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The Perfect Combination: The Weka Next-Gen File System, Supermicro A+ Servers and AMD EPYC™ CPUs

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The Perfect Combination: The Weka Next-Gen File System, Supermicro A+ Servers and AMD EPYC™ CPUs

Weka’s file system, WekaFS, unifies your entire data lake into a shared global namespace where you can more easily access and manage trillions of files stored in multiple locations from one directory.

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One of the challenges of building machine learning (ML) models is managing data. Your infrastructure must be able to process very large data sets rapidly as well as ingest both structured and unstructured data from a wide variety of sources.

 

That kind of data is typically generated in performance-intensive computing areas like GPU-accelerated applications, structural biology and digital simulations. Such applications typically have three problems: how to efficiently fill a data pipeline, how to easily integrate data across systems and how to manage rapid changes in data storage requirements. That’s where Weka.io comes into play, providing higher-speed data ingestion and avoiding unnecessary copies of your data while making it available across the entire ML modeling space.

 

Weka’s file system, WekaFS, has been developed just for this purpose. It unifies your entire data lake into a shared global namespace where you can more easily access and manage trillions of files stored in multiple locations from one directory. It works across both on-premises and cloud storage repositories and is optimized for cloud-intensive storage so that it will provide the lowest possible network latencies and highest performance.

 

This next-generation data storage file system has several other advantages: it is easy to deploy, entirely software-based, plus it is a storage solution that provides all-flash level performance, NAS simplicity and manageability, cloud scalability and breakthrough economics. It was designed to run on any standard x86-based server hardware and commodity SSDs or run natively in the public cloud, such as AWS.

 

Weka’s file system is designed to scale to hundreds of petabytes, thousands of compute instances and billions of files. Read and write latency for file operations against active data is as low as 200 microseconds in some instances.

 

Supermicro has produced its own NVMe Reference Architecture that supports WekaFS on some of its servers, including the Supermicro A+ AS-1114S-WN10RT and AS-2114S-WN24RT using the AMD EPYC™ 7402P processors with at least 2TB of memory, expandable to 4TB. Both servers support hot-swappable NVMe storage modules for ultimate performance. Also check out the Supermicro WekaFS A/I and HPC Solution Bundle.

 

 

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Supermicro SuperBlades®: Designed to Power Through Distributed AI/ML Training Models

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Supermicro SuperBlades®: Designed to Power Through Distributed AI/ML Training Models

Running heavy AI/ML workloads can be a challenge for any server, but the SuperBlade has extremely fast networking options, upgradability, the ability to run two AMD EPYC™ 7000-series 64-core processors and the Horovod open-source framework for scaling deep-learning training across multiple GPUs.

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Running the largest artificial intelligence (AI) and machine learning (ML) workloads is a job for the higher-performing systems. Such loads are often tough for even more capable machines. Supermicro’s SuperBlade combines blades using AMD EPYC™ CPUs with competing GPUs into a single rack-mounted enclosure (such as the Supermicro SBE-820H-822). That leverages an extremely fast networking architecture for these demanding applications that need to communicate with other servers to complete a task.

 

The Supermicro SuperBlade fits everything into an 8U chassis that can host up to 20 individual servers. This means a single chassis can be divided into separate training and model processing jobs. The components are key: servers can take advantage of the 200G HDR InfiniBand network switch without losing any performance. Think of this as delivering a cloud-in-a-box, providing both easier management of the cluster along with higher performance and lower latencies.

 

The Supermicro SuperBlade is also designed as a disaggregated server, meaning that components can be upgraded with newer and more efficient CPUs or memory as technology progresses. This feature significantly reduces E-waste.


The SuperBlade line supports a wide selection of various configurations, including both CPU-only and mixed CPU/GPU models, such as the SBA-4119SG, which comes with up to two AMD EPYC™ 7000-series 64-core CPUs. These components are delivered on blades that can easily slide right in. Plus, they slide out as easily when you need to replace the blades or the enclosure. The SuperBlade servers support a wide network selection as well, ranging from 10G to 200G Ethernet connections.

 

The SuperBlade employs the Horovod distributed model-training, message-passing interface to let multiple ML sessions run in parallel, maximizing performance. In a sample test of two SuperBlade nodes, the solution was able to process 3,622 GoogleNet images/second, and eight nodes were able to scale up to 13,475 GoogleNet images/second.


As you can see, Supermicro’s SuperBlade improves performance-intensive computing and boosts AI and ML use cases, enabling larger models and data workloads. The combined solution enables higher operational efficiency to automatically streamline processes, monitor for potential breakdowns, apply fixes, more efficiently facilitate the flow of accurate and actionable data and scale up training across multiple nodes.

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Fast Supermicro A+ Servers with Dual AMD EPYC™ CPUs Support Scientific Research in Hungary

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Fast Supermicro A+ Servers with Dual AMD EPYC™ CPUs Support Scientific Research in Hungary

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The Budapest Institute for Computer Science and Control (known as SZTAKI) conducts a wide range of scientific research spanning the fields of physics, computer science, industrial controls and intelligent systems. The work involves medical image processing, autonomous vehicles, robotics and natural language processing, all areas that place heavy demands on computing equipment and a natural use case for performance-intensive computing.


SZTAKI has been in operation since 1964 and has more than 300 full-time staff, with more than 70 of them holding science-related degrees. It works with both government and other academic institutions jointly on research projects as well as contract research and development of custom computer-based applications.

The institute also coordinates similar types of work done at Hungary’s AI national lab. For example, there are several projects underway to develop AI-based solutions to process the Hungarian language and build computational-based models that can be more effective and not require as much training as earlier models. They are also working on creating more transparent and explainable machine learning models to make them more reliable and more resilient in preserving data privacy.

SZTAKI has been using Supermicro’s A+ 4124GO-NART servers with GPUs that are configured with two AMD EPYC™ 7F72 CPUs. “Our researchers are now able to advance our use of AI and focus on more advanced research," said Andras Benczur, scientific director at the AI lab. One challenges they face is keeping up with the advanced algorithms that its researchers have developed. Having the Supermicro servers, which operate at 20x the speed of previous servers, means that researchers can execute coding and modeling decisions far more quickly.

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Performance-Intensive Computing Helps Lodestar Computer Vision ‘Index’ Video Data

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Performance-Intensive Computing Helps Lodestar Computer Vision ‘Index’ Video Data

Lodestar is a complete management suite for developing artificial intelligence-based computer vision models from video data. It can handle the navigation and curation of a native video stream without any preparation. Lodestar annotates and labels video, and using artificial intelligence, creates searchable, structured data.

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Lodestar doesn’t call it indexing, but the company has a product that annotates video, and using artificial intelligence (AI), creates searchable, structured data. Lodestar offers a complete management suite for developing AI-based computer vision models from video data. The company’s technology includes continuous training of its AI models along with real-time active learning and labeling.

 

The challenge for computer vision efforts before Lodestar's technology came into the picture was the sheer amount of data contained in any video stream: an hour of video contains trillions of pixels. The result was a very heavy computational load to manipulate and analyze. That meant video had to be pre-processed before anyone could analyze the stream. But thanks to performance-intensive computing, there are new ways to host more capable and responsive tools.

 

That's where Lodestar comes into play, handling the navigation and curation of a native video stream without any preparation, using the video as a single source of truth. Metadata is extracted on the fly so that each video frame can be accessed by an analyst. This is a highly CPU-intensive process, and Lodestar uses Supermicro A+ servers running Jupyter’s data science applications across a variety of containers. These servers have optimized hardware that combines AMD CPU and GPU chipsets with the appropriate amount of memory to make these applications function quickly.

 

By harnessing this power, data scientists can now collaborate in real time to validate the dataset, run experiments, train models and guide annotation. With Lodestar, data scientists and domain experts can develop a production AI in weeks instead of months.

 

That’s what a leading European optical and hearing aid retailer did to help automate its in-store inventory management processes and keep track of its eyewear collection. Before the advent of Lodestar, each store’s staff spent 10 hours a month manually counting inventory. That doesn’t sound like much until you multiply the effort by 300 stores. With Lodestar, store inventory is completed in minutes. Given that the stores frequently update their product offerings, this has brought significant savings in labor, and more accurate inventory numbers have provided a better customer experience.

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Lawrence Livermore Labs Advances Scientific Research with AMD GPU Accelerators

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Lawrence Livermore Labs Advances Scientific Research with AMD GPU Accelerators

The Lawrence Livermore National Lababoratory chose to use a cluster of 120 servers running AMD EPYC™ processors with nearly 1,000 AMD Instinct™ GPU accelerators. The hardware, facilitated by Supermicro, was an excellent match for the molecular dynamics simulations required for the Lab's cutting-edge research, which combines machine learning with structural biology concepts.

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Lawrence Livermore National Laboratory is one of the centers of high-performance computing (HPC) in the world and it is constantly upgrading its equipment to meet increasing computational demands. It houses one of the world's largest computing environments. Among its more pressing research goals derives from the COVID-19 crisis.

Lawrence Livermore researches and supports proposals from the COVID-19 HPC Consortium, which is composed of more than a dozen research organizations across government, academia and private industry. It aims to accelerate disease detection and treatment efforts, as well as to screen antibody candidates virtually and run several disease-related mathematical simulations.

"By leveraging the massive compute capabilities of the world’s [more] powerful supercomputers, we can help accelerate critical modeling and research to help fight the virus," said Forrest Norrod, senior vice president and general manager, AMD Datacenter and Embedded Systems Group.

The lab chose to use a cluster of 120 servers running AMD EPYC™ processors with nearly 1,000 AMD Instinct™ GPU accelerators. The servers were connected by Mellanox switches. The product choices had two benefits: First, the hardware, facilitated by Supermicro, was an excellent match for the molecular dynamics simulations required for this research. The lab is performing cutting-edge research that combines machine learning with structural biology concepts. Second, the gear was tested and packaged together, so it could become operational when it was delivered to the lab.

AMD software engineers and application specialists were able to modify components to run GPU-based applications. This is top-of-the-line gear. The AMD accelerators deliver up to 13.3 teraFLOPS of single-precision peak floating-point performance combined with 32GB of high-bandwidth memory. The scientists were able to reduce their simulation run-times from seven hours to just 40 minutes, allowing  them to test multiple modeling iterations efficiently.

For more information, see the Supermicro case study and Lawrence Livermore report.

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