Artificial Intelligence (AI) seems to have become the buzzword of the last decade and not without good reason. Some of the most notable technological advancements over the last 10 years can be attributed to emerging, robust (review link comment to Wikipedia)– many that have settled into our daily lives without us even noticing their arrival.

This is why it’s not uncommon for firms to leverage AI-focused workstations as a critical part of their AI workflows. AI workstations can handle all of the core AI processes that are needed for a variety of industries including engineering, manufacturing, medical, energy, higher education institutional research, earth sciences and more. The sky is truly the limit with workstations empowered by artificial intelligence and  , but this is only true when those AI workstations are developed with a thorough understanding of your business and vertical market. Firms deploying AI workstations need vendors that understand their unique workflow and business demands. The artificial intelligence partner needs to be able to digest this information and then utilize it to build a custom AI system that is comprehensively built for performance, speed, and reliability, and ideally one that is also delivered application-ready.

Even with the right artificial intelligence vendor, it’s usually best for the client to work through a checklist of their own to ensure that the system being built on their behalf will have the right specifications required to be successful in their unique situation.  The PSSC Labs AI team have compiled the following four important considerations that should be focused upon when working with a AI partner, like PSSC Labs, to develop the perfect AI workstation.

Your Checklist for Building AI Workstations

1. Prepare Today for the Future of AI

Those working in the tech industry would say that the – see link comments is going to be all about its ability to support a complex and diverse business infrastructure with near full autonomy. In reality, this vision is still years away – but that doesn’t mean that firms aren’t making large strides towards that reality. AI practitioners should take a forward-thinking, focused approach to how they will utilize and implement their AI applications.  The PSSC Labs team has some excellent resources that can help support you and your organization with this process so email us today at sales@site-a.com to receive our latest AI Workstation future planning guide.

2. Consider Existing and Future Artificial Intelligence Infrastructure Needs

AI workflows are best supported when they are built to be integrated with existing or future high-performance computing infrastructure. Keeping this in process in mind when building and deploying AI technologies using workstations that will be able to fully assist you and your organization with the focus of streamlining future transitions. As COVID-19 continues to disrupt the way that firms operate, these transitions between on premise systems, private cloud and public cloud are sure to come.

3. Focus on Security

With COVID-19, we saw employees transferring their workstations into their at-home setup at recording breaking numbers. This shift in the way firms are working has meant that security has never been more important, particularly if the firm possesses customer or other sensitive data.

Most AI decision makers would agree that security, performance, and cost are the most important features to consider when choosing an AI system as demonstrated by a Gartner report on AI security published in 2020. We recommend that users make security capabilities a key priority. Afterwards, use cases can be assessed to balance between speed and cost. With the right AI/HPC partner like PSSC Labs, you shouldn’t have to sacrifice performance or security simply due to budget.

4. Carefully Select the Right Components for Your AI Needs

The right components will make all the difference in the livelihood of your system. As the brains of the system, CPU selection continues to be one of the most important elements in your AI workstation. The Intel Xeon® Scalable Processor delivers truly breakthrough performance for handling big data tasks such as real-time analytics, virtualized infrastructure and high-performance computing.

In addition to advanced architecture, Intel Xeon® processors feature a rich suite of platform and security innovations for enhanced application performance, including Intel AVX-512, Intel Mesh Architecture, Intel QuickAssist, Intel Optane SSDs and Intel Omni-Path Fabric.

The AMD EPYC Rome Processor, based on the Zen 2 microarchitecture is another viable option. AMD touts this product as offering a very high performance per dollar in the marketplace, with significantly improved instructions per cycle over the first-generation processor, as well as more cores and better thermal efficiency. Additionally, the processor features a higher base frequency and a higher boost frequency than the previous AMD top-of-the-line processor.

While CPU selection is significant, one should also be mindful in selecting the right GPU option. One of our favorite GPU options is the NVIDIA A100, an all-around powerhouse built for speed and performance. The A100 is designed for scientific computing, graphics, and data analytics in data centers and touted as the “best data center GPU ever made” by NVIDIA CEO Jensen Huang.

With the NVIDIA A100, the right amount of computing power, memory, and scalability is delivered to tackle massive workloads. There are more than 54 billion transistors and it’s 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.

PSSC Labs takes a unique approach to developing custom Artificial Intelligence workstation solutions for organizations looking to deploy AI workstations. Our experts take your unique vision and business needs and then design a custom system architected with the latest CPU technology for your AI workflow. With a California-based support team of experienced AI engineers we are here for you and your organization. To discuss your situation in more detail and to receive a free quote for your customized AI workstation solution please call us today at 949-380-7288 or email us at 4sales@site-a.com.  We can not only provide you with a custom quote for your AI workstation but also discuss other possible workstation options you may wish to consider.

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. 

Building a high performance computing cluster for research university purposes is no insignificant task. There is much to be considered when deciding how to build out the right university HPC cluster for your important work. But how do you weigh specific factors against each other and determine what to focus on?

5 Things to Consider when Building an HPC Cluster for University Research

1. Determine the community that is going to utilize the resource.
   Is the resource going to be used by an individual? A research group with a number of users? A specific department? A campus wide system? This is important to note from the get-go, because the larger the network of the system, the more complex the conversations need to be in designing it. If multiple departments are going to be relying on the system, there will be several inputs to consider in determining which capabilities the system will require.
2. Determine how you’re going to get funding for the cluster. 
   Is your university HPC system going to be funded by your university, NSF grants, start up packages, or something different? How will the resources be financed? This is significant in ensuring that the financier receives adequate resources in return.
3. Begin to design your cluster based upon the two first steps while maintaining a keen focus on potential growth in the future.
   Keeping in mind potential future growth is critical to ensure that the purchased system will be useful 5 years down the road. For example, with a bandwidth limited network, other components added later may not provide anticipated performance improvements and will just bottleneck your work.
4. Continue exploring new technologies.
   It’s always a good idea to keep an eye on new advancements in components that are coming to market. Next generation GPUs are developed regularly and tend to be a significant focus for research universities when building an HPC cluster. Advancements in parallel file systems are also important to identify because they allow universities an expandable storage system.
5. Determine who will administer the cluster and be responsible for operation and maintenance.
   Will your university HPC cluster be managed by a dedicated resource? Will it be department personnel or even a student? You’ll want to make sure the person managing the system is well-versed in the technology and hardware. Student labor, while affordable and temporarily convenient, is not always the best option, as they may not have the necessary knowledge and need to be replaced when they leave the university.

As you can see, there is definitely a lot to consider when building out a cluster for research university purposes. That’s why it’s so important to pick the right vendor to partner with, one that will listen to what your needs are and create a system to match. PSSC Labs does just that, working closing with you throughout the entire process, ultimately delivering a production-ready system, regardless of time and budgetary constraints.

For more information on how we help research universities acquire the research instruments they need, visit us at: https://pssclabs.losangeles.dev.buckupstudio.com/industries/higher-education/