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Tag: HPC

SpaceTech Expo Highlights Public Private Partnerships for the Final Frontier

By marketing@site-a.com

SpaceTech Expo 2022, Long Beach, CA

PSSC Labs continues to support space technology and just attended the 2022 SpaceTech Expo in Long Beach, CA. We found some phenomenal resources for the future of space, and we hope to share those with you.

One of the major takeaways from this event was the keynote given by major Adam A. Burnetta, Program Manager, Space Enterprise Consortium, United States Space Force (USSF). Major Burnetta shed light on many new initiatives the U.S. Government is doing to close supply chain gaps for advanced space research and technology. Noted by Major Burnetta is a shift in funding towards more commercial industry investment. Major Burnetta explained that Space Systems Command for the USSF wants to leverage the commercial abilities that we have seen explode in the last five years. In a clear statement Major Burnetta says, “Space Systems Command is instituting a simpler method to engage and facilitate industry partnership in order to leverage commercial innovation and investment on space technology.” Major Burnetta went on to discuss how the field offices will interact with the businesses local to them in order to facilitate direct lines of communication with the industry.

This type of government and commercial partnership outreach program is very similar to the post-war innovation period. In fact, the National Bureau of Economic Research analyzed the research and development contracts of that time and concluded that government spending for R&D in the commercial sector was valued at $7.4 billion in today’s dollar. This will be a welcome change for many businesses looking to work with the USSF. Major Burnetta’s presentation explained the government wants to adopt, buy, adapt, and create from the commercial platform in order to advanced space development. America’s goal is clear – continued superiority over the vast expanse of space, but this time with the help of businesses that make it possible.

References:

SpaceTech Expo 2022 Keynote: Major Adam A. Burnetta Program Manager, Space Enterprise Consortium, US Space Force

World War II R&D spending catalyzed post-war innovation hubs. NBER. (2020, September). Retrieved May 24, 2022, from https://www.nber.org/digest/sep20/world-war-ii-rd-spending-catalyzed-post-war-innovation-hubs

We hope to see you at the next expo! Interested in future tech updates from PSSC? Please fill out the form below to be added to our mailing list.


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How Technology Helps to Forecast Weather and Reduce Wildfire Ignitions

By marketing@site-a.com

How Big Data Is Changing Weather Modeling and Wildfire Analysis – Updated September 2020

LAKE FOREST, Calif., September 16, 2020 /PRNewswire/ — In early 2018, PSSC Labs, a developer of custom High Performance Computing and Big Data computing solutions, in collaboration with Atmospheric Data Solutions (ADS), partnered together to design, build and implement custom HPC Clusters that could be used to assist utility companies as well as public and private agencies in predicting, mitigating and managing risk from severe weather patterns. This type of technology is absolutely critical in helping municipalities and government agencies in being able to assist in predicting and addressing wildfires like those being experienced in 2020 which to date has resulted in the evacuation orders of over 500,000 in Oregon alone. PSSC Labs HPC clusters can be used to analyze real-time data near wildfires, like temperature, humidity, and wind speed to help experts predict the movement patterns of the fire.

PSSC Labs is currently working with a number of large utility companies to quickly implement high performance computing solutions that are focused specifically on weather modeling and that have the ability to predict the potential paths of wildfires.  This powerful HPC computing combination of weather modeling and wildfire analysis has the power to potentially save lives.

Accurate severe weather and wildfire predictions require technologically advanced high performance computing servers and artificial intelligence (AI) and PSSC Labs has powerful custom solutions to help meet this need.  PSSC Labs’ PowerWulf Cluster weather solutions provide cutting-edge hardware that efficiently processes large amounts of data needed to train complex AI models for valuable wildfire potential forecasts.

For example, a government website called the Santa Ana Wildfire Threat Index developed in collaboration with a major southern California utility company, the U.S. Forest Service (USFS), and ADS forecasts short-term and long-term large wildfire potential. The Santa Ana Wildfire Threat Index site advises the USFS and the public on approaching wildfire potential events using forecasted weather and wildfire-centric variables including dead and live fuel moisture, all of which are generated on PSSC Labs PowerWulf Clusters.

This highly developed solution predicted large fire potential during the busy Santa Ana wind season providing month-ahead and season-ahead forecasts that warned of above normal Santa Ana winds this past fall. The public received forecasts and recommended actions well ahead of approaching critical fire weather. The utility agency used this advanced predictive technology to create forecasts to reduce the potential for accidental wildfire ignitions in their territories.

“Big Data is playing a crucial role in weather forecasting and wildfire analysis. This advanced information is important because we cannot just look outside for the weather,” said Alex Lesser, executive vice president at PSSC Labs. “With improved technology and the ability to process large amounts of data comes better extreme weather forecasts, which can save lives.”

How HPC, Big Data and AI Impact Weather Modeling and Wildfire Analysis

Weather models require specialized computing platforms that allow parallel computations. The closer the model horizontal and vertical grid spacing, the more computational resources are needed. Timely dissemination of the high-impact forecast to stakeholders and the public also requires state-of-the-art computing hardware.  

PSSC Labs works with ADS to deliver cluster servers that are individually customized to meet the specific needs of each client. Key features of the PSSC Labs PowerWulf HPC Clusters include pre-configured and fully validated blocks with the latest Intel HPC technology and all the necessary hardware, network settings, and cluster management software prior to shipping.

Thanks to the processing power of PSSC Labs clusters, AI and Big Data can now play a key role in developing forecasting and wildfire analysis solutions that can prevent casualties. ADS uses AI to find relationships in data that can transcend the current understanding. For example, ADS uses AI to forecast damage to infrastructure during severe downslope windstorms that may occur during Santa Ana wind events.

“AI solutions are being built using multi-decadal historical atmospheric and land surface data at a close-enough grid spacing. This data allows for an intelligent historical ranking of a forecast which is one of the most valuable analytics ADS can provide a stakeholder,” said Dr. Scott Capps, Principal and Founder of ADS.   

“The solutions ADS are developing use a comprehensive blend of predictors that are important to large wildfire potential including dead fuel and live fuel moisture, near-surface atmospheric moisture and temperature, wind speed and gusts,” Dr. Capps, continues. “Our partnership with PSSC Labs provides us with the hardware platform to meet the demands of high performance computing in order to maximize accuracy and maximize the number of times models can be run daily.” 

According to Dr. Capps, the operational month-ahead and season-ahead Santa Ana wind forecasts are the first such solution.

For more information visit https://www.site-a.com/solutions/hpc-cluster/

Source: https://www.prnewswire.com/news-releases/pssc-labs-and-atmospheric-data-solutions-deliver-hpc-clusters-to-forecast-weather-and-reduce-wildfire-ignitions-300588776.html?tc=eml_cleartim

AMD EPYC Rome Processor: An Emerging Alternative for HPC

By marketing@site-a.com

The AMD second generation of EPYC processors, code-named “Rome” and based on the Zen 2 microarchitecture, are emerging as an alternative processor for high-performance computing (HPC) server applications.

The processor, which was released last August, features up to 64 cores per socket with 128 PCIe 4.0 lanes and 8-channel memory. AMD touts it as offering a very high performance per dollar in the marketplace. For organizations familiar with AMD desktop technology, EPYC Rome is to the server what AMD Ryzen 3000 was to the desktop. Namely, the second-generation AMD EPYC Rome processor offers 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.

Workloads That Will Benefit from AMD EPYC

When launched, AMD noted that Rome offers far more CPU threads per socket than Intel’s Xeon Scalable CPUs do. The processor also supports a higher DDR4 clockrate (up to 3200 MHz). Most important for demanding data center applications, the 128 PCIe 4.0 lanes each have twice the bandwidth of a PCIe 3.0 lane. Such performance is critical in HPC and large data center applications because data ingestion by CPUs is often a bottleneck. The higher performance capacity means workloads will not be degraded, and jobs will run faster. Most Host Bus Adapters may not be able to immediately take advantage of this larger pipe, but we don’t expect that to be the case for very long. Over time, more and more network adapters, controllers, etc. will be able to fully take advantage of PCI Express® 4.0.  That’s not to say that right now there are no benefits. In fact, the immediate impact is significantly more lanes for PCI devices, such as NVMe devices.  This will dramatically improve IO performance and increase the number of devices connected to the server. 

Another very basic advantage for HPC use cases is that since AMD EPYC processors have more cores than their comparable competitive processors, they deliver more computing power. Processors with more cores are ideal for advanced modeling and simulation algorithms, such as a finite element simulation. For these workloads, a job needs to use a very large number of cores at a given time. And the workloads often require large amounts of shared memory. This changes the game for HPC environments (ie. Design & Engineering, Weather Modeling, Life Science, and Artificial Intelligence), where total core count directly relates to program success. AMD® is realizing their investment in the 7 nm technology is finally paying off.

AMD EPYC processors deliver other benefits for HPC workloads and environments. By virtue of having more cores, the processors can run more virtual machines (VMs). As a result, a company gets more efficient compute resource use out of a single system.  This is ideal for virtual desktop environments (VDI) as well as converged / hyper-converged applications.

Selecting the Right Technology Platform

AMD use for HPC server workloads has had a mixed track record over the years. However, the introduction of AMD EPYC processors seems to have caught the attention of the marketplace.

Noting this interest in the user community, PSSC Labs is rolling out solutions based on the processor this year. PSSC Labs has a more than 30 years history of delivering systems that meet the most demanding workloads across industries, government, and academia.

Its offerings include the PowerServe Uniti Servers line that leverages the latest components from Intel® and Nvidia®. These servers are ideal for a wide range of applications, including AI and deep learning, as well as for computational and data analysis.

PSSC Labs also offers CloudOOP Big Data Servers that deliver the highest level of performance one would expect in an enterprise server combined with the cost-effectiveness of direct attach storage for Big Data applications. The servers deliver 200+ MB/sec sustained IO speeds per hard drive (which is 30%+ faster than other OEMs.)

Due to the increase in the number of PCI Express lanes, PSSC Labs is upgrading their CloudSeek Database Servers as well. The products we plan to bring to market in the near future will be built to support up to 36 x NVMe devices

PSSC Labs plans to have an AMD EPYC version of all its platforms for HPC and Big Data by the second half of the year. User interest is sparking this move. The company has not sold an AMD-based server in more than ten years. The impression PSSC gets from the end-user community and its own evaluation of the new processors is that AMD is a viable choice for the most demanding HPC workloads.

Infrastructure Considerations for Containers and Kubernetes

By marketing@site-a.com

Containers and Kubernetes are at the heart of a broad industry shift where applications and services are based on a microservices architecture. Specifically, microservices are being rapidly adopted as a means of building and modernizing distributed applications allowing them to be more scalable, flexible, resilient, and easier to build.

Instead of building self-contained, monolithic applications, a microservices approach breaks applications into modular, independent components that can be dynamically integrated with one another using application programming interfaces (APIs).

Increasingly, companies are using containers to power their microservices application architectures. Containers encapsulate a lightweight runtime environment for an application. Specifically, containers enable finer-grained execution environments, permit application isolation, and are lightweight.

Furthermore, containers include everything needed to run, such as code, dependencies, libraries, binaries, and other elements. Today, Docker is the most popular choice for building and running containers.

Compared to Virtual Machines, containers share the OS kernel instead of having a full copy of it and take up less space. Because they do not require OS spin-up time associated with a VM, containers initialize faster. In general, containers start in seconds or even milliseconds, which is much faster than VMs. As such, containers deliver performance characteristics that match the needs of a microservices architecture. In particular, the quick instantiation maps better to the unpredictable workload characteristics associated with microservices. 

The growing embracement of containers was validated in a 2019 industry container usage survey that found the median number of containers per host doubled (to 30) between 2018 and 2019. And the maximum per-node density was 250 containers, which was a 38% increase from 2018.

Managing Your Containers

With such explosive growth in the use of containers, companies need a way to oversee and manage their efforts. That’s where Kubernetes comes in.

Kubernetes is an open-source container orchestrator system for automating deployment, scaling, and management of application containers across clusters of hosts. It was originally designed by Google and is now maintained by the Cloud Native Computing Foundation. Kubernetes works with a range of container tools, including Docker. It groups containers that make up an application into logical units for easy management and discovery.

Kubernetes provides a framework to run distributed systems resiliently. It takes care of scaling and failover for an application. For example, in a production environment, Kubernetes can start a new container if one goes down. Thus, helping to ensure there is no application downtime. Additionally, Kubernetes provides service discovery and load balancing, storage orchestration, automated rollout and rollbacks, self-healing features, configuration management, and more.

While there are a handful of container orchestrators available today, Kubernetes dominates the market. In addition to the widely used open-source variant, some commercial offerings such as Red Hat OpenShift are built on Kubernetes. (The commercial offers add enterprise features and support.)

Kubernetes can be deployed on a bare-metal cluster or on a cluster of virtual machines. Kubernetes, in turn, can orchestrate the containers it manages directly on bare metal or on virtual machines. Most instances of Kubernetes today are run on VMs running on-premises or in the cloud.

Bare-metal instances are not as common. However, there are use cases where they offer advantages. For example, a network edge application might be too latency-sensitive to tolerate the overhead created by a VM. Or an application (such as machine learning) might need to run on GPUs or other hardware accelerators, which do not lend themselves to VMs.

Optimized, Integrated Solutions

Running container workloads comes down to hardware. Businesses need physical machines, with CPUs, memory, and local persistent storage. In addition, they need some shared persistent storage and networking element to hook up all the machines.

A suitable system must be able to be dynamically provisioned by the users to handle different data workflows. Many companies are looking for turnkey solutions that combine the needed processing, storage, memory, and interconnect technologies to provide either the bare metal or VM foundation for their container and microservices efforts. Delivering such a solution requires expertise and real-world best practices across both HPC and container/Kubernetes domains, plus deep industry knowledge about the specific applications.

PSSC Labs has a more than 30 years history of delivering systems that meet the most demanding workloads across industries, government, and academia. Its offerings include the PowerServe Uniti Servers line that leverages the latest components from Intel® and Nvidia®. These servers are ideal for a wide range of applications, including AI and deep learning, as well as for computational and data analysis.

PSSC Labs also offers CloudOOP Big Data Servers that deliver the highest level of performance one would expect in an enterprise server combined with the cost-effectiveness of direct attach storage for Big Data applications. The servers deliver 200+ MB/sec sustained IO speeds per hard drive (which is 30%+ faster than other OEMs.)

As the number of containers per host grows and Kubernetes use grows, these solutions and other PSSC Labs systems are designed to meet the requirements of enterprises today. Such systems will increasingly become more important as companies explore new ways to make use of the containers, Kubernetes, and microservices to serve their users better and quickly react to new business opportunities.

Supercomputing for Design & Engineering SMBs

By marketing@site-a.com

Supercomputing is most commonly thought of as a tool only for the largest government agencies, top commercial companies, and most prestigious universities — not your everyday small and medium-sized businesses (SMBs). The focus of supercomputing is typically on the largest systems, which scale well beyond many thousands of processor cores. Many SMBs believe supercomputing capabilities to be well beyond their reach, but nothing could be further form the truth. SMBs need a competitive advantage to obtain government and commercial contracts, and that advantage is a powerful, scalable, high performance supercomputing platform.

Over the past 12 months, interest and deployment of high performance computing clusters at SMBs performing computational fluid dynamics (CFD), finite element analysis (FEA) and structural engineering work have trended significantly upward. Without HPC systems, the work these organizations are accomplishing would be nearly impossible. But with the right systems, it could be the lifeline to their organizational success. “Supercomputing is not just for the large and powerful organizations,” says Alex Lesser, Vice President of PSSC Labs. “Supercomputing is an accessible tool for everyone. Small and Medium Businesses need to leverage these tools to ensure their success, and it does not have to be costly or intimidating.”

PSSC Labs works closely with several trusted leaders in the design and engineering space. These organizations develop state-of-the-art physics-based models for test programs, modeling and simulation analysis solutions for highly dynamic events and reactivity of energetic materials, and much more. Many of our clients also design simulation software in multiple areas of physics, including computational fluid dynamics (CFD), aero and hydro acoustics, and more. The technology these best-of-breed design and engineering firms have developed are applicable to many areas of interests to scientists, engineers, technologists, and educators. We’re proud to support game-changing organizations with the necessary HPC hardware systems they need to build the best of the best, while giving them immediate, frontend access to a variety of CFD applications, including Ansys Fluent, Star CCM, CFD++, and OpenFOAM, as well as Finite Element Analysis (FAE) applications, like Abaqus FEA, COMSOL Multiphysics and SimScale.

POWERWULF ZXR1+ HPC CLUSTER

Starter configuration for under $100k

  • 200 Intel or AMD Processor Cores
  • 4 GB of Memory Per Processor Core
  • 100 Gbps High Speed Network
    (Intel Omnipath or Mellanox Infiniband)
  • 10 TB High Performance Flash Storage
  • 40 TB Long Term / Secondary Storage
  • CBeST Cluster Management Toolkit
    • Linux Cluster Operating System
    • Message Passing Libraries
    • Batch Scheduled
  • Complete 3 Year Service Level Agreement

engineering hpc cluster

While some SMBs might be hesitant to deploy an on-premise HPC platform due to the lack of an internal IT department or perceived level of complexity, these concerns are easily put to rest when partnering with the right partner. PSSC Labs’ goal is to deliver the most turn-key, highest performance, head-ache free HPC experience possible. Our PowerWulf ZXR1+ Clusters include all necessary hardware, software, and networking integrated by 20+ year HPC experts. “We are very proud of our 57 step testing and integration process, which is considered one of the industry’s most rigorous,” begins Larry Lesser, PSSC Labs Chief Technology Officer. “The results of this painstaking process are evident with a record of never delivering a DOA system and a consistently proven 99.99% system uptime.”  PSSC Labs stands firmly behind their product with a complete hardware and software service level agreement for up to five years. Because PSSC Labs is both the manufacturer of the hardware and developer of system software, SMBs have one phone number to call for immediate access to knowledgeable support.

Cloud providers and brokers will continue to try scaring SMBs into believing that they are incapable of supporting an on-premise HPC solution. They may argue the merits of using offerings from Amazon, Microsoft and Google will save companies significant cost, offer superior performance and greater scalability, but they’re wrong. SMBs in the Design & Engineering space are comprised of very intelligent and experienced engineers who can evaluate the numbers on their own and are not easily swayed. Companies who choose the cloud over on-premise models are accepting ever-expanding monthly bills, loss of control and governance, and lower security, while allowing their data to be completely at the mercy of a third party. With the threat of cyber attacks ever-looming, putting critical and confidential data on the cloud is possibly one of the most irresponsible things SMBs could do. A quick read of the “Cloud Hopper” investigation by the Wall Street Journal should serve as a stark warning.

More and more SMBs are deploying HPC systems than ever before. On-premise, high-performance supercomputers are well within the grasp of design & engineering organizations of all sizes. SMBs need to utilize these tools to develop new products, win more contracts and push their businesses forward.

Scaling Machine Learning Systems for the Data Scientist

By marketing@site-a.com

The use of machine learning (ML) is on the rise. From a data scientist’s perspective, the computing challenge is how to scale the ingesting of more data in faster times to train machine learning algorithms, as well as how to scale processing power. Looking deeper, the issue is that ML applications parse ever-growing amounts of data requiring enormous parallel processing capabilities using large numbers of cores.

Traditional computing systems based on standard CPUs will not suffice. They cannot process the data, train the machine learning algorithms, or run the ML applications against new data in an efficient manner. In most cases, scaling legacy systems to the processing level required is too costly. Even if the investment is made, the time it takes to train and run ML applications is impractical for the needs of the business.

What’s needed is an infrastructure update that delivers the required parallel processing performance at a reasonable cost. In most cases, the best solution is one that combines multithreaded CPUs with GPUs, large memory, high-performance interconnects, and HPC storage solutions with high I/O and throughput, plus low latency features.

What’s Different?

Organizations have been scaling their systems for things like Big Data and the use of more sophisticated analytics for years. In most cases, solutions based on traditional CPU architectures were enough.

Why is machine learning different? Why do these traditional solutions not fit the bill?

Many organizations found their installed systems were hitting a wall because of the amounts of data involved and the nature of the ML algorithms. Training models took too long to run due to computing limitations.

When confronted with this problem, organizations have looked for systems that lend themselves to the requirements of ML and machine learning algorithms. Such systems often include HPC servers with greater processor performance, systems that scale up (vs. scale-out), I/O solutions with bandwidth, and accelerator technologies such as GPUs or FPGAs.

Solutions with these characteristics get to the heart of the problem for ML / Machine Learning — core starvation. CPUs are designed for serial processing. Machine learning training and applications must be done in parallel on many more cores than CPUs can provide. Accelerators overcome this problem. GPUs offer thousands of cores, and custom-designed processors (ASIC, FPGAs) complement CPU processing capabilities.

Such accelerators offer a massively parallel architecture that economically delivers the needed parallel compute performance. Going hand-in-hand with the use of accelerators, systems that can scale to meet the demands of ML applications also must have high-speed interconnects, increased memory size, and fast storage.

Technology from an Experienced Partner

PSSC Labs has delivered tens of thousands of custom-engineered HPC servers to higher education, government agencies, small/medium businesses, and large enterprise organizations across 36 countries.

PSSC Labs delivers integrated HPC / High Performance Computing solutions for ML that tightly integrate and optimize hardware and software. Its PowerServe Uniti Servers include the latest components from Intel® and Nvidia®.

PSSC Labs GPU options include:

  • NVIDIA Tesla P100 GPU accelerators for PCIe based servers. Tesla P100 with NVIDIA NVLinkdelivers up to a 50X performance boost for the top HPC applications and all deep learning frameworks.
  • NVIDIA Tesla V100 Tensor Core, powered by NVIDIA Volta architecture, is a data center GPU to accelerate HPC and AI workloads.
  • NVIDIA T4 GPU, which accelerates cloud workloads, is used for HPC, deep learning training, and inference, machine learning, and data analytics.
  • GEFORCE RTX 2080 Ti is NVIDIA’s flagship graphics card based on NVIDIA Turing™ GPU architecture and ultra-fast GDDR6 memory.

Systems that use these and other GPUs to scale ML workloads need high-performance interconnect technologies to make cost-effective use of their performance capabilities. Interconnect technologies available include InfiniBand, Omni-Path, and remote direct memory access (RDMA).

Most important, PSSC Labs’ family of HPC systems are Machine Learning ready systems that are purpose-built for an organization’s needs. The solutions come production ready, which is known to be critical from a data scientist’s perspective. PSSC Labs does not need to spend time on IT issues, as clients are assured the systems they are using for their ML efforts can scale to meet the demands of their applications. 

To learn more about our HPC systems specifically designed to meet the need of data scientists across various industries, click the button below to schedule a meeting with one of our knowledgable Solutions Architects. 


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