Competitive stakes are high in the race for artificial intelligence (AI) innovations. No matter what subject your AI initiative quest covers, such as autonomous driving, cures for disease, or vaccines for life-changing pandemics like COVID-19, an effective and competitive AI setup requires a solid foundation of three essential elements working together in harmony to provide the quick wins you want:
- A server platform providing performance through accelerated compute
- A fast network
- A modern parallel file system to manage the data
These elements can be depicted as a triangle, with all sides fitting together and sitting firmly on your organization’s foundation of talent.
Where AI Meets HPC
First, let’s look at the changing landscape. Historically, high-performance computing (HPC) and AI were two distinct markets, but there’s a convergence. Whereas HPC traditionally focused on relatively few large organizations that led crazy-big research projects and used enormous data clusters, these days AI, machine learning (ML), and deep learning have become HPC in the enterprise.
GPUs Are the Workhorses of Computing
Now, to elaborate upon the first essential element in our triangle, let’s talk about what’s happening in HPC and AI enterprises. The modern buyer journey involves investing in server platforms that leverage compute acceleration technologies, like GPUs. AI needs a powerful compute infrastructure to explore, extract, and examine data for deep insights and breakthrough results.
GPUs are the quintessential workhorses and multitaskers at the heart of modern supercomputing and easily manage the most complex data sets in AI workloads. Choose a platform that provides the balance of accelerated compute, memory, and high speed NVLINK interconnects to get faster, high-quality results.
Bigger, Better, Faster, Stronger
Our second essential element is a fast network. Data centers carry heavy loads to stay competitive in AI. Everything is getting bigger: application size, data size, cluster size, compute size, and more.
Networking continues to improve. Choose a high-speed and low-latency solution that replaces aging Fibre Channel and Ethernet links to speed data transfers from your network to your servers and storage systems.
WekaFS™ for the Win
You might think you’re set with great networking and workhorse GPU compute platforms. Think again. A modern file system, our third essential element, will get the most out of the other two elements. When GPU technology goes into production, often companies haven’t considered the ability of their storage infrastructures to support their data-hungry beasts. GPUs sit idle because legacy storage infrastructures can’t get the data to the application servers fast enough, creating a bottleneck.
Choose a high-performance, scalable parallel file system that can solve today’s biggest storage problems and accelerate IO-intensive workloads that need high bandwidth and metadata-dense performance (mixed workloads with billions of small files). With customers that actively work in AI and ML at scale, WekaFS™ breaks the bottleneck imposed by legacy storage file systems, providing first-to-market competitive advantages by reducing time to market and delivering storage that’s an order of magnitude faster across mixed workloads at exabyte scale.
Effectively Arm Your Data Scientists for Success
Organization’s need three essential elements when designing an AI architecture for success. If you’re just beginning your AI journey or looking for a performance file system that effectively arms your data scientists with the tools they need for the quick wins they want, choose a solution built to support your quest for success. Contact WekaIO to get started!
Do you want to dive into the impact of storage, compute, and networking on the success of your AI initiatives? Join us on September 1st when WekaIO’s Field CTO, Shimon Ben David, will host a fireside chat with Darrin Johnson, Director of Solutions Architecture and Technical Marketing, Enterprise at NVIDIA and Scot Schultz, Sr. Director, Mellanox HPC and Technical Computing at NVIDIA.
For more information about WekaFS, go to https://www.weka.io/products