increases in an HPC setup.
Another challenge is that when vendors test performance of AI on HPC platforms, they use classical neural network models, such as ResNet trained on the standard ImageNet dataset. This provides a general idea of HPC performance for AI. However, in real life, there are noisy, incomplete, and heterogeneous AI architectures, which may perform very differently from these benchmark results.
The following developments will promote the convergence of AI and HPC in the future:
Run:AI lets you apply the power of Kubernetes to HPC infrastructure. It automates resource management and orchestration for AI workloads that utilize GPUs in HPC clusters. With Run:AI, you can automatically run as many compute intensive workloads as needed on GPU in your HPC infrastructure.
Here are some of the capabilities you gain when using Run:AI:
Run:AI accelerates deep learning and other compute intensive workloads, by helping teams optimize expensive compute resources.
Learn more about the Run:AI Kubernetes HPC Scheduler