In our everyday lives, we rely on technology quite a bit, so it’s shocking when we realize that even with technology advancements happening so regularly, we often hardly notice them. Because of this, it’s even more difficult to keep up with the industry’s best advancements.  At PSSC Labs, we find it imperative that we can keep abreast of the ever-changing technology landscape to determine what is really worthwhile and what is just marketing hype. 

2021 is going to be an interesting year for the virtualization, HPC, AI and data center server spaces. We’ve outlined a few key technology advancements that will not only provide a step forward in the industry, but can actually deliver business value. This value is so often missed, but we believe it’s critical that companies adopt the best of the best technologies.

The CPU market started really shaping up in 2020. Despite being the year from our nightmares, industry leaders like Intel and AMD are pushing their technologies forward. At HPE Cast 2019, AMD revealed their 3^rd Generation Zen 3 based EPYC CPUs codenamed ‘Milan’ and it excited computer nerds like us around the globe. Here we breakdown everything you need to know about these new releases to keep you at the top of your HPC and AI game.

AMD Milan

Flagship product of the Zen 3 core architecture is the 3rd Gen EPYC line known as Milan. Early specs for a singular node reveal that the Milan CPU has 64 cores and 128 threads and AVX2 SIMD (256-bit). There is also support for 8-channel DDR ram and support for greater than 265 GB per node.  AMD unveiled plans for Zen 3 core generation codenamed Milan in 2019. The Zen 3 core is based on the 7nm+ processor node, allowing for 20% transistors than Zen 2’s 7nm process. The 7nm+ process node is also said to be built to deliver 10% better efficiency. AMD has highlighted that their processors offer much better performance per watt than industry players of similar caliber. AMD’s CTO, Mark Papermaster also revealed that Zen 3 is built on the foundations of Zen 2 (EPYC Rome) and will primarily leverage efficiency along with an uplift to overall performance.

Intel Ice Lake – CPU Family

Intel has been striving to build 10nm architecture processors for many years. In 2019, we saw the launch of the first Ice Lake laptops. But desktop 10nm chips still seemed quite distant, with Intel admitting that it has fallen years behind AMD in the race to ever smaller process technology and was considering skipping 10nm on desktop altogether. 

Intel Ice Lake is using a new core architecture referred to as Sunny Cove. Sunny Cove delivers an increased IPC of up to 18%. This will likely result in pretty decent performance gains, but the jury is still out on whether Intel’s 10nm+ for servers can clock as high as other parts that were released around the same time. But when combined with software tweaks that allow for on-the-fly adjustments to memory frequency and screen refresh rate, Ice Lake-equipped laptops should be very energy efficient. Intel confirmed there will be 10th Gen Intel Ice Lake Core processors in both the U-Series and Y-Series, with the former looking to generally offer higher frequencies while also consuming more power, consequently see battery life plummet quicker.

With these technology advancements in mind, we’re beginning to build our new product line and it’s coming soon. We are looking forward to utilizing these new technologies and others as they come to market in an effort to help government, military, higher education and commercial organizations achieve their business and organizational goals. Keep an eye out for our new releases coming soon

Artificial intelligence / AI continues to change the way many organizations conduct their work and research. Deep learning applications are constantly evolving and organizations are adapting to new technologies, improving their performance and capabilities. Companies that fail to adapt to these emerging technologies run the risk of falling behind the competition. At PSSC Labs we want to make sure that doesn’t happen to you. There is a lot going on in the world of artificial intelligence and even more to think about when building a GPU-heavy AI server or cluster system.

Five Essential Elements to an AI/GPU Computing Environment:

1. AI Applications

   The types of artificial intelligence applications you plan to run will play a significant role in determining how you build out your system. Deep learning is arguably one of the most exciting tools to be brought into the life sciences and engineering fields in recent years. Considering which applications you need can help your vendor build out the perfect artificial intelligence system for your organization. Our software engineers can help you through this critical step and the unique needs for your organization.

   Machine learning (ML) has evolved quite significantly over the past decade, and even more so in the last few years. Machine learning applications, more specifically deep learning applications which fall under the ML umbrella, can help organizations solve a wide range of problems, from science to engineering. This is done through the application of trained deep networks, something that’s only become possible through advancements in GPU parallel computation, better algorithms, and a few other significant advancements. As deep learning plays an increasingly important role in our world’s organizations, it will become more and more important day over day to consider how these technological advancements will change the field of our work.

2. GPU Needs and Capabilities

   When it comes to the right GPU selection, there are often so many choices to consider it can be difficult to know which direction is best. Among the most impressive GPU options is the NVIDIA A100, an all-around powerhouse when it comes to speed and performance. Designed specifically for scientific computing, graphics, and data analytics in data centers, the A100 GPU is “one of the best data center GPU ever made,” according to NVIDIA CEO Jensen Huang.

   With the NVIDIA A100, the right amount of computing power, memory, and scalability is delivered to help organizations tackle their massive workloads. It has more than 54 billion transistors and is the world’s largest 7nm processor. The A100 can also efficiently scale to thousands of GPUs or, with NVIDIA Multi-Instance GPU (MIG) technology, be partitioned into seven GPU instances to accelerate workloads of all sizes. Read up on other GPUs to consider.

3. HPC Cluster vs. Single Server

   Consider whether you’ll need a single AI server or a HPC Cluster. This determination will often come down to budget constraints and the amount of data you plan to ingest, store, analyze, and process. AI/HPC server platforms offer a simple way for you take control of your AI computing projects with maximum performance at the lowest cost of ownership, so a cluster is not always necessary. But just like the individual AI server, our clusters come application-optimized with popular industry applications like OpenFOAM, Ansys Fluent, Comsol Multiphysics, Matlab, and WRF

4. AI Infrastructure Needs

   When it comes to GPU-heavy systems, our primary focus as it pertains to infrastructure is typically around power and cooling. AI servers are drawing significantly more power than previous generation servers, with some of the higher-end platforms maxing out at 6000 watts. Ensuring that your facility can provide adequate power is essential in determining the size and breadth of your system.

   Your facility also needs an HVAC unit that can properly exact the heat created from these systems from the storage area. When it comes to AI system deployments, a lack of consideration on how to properly pose and cool the system can create a situation where you buy an expensive system only to learn that you can’t actually properly run it where you planned to. With a true vendor partner, this is avoided as conversations concerning these potential constraints happen upfront.

5. Budget Constraints

   Most organizations looking for an AI server will evaluate both on-premise and HPC cloud providers to do the job. The problem with cloud providers is that too often the cost you see when first deploying is not the cost you get. While on-premise systems provide stable, predictable costs overtime, cloud computing often results in 3-4x the original deployment cost within 4 years. Budget constraints can be difficult for some vendors to work with, but with a partner that works with you from day one to build a fully customized system, it’s easier to get the right equipment for less.

With years of experience providing artificial intelligence servers and clusters to organizations of all kinds, our engineers here at PSSC Labs listen to the specific needs of our clients and then work with them to customize a solution, within any time and budgetary constraints. Our AI/HPC PowerServe Uniti Servers and our PowerWulf ZXR1+ HPC Cluster are the two products that many of our customers end up purchasing, and for good reason – it’s application-optimized, scalable, and delivered production-ready.

All in all, there are several things to consider when selecting or building a custom AI system. That’s why it’s even more important to work with the right partner – one that can listen to your unique business needs and help you build a system that will perform exactly as you need it to, without the sky-high costs of Tier 1 manufacturers.

To learn more about the important considerations of your AI system or to request a quote for the PowerWulf ZXR1+ HPC Cluster, or any of our other systems, click here. 

When it comes to storing, processing, and analyzing data, there’s much for users to consider. Companies are often faced with the decision to keep data internally stored via their own data centers or allow for their data to live in the cloud. With many factors at play, we wanted to help breakdown what we’ve found to be the most important things to pay attention to.

Three Key Benefits of On-Premise Computing

1. Cost
   Cloud computing is often touted as the least costly alternative to on-premise systems, but the reality is nothing could be further from the truth! On-premise computing systems need to be purchased initially, but following that purchase they offer stable costs over time, meaning your expenses are not increasing year over year like they would in cloud.With the cloud, any increase in storage or speed would result in an immediate increase in cost. In fact, a recent technology whitepaper showed that cloud users saw an average of a 325% cost increase over a 5-year period!
2. SecurityWhen you choose to store your data in an on-premise system, you’re choosing the most secure option. You know where your data lives and you know who has access to your data centers, because it’s privately owned and operated only by your trusted technology professionals.

   With data that is stored in the cloud, it’s essentially being stored on shared servers. That means other companies, maybe even your competitors, are all storing data on shared platforms that are being entirely operated by a third-party.

3. ControlControl is ultimately retained by the enterprise throughout the lifetime of the data when an on-premise system is used. That means you can access it at any time you’d like, without the limitations of a third party. These limitations often come in the way of encryption from the cloud provider, additional costs, and time constraints, meaning more downtime and less access to the data that you already own.

As you can see, the argument for the cloud truly just doesn’t make sense for organizations that are looking to manage their expenses while still maintaining control of their data. For more information about on-premise systems, please feel free to contact our team at www.site-a.com or (949) 380-7288.

To download the infographic as a PDF, please click here.