Integration of High-performance Computing And Artificial Intelligence
What Is High-Performance Computing (HPC)?
High-performance Computing (HPC) refers to the consolidation of computational resources to achieve significantly higher processing power compared to conventional computers and servers. Its primary objective is to perform extensive calculations for solving intricate problems that involve processing vast volumes of data.
At the core of HPC lies the principle of executing code in parallel, harnessing the potential of large-scale runtime acceleration. This is achieved by parallelizing processes and adding more computing cores, which can result in HPC systems of considerable scale.
How Does HPC Work?
In contrast to a standard computer that tackles problems by sequentially dividing the workload into tasks and executing them on a single processor, HPC harnesses the power of massive parallel computing, computer clusters, and high-performance components.
In HPC, parallel computing utilizes millions of processors or processor cores to achieve enhanced performance. The computing cluster comprises interconnected high-speed servers. Additionally, HPC clusters incorporate high-speed and low-latency components for networking, storage, memory, and file systems. These components optimize and enhance the overall performance of the cluster.
High-Performance Computing & AI
Artificial Intelligence (AI) can enhance the data analysis process in High-Performance Computing (HPC), delivering faster results while maintaining the same level of accuracy. The integration of HPC and AI relies on similar architectural foundations, as both involve processing extensive datasets that tend to grow in size. This necessitates robust computing and storage capabilities, high-bandwidth connectivity, and efficient fabric architectures. The application of AI capabilities can benefit various HPC use cases, including financial analysis, astrophysics, astronomy, climate science, earth science, scientific visualization and simulation, etc.
How HPC Can Help Build Better AI Applications
High-Performance Computing (HPC) plays a pivotal role in advancing AI applications through various means:
Accelerate Calculations: HPC leverages massive parallel computing, enabling swift calculations and efficient processing of large datasets within shorter timeframes.
Improve Storage and Memory: Ample storage and memory capacity provided by HPC facilitate seamless handling of substantial volumes of data, resulting in improved accuracy of AI models.
Enhance Performance and Efficiency: HPC systems effectively harness the power of GPUs to optimize the processing of AI algorithms, leading to enhanced performance and efficiency.
Reduce Cost: HPC services available on the cloud offer cost-effective solutions by reducing upfront expenses, allowing users to access and leverage HPC capabilities with ease.
By leveraging the capabilities of HPC, AI applications can benefit from accelerated calculations, increased storage and memory capacity, efficient GPU utilization, and affordable cloud-based access, ultimately leading to the development of more sophisticated and impactful AI solutions.
Integration of AI and HPC
The convergence of High-Performance Computing (HPC) and Artificial Intelligence (AI) necessitates certain adaptations in workload tools and management. Following are a few ways High-Performance Computing is evolving to address the challenges associated with integrating AI and HPC.
HPC traditionally employs languages such as C, C++, and Fortran, with extensions and libraries tailored to HPC processes. In contrast, AI heavily relies on Python and Julia. To enable the use of the same infrastructure for both HPC and AI, compatibility between software and interfaces is crucial. Often, AI frameworks and languages are layered over existing software, allowing programmers from both domains to continue using their preferred tools without necessitating a migration to a different language.
Virtualization and Containers
Containers offer flexibility in adapting infrastructure to changing workload requirements and ensure consistent deployment. They enable scalability for Julia and Python applications, commonly used in AI. By leveraging containers, teams can swiftly create HPC configurations that are easy to deploy without the need for time-consuming configurations.
Enhanced Memory Capacity
AI heavily relies on large datasets, and the volume of data continues to grow. Efficiently collecting and processing these datasets demands a substantial amount of memory to leverage the speed and efficiency that HPC offers. HPC systems address this challenge through technologies that support ample persistent and ephemeral RAM. By accommodating increased memory capacity, HPC empowers AI applications to maintain optimal efficiency and performance while handling big data.
FS High-performance Computing Network Solution
The widespread adoption of High-performance Computing (HPC) and AI technology has led to increasingly stringent requirements for network speed and latency across various industries. Building high-performance networks has become a top priority. Recently, FS has utilized 25G/100G and 100G/400G Ethernet network devices to construct high-performance computing data centers, which ensure high throughput, high bandwidth, and low latency network performance with low network construction costs. The following picture shows a typical HPC network application, which adopts FS N8560-48BC as the access switch, modular switch NC8400-4TH as the spine switch, and N9510-64D as the core switch.
FS Product List of HPC Network Solution
||N5860-48SC||48x 10Gb SFP+, 8x 100Gb QSFP28||
|N8560-48BC||48x 25G SFP28, 8x 100G QSFP28|
|N8560-32C||32x 100G QSFP28|
|Spine Switch||NC8400-4TH (NC8400-16CD)||16x 40/100G QSFP28, 4x 400G QSFP-DD||
|Core Switch||N9510-64D||64x 400G QSFP-DD||