# High-Performance Computing Published articles for High-Performance Computing. This is one page of public article previews, not the complete archive. Follow Next page to continue. Summaries are not the original full articles. ## Cornelis and Delos Data propose open alternatives to Nvidia's NVLink for AI scale-up networking DevFeed: [Cornelis and Delos Data propose open alternatives to Nvidia's NVLink for AI scale-up networking]() Original publisher: [Read original article]() Author: Tobias Mann Published: 2026-09-15T20:56:48Z Content type: news Language: en Sources: [www.theregister.com - Articles]() Topics: [NVLink](), [AI Networking](), [Nvidia](), [networking](), [Hardware](), [High-Performance Computing]() Tags: [ai](), [ai-networking](), [cornelis-networks](), [delos-data](), [hardware](), [high-performance-computing](), [network](), [networking](), [nvidia](), [nvlink](), [performance](), [software](), [systems]() ### AI overview The article reports that Cornelis Networks and Delos Data have entered the AI scale-up networking market with proposed alternatives to Nvidia's NVLink. Cornelis introduced its open Active Compute Fabric architecture, which combines programmable in-network compute with scale-up and scale-out networking, while the broader market is developing alternatives using protocols such as Ultra Ethernet and UALink. ### Source excerpt Intel spin-off Cornelis and newcomer Delos Data pitch open alternatives for scaling AI beyond the rack ## NVIDIA to Acquire Hugging Face DevFeed: [NVIDIA to Acquire Hugging Face]() Original publisher: [Read original article]() Author: 黄仁勋 Published: 2026-09-03T11:56:49Z Content type: news Language: en Sources: [NVIDIA Blog]() Topics: [hugging face](), [datasets](), [Deployment](), [cloud-infrastructure]() Tags: [ai](), [artificial-intelligence](), [cloud](), [corporate](), [datasets](), [deployment](), [developers](), [high-performance-computing](), [hugging-face](), [models](), [nvidia](), [open-source](), [platform](), [software](), [supercomputing]() ### AI overview NVIDIA says it has agreed to acquire Hugging Face and plans to scale its platform and infrastructure. The announcement says Hugging Face will remain open, supporting model, framework, cloud, inference-provider and hardware choices across the AI ecosystem. ### Source excerpt I'm excited to announce that NVIDIA has agreed to acquire Hugging Face for $12,930,300,000. Together, we will scale Hugging Face's platform, strengthen its infrastructure and expand access to AI for developers and institutions worldwide. Over the past decade, Clem, Julien, Thomas and the team at Hugging Face have built something remarkable: a vibrant home for [...] ## VDURA Deploys High-Performance Storage Platform for AI and HPC at New Mexico State University DevFeed: [VDURA Deploys High-Performance Storage Platform for AI and HPC at New Mexico State University]() Original publisher: [Read original article]() Author: Harold Fritts Published: 2026-09-02T10:00:00Z Content type: news Language: en Sources: [StorageReview.com]() Topics: [Artificial Intelligence](), [High-Performance Computing](), [Data Infrastructure](), [InfiniBand](), [Post-quantum cryptography](), [Deployment](), [Training AI Models](), [Hardware](), [Low Latency](), [Quantum Computing]() Tags: [ai](), [data-infrastructure](), [deployment](), [enterprise](), [enterprise-storage](), [hardware](), [high-performance-computing](), [infiniband](), [low-latency](), [post-quantum-cryptography-pqc](), [quantum](), [storage]() ### AI overview VDURA has moved its storage platform at New Mexico State University into full production to support AI and high-performance computing research. The deployment combines NVMe flash and high-density HDD tiers through a global namespace over InfiniBand, allowing separate scaling of performance and capacity. It also supports NMSU's post-quantum cryptography research and large-scale data pipeline projects. ### Source excerpt VDURA has completed the deployment of its data platform at New Mexico State University (NMSU), moving the system into full production. The infrastructure is designed to serve the university's research community with a high-durability, high-throughput storage environment tailored specifically for artificial intelligence and high-performance computing (HPC) workloads. NMSU, which holds Carnegie R1 status and manages The post VDURA Deploys High-Performance Storage Platform for AI and HPC at New Mexico State University appeared first on StorageReview.com. ## Quantum-Augmented Applications: Integrating Quantum Subroutines into Classical Software Stacks DevFeed: [Quantum-Augmented Applications: Integrating Quantum Subroutines into Classical Software Stacks]() Original publisher: [Read original article]() Author: Dr. Ahmad Mateen Ishanzai Published: 2026-08-20T18:43:39Z Content type: article Language: en Sources: [Stack Overflow Blog]() Topics: [Software](), [Optimization](), [Hardware](), [coding](), [Network]() Tags: [buiilding-software](), [cc-by-sa](), [combinatorial-optimization](), [contributed](), [high-performance-computing](), [latency](), [low-latency](), [optimization](), [quantum](), [quantum-computing]() ### AI overview This article presents quantum-augmented applications as hybrid systems that integrate Quantum Processing Units with classical software pipelines. It describes delegating targeted computationally difficult subroutines to QPUs while retaining classical business logic, data preprocessing, orchestration, and optimization, and discusses practical constraints including noise, transpilation latency, and network overhead. ### Source excerpt Founded in 2008, Stack Overflow's public platform is used by nearly everyone who codes to learn, share their knowledge, collaborate, and build their careers. ## IBM commits $50M in quantum access for US Genesis Mission DevFeed: [IBM commits $50M in quantum access for US Genesis Mission]() Original publisher: [Read original article]() Published: 2026-07-22T20:00:00Z Content type: release Language: en Sources: [IBM Research]() Topics: [ibm](), [Artificial Intelligence](), [Quantum Computing](), [High-Performance Computing](), [Supercomputing]() Tags: [accelerate](), [ai](), [department-of-energy](), [discovery](), [government](), [high-performance-computing](), [ibm](), [news](), [project](), [quantum](), [supercomputing](), [us]() ### AI overview IBM says a project was selected by the U.S. Department of Energy's Genesis Mission to accelerate AI-driven scientific discovery and will contribute up to $50 million in quantum system access. The mission combines AI, quantum computing, supercomputing, and scientific instruments. ### Source excerpt An IBM project was also selected to accelerate AI-driven quantum application discovery. ## Нейро сети для самых маленьких. Часть первая (которая после нулевой). Удобство в прокрустовом ложе оптимизации DevFeed: [Нейро сети для самых маленьких. Часть первая (которая после нулевой). Удобство в прокрустовом ложе оптимизации]() Original publisher: [Read original article]() Author: eucariot (Яндекс, Yandex Cloud & Yandex Infrastructure) Published: 2026-07-01T07:00:06Z Content type: article Language: ru Sources: [Яндекс - Как мы делаем Яндекс / Статьи]() Topics: [High-Performance Computing](), [GPU](), [InfiniBand](), [NVLink](), [Linux]() Tags: [ethernet](), [gpu](), [gpudirect-rdma](), [high-performance-computing](), [infiniband](), [linux](), [nvidia](), [nvlink](), [performance](), [rdma](), [roce](), [tcp](), [zero-copy]() ### AI overview This introductory article in a series explains the infrastructure used to train and run neural networks and for high-performance computing. It surveys specialized technologies including GPUs and TPUs, RDMA, kernel bypass, NVLink, InfiniBand, and RoCE, arguing that specialized solutions can outperform and cost less than a generic Linux and Ethernet/IP stack at scale. ### Source excerpt Это первая (после нулевой) статья из серии Нейро сети для самых маленьких, в которой мы разбираем инфраструктуру для запуска нейронных сетей. Для обучения и инференса нейросетей и для любых видов High Performance Computing используются специализированные технологии: GPU/TPU, RDMA, Kernel bypass, NVLink, InfiniBand, RoCE и другие. Про некоторые из них большинство только что-то слышали, но сталкиваться с ними не приходилось. Нельзя просто взять ванильный стек Linux, воткнуть в него 400Gb Ethernet+IP и получить рабочее решение. Почему? Потому что общее решение на масштабе в большинстве случаев проигрывает специализированным как в скорости, так и в стоимости. Как бы странно последнее ни звучало. Читать далее ## Inspect Volcano workloads faster with Headlamp DevFeed: [Inspect Volcano workloads faster with Headlamp]() Original publisher: [Read original article]() Author: Mahmoud Magdy Published: 2026-06-25T20:00:00Z Content type: article Language: en Sources: [Kubernetes Blog]() Topics: [Kubernetes](), [jobs](), [High-Performance Computing](), [Machine learning](), [Web](), [Command-line interface]() Tags: [ai-ml](), [cli](), [high-performance-computing](), [jobs](), [kubernetes](), [quotas](), [ui](), [workflow]() ### AI overview This article introduces the Volcano plugin for Headlamp, a Kubernetes web UI. The plugin brings Volcano jobs, queues, PodGroups, Pods, and events into a unified interface, helping teams inspect batch workloads, scheduling behavior, quotas, priorities, and gang scheduling for Kubernetes, AI/ML, and high-performance computing workloads. ### Source excerpt Volcano is a cloud native batch scheduler for Kubernetes, built for high-performance computing, AI/ML, and other batch workloads. Headlamp is an extensible Kubernetes web UI. With its plugin system, Headlamp can surface APIs and workflows beyond the built-in Kubernetes resources. The Volcano plugin brings core Volcano resources into Headlamp so you can inspect workload state, queue behavior, and gang scheduling details in one place. Kubernetes was originally designed around long-running services, where applications are expected to start and remain available over time. Batch, AI/ML, and HPC workloads often behave differently: jobs arrive dynamically, compete for limited resources, and may need multiple workers to start together before useful work can begin. Volcano extends Kubernetes with concepts such as queues, priorities, quotas, and gang scheduling. Instead of treating every Pod independently, Volcano schedules workloads with awareness of the job as a whole and the resources it needs to make progress. To make these workloads easier to operate and troubleshoot, the Volcano plugin brings that scheduling context directly into Headlamp. Watch this short walkthrough to see the Volcano plugin in Headlamp: Visual context helps teams understand Volcano jobs, queues, and PodGroups faster Working with Volcano often means moving across several related resources while trying to understand a batch workload. You might start with a Job, then look at the related PodGroup, inspect the Pods behind it, check the Queue, and finally return to the Job again. All of that is possible with CLI tools like kubectl and the Volcano CLI, but it can become fragmented very quickly. The Volcano plugin for Headlamp makes that workflow easier by bringing the key resources together in a single UI. Instead of reconstructing relationships manually, you can move directly between Jobs, Queues, PodGroups, Pods, and events from the same interface. Volcano introduces its own resources on top of core Kuber ## How the D. E. Shaw group powers high-cardinality observability at scale with ClickHouse DevFeed: [How the D. E. Shaw group powers high-cardinality observability at scale with ClickHouse]() Original publisher: [Read original article]() Author: ClickHouse Published: 2026-05-15T00:00:00Z Content type: article Language: en Sources: [ClickHouse Blog]() Topics: [clickhouse](), [observability](), [High-Performance Computing](), [Prometheus](), [Time Series](), [benchmarking](), [Compression](), [tracing]() Tags: [clickhouse](), [compression](), [compute](), [high-performance-computing](), [infrastructure](), [metrics](), [observability](), [performance](), [production](), [prometheus](), [scale](), [site-reliability](), [systems](), [time-series](), [tracing]() ### AI overview The D. E. Shaw group uses ClickHouse for high-cardinality observability across millions of compute workloads on its internal grid. The article describes evaluation results showing approximately 7x better performance than alternatives, production ingestion exceeding 500,000 records per second, and improved long-term capacity planning and tracing analysis. ### Source excerpt How the D. E. Shaw group replaced its previous observability platform with ClickHouse to handle high-cardinality metrics at scale, achieving 7x better query performance and enabling multi-year capacity planning across millions of compute workloads. ## Expanding our Agentic Inference Cloud: Introducing GPU Droplets Powered by AMD Instinct™ MI350X GPUs DevFeed: [Expanding our Agentic Inference Cloud: Introducing GPU Droplets Powered by AMD Instinct™ MI350X GPUs]() Original publisher: [Read original article]() Author: Waverly Swinton Published: 2026-02-19T12:30:00Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [GPU](), [Digital Ocean](), [Inference](), [Cloud](), [High-Performance Computing](), [Generative AI]() Tags: [ai-ml](), [amd](), [digitalocean](), [gpu](), [hardware](), [high-performance-computing](), [inference](), [product-updates](), [speed]() ### AI overview DigitalOcean announces new Gradient AI GPU Droplets powered by AMD Instinct MI350X GPUs. The offering targets AI inference workloads, with the article describing support for large models, larger context windows, low-latency inference, and higher token-generation throughput. ### Source excerpt As our Agentic Inference Cloud continues to grow, we're excited to announce the availability of new, high-performance GPU Droplets powered by AMD Instinct™ MI350X GPUs. By integrating these cutting-edge GPUs with our platform, we're continuing to deliver the power and scale that leading AI-native companies and builders need to run their most complex inference workloads. Optimizing production inference with AMD Instinct™ MI350X The AMD Instinct™ MI350X Series sets a new standard for generative AI and high-performance computing (HPC). Built on the AMD CDNA™ 4 architecture, these GPUs are designed for the most demanding tasks: training massive models, high-speed inference, and complex scientific simulations. The capabilities of the GPUs allow optimization for the compute bound prefill phase, while enabling high-performance inference at low latency and high token generation throughput. This provides the ability to load large models and larger context windows, leading to supporting a higher inference request density per GPU. Paired with our optimized inference platform, these feature enhancements of AMD Instinct™ MI350X GPUs offer lower latency and higher throughput. Proven results for AI innovators We've already seen what's possible when customers pair DigitalOcean's optimized platform with AMD's hardware. Earlier this year, we helped Character.AI achieve a 2X increase in production request throughput and a 50% reduction in inference costs. Now, customers like ACE Studio are using DigitalOcean software paired with AMD hardware to push the boundaries of music creation. "At ACE Studio, our mission is to build an AI-driven music workstation for the future of music creation," said Sean Zhao, Co-Founder & CTO. "As we expand our footprint on DigitalOcean, the next-generation AMD Instinct™ MI350X architecture, supported by close collaboration on inference optimization with AMD and DigitalOcean, provides us a strong foundation to push performance and cost efficiency even furthe ## DigitalOcean Announces GPU Droplets Accelerated by NVIDIA HGX B300 DevFeed: [DigitalOcean Announces GPU Droplets Accelerated by NVIDIA HGX B300]() Original publisher: [Read original article]() Author: Waverly Swinton Published: 2025-12-15T17:51:51Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [Digital Ocean](), [GPU](), [Blackwell](), [Nvidia](), [virtual machines](), [High-Performance Computing](), [Inference](), [Cloud](), [Generative AI](), [data analytics](), [Multi-GPU](), [long-context]() Tags: [ai](), [blackwell](), [compute](), [digitalocean](), [gpu](), [high-performance-computing](), [inference](), [infrastructure](), [long-context](), [multi-gpu](), [nvidia](), [product-updates](), [real-time]() ### AI overview DigitalOcean announces GPU Droplets accelerated by NVIDIA HGX B300, describing the platform's intended benefits for AI training, inference, generative AI, data analytics, and high-performance computing workloads. ### Source excerpt AI continues to evolve at an unprecedented pace, with new models and demanding workloads pushing the boundaries of what's possible. From complex large language models (LLMs) to intricate scientific simulations, developers and businesses need access to the most powerful and efficient computing infrastructure. At DigitalOcean, we're committed to providing the cutting-edge tools you need to build, deploy, and scale your AI initiatives with simplicity and affordability. That's why we're excited to announce that GPU Droplets accelerated by NVIDIA HGX™ B300 are coming soon to DigitalOcean, marking a significant upgrade to our GPU offerings. Why NVIDIA HGX™ B300? The NVIDIA Blackwell Ultra accelerated computing platform represents a leap forward in AI reasoning. Designed for both training and inference, the NVIDIA HGX B300 offers substantial improvements in computational power, memory bandwidth, and energy efficiency compared to previous generations. The NVIDIA Blackwell architecture at the heart of the HGX B300 is not just about raw power; it's also about efficiency and innovation. With 1.5X more dense Tensor Core FLOPS, enhanced attention performance, and significantly expanded memory, the HGX B300 is optimized for the most demanding AI workloads including generative AI, data analytics, and high-performance computing (HPC). Featuring 7X more AI compute than NVIDIA Hopper platforms, 2.1TB of HBM3e memory, and high-performance networking integration with NVIDIA ConnectX-8 SuperNICs, Blackwell Ultra delivers breakthrough performance on the most complex workloads from agentic systems and reasoning, to real-time video generation. For AI-native enterprises running large reasoning models and long-context workloads, this enables: -Reduced model offloading and improved time-to-first-token -Higher sustained throughput under concurrency -More efficient multi-GPU scaling -Improved tokens-per-second per dollar Unlike GPU capacity providers, DigitalOcean integrates inference-optimized ## Differentially private machine learning at scale with JAX-Privacy DevFeed: [Differentially private machine learning at scale with JAX-Privacy]() Original publisher: [Read original article]() Published: 2025-11-12T15:32:00Z Content type: article Language: en Sources: [The latest research from Google]() Topics: [Machine Learning & Artificial Intelligence](), [Algorithms, Complexity](), [Deep learning](), [distributed-training](), [data](), [Google](), [High-Performance Computing](), [Programming]() Tags: [algorithms](), [algorithms-theory](), [data](), [deep-learning](), [distributed-training](), [google](), [high-performance-computing](), [libraries](), [machine-learning](), [release](), [research](), [responsible-ai](), [scalability](), [security-privacy-and-abuse-prevention](), [training]() ### AI overview Google announces JAX-Privacy 1.0, a library for differentially private machine learning built on JAX. The release is intended to help researchers and developers implement, audit, and scale private training workflows for deep learning models using large datasets and distributed training. ### Source excerpt Algorithms & Theory ## Announcing GPU Droplets accelerated by NVIDIA HGX H100 in the EU DevFeed: [Announcing GPU Droplets accelerated by NVIDIA HGX H100 in the EU]() Original publisher: [Read original article]() Author: Waverly Swinton Published: 2025-10-07T20:57:57Z Content type: release Language: en Sources: [DigitalOcean]() Topics: [GPU](), [Digital Ocean](), [Nvidia](), [datacenter](), [Cloud](), [Inference](), [Training AI Models](), [High-Performance Computing](), [Large Language Model]() Tags: [cloud](), [cost](), [data-center](), [digitalocean](), [gpu](), [high-performance-computing](), [inference](), [llms](), [nvidia](), [product-updates](), [train]() ### AI overview DigitalOcean announces GPU Droplets powered by NVIDIA HGX H100 GPUs in its Amsterdam data center, giving European developers access to GPU-accelerated virtual machines for training, inference, and high-performance computing. ### Source excerpt Demand for high-performance computing resources, particularly for cutting-edge training and inference workloads, continues to grow exponentially. We know that developers and businesses across Europe need simple, localized access to powerful GPUs to keep their innovation pipelines moving fast. We're excited to announce that NVIDIA HGX H100s as GPU Droplets--DigitalOcean's on-demand, GPU-powered, virtual machines--are now available in our Amsterdam data center. By integrating NVIDIA HGX H100 GPUs into an EU-based data center, we're making it simpler for developers and digital native enterprises to access cutting-edge performance with the simplicity and affordability you've come to expect from DigitalOcean. Accelerating training workloads with NVIDIA HGX H100s and DigitalOcean NVIDIA HGX H100 is a powerful GPU that is known for its training speed. It covers a range of use cases, including training LLMs, inference, and high-performance computing. Learn more about its full range of capabilities> DigitalOcean customers are putting NVIDIA HGX H100s to the test with complex use cases. WindBorne Systems, which operates the largest balloon constellation in the world while also developing state-of-the-art deep learning models for real-time forecasting, relies on NVIDIA HGX H100s on DigitalOcean for training. Combining NVIDIA's state-of-technology with DigitalOcean's resources, they found that DigitalOcean's infrastructure allowed them to train models on H100s faster and more cost-effectively than other cloud options. "We have two terabytes of RAM on each of the nodes, which we leverage to train larger models. DigitalOcean's ability to provide us with all of the resources that we need, which we often can't find in other places, has been really helpful." Anuj Shetty, Machine Learning Engineer at WindBorne Systems Read the full story > Simple, powerful GPU Droplets Like all DigitalOcean products, GPU Droplets are designed for simplicity. You can launch them in just a few clicks, fr ## TPU vs. GPU: Differences in Performance, Applications, Cost, and Ecosystem DevFeed: [TPU vs. GPU: Differences in Performance, Applications, Cost, and Ecosystem]() Original publisher: [Read original article]() Published: 2025-03-11T23:10:26Z Content type: comparison Language: en Sources: [Learn on Tailscale]() Topics: [GPU](), [Artificial Intelligence](), [AI Infrastructure](), [Deep learning](), [Google](), [Google Cloud Platform (GCP)](), [High-Performance Computing](), [Neural Network]() Tags: [ai](), [applications](), [deep-learning](), [efficiency](), [google](), [google-cloud-platform](), [gpu](), [high-performance-computing](), [speed](), [tpu](), [vs]() ### AI overview This comparison explains how Google TPUs and GPUs differ in AI processing. TPUs are designed for high-speed, low-precision deep-learning computation on Google Cloud, while GPUs provide more flexible parallel processing and broad framework compatibility. ### Source excerpt Two key players dominate the efficiency and speed of AI applications: the Graphics Processing Unit (GPU) and the Tensor Processing Unit (TPU). Both have their strengths and weaknesses. ## Transforming GPU resource management with Temporal DevFeed: [Transforming GPU resource management with Temporal]() Original publisher: [Read original article]() Author: Tim Imkin Published: 2024-12-19T08:00:00Z Content type: article Language: en Sources: [Temporal Blog]() Topics: [GPU](), [Cloud](), [High-Performance Computing](), [Scalability](), [reliability]() Tags: [cloud](), [community](), [gpu](), [high-performance-computing](), [reliability](), [scalability](), [workflows]() ### AI overview A case study describes how a technology company used Temporal to build a resource management platform for long-running GPU workflows. The platform automated health checks, updates, repairs, retries, and state management, and was launched in three months. ### Source excerpt See how a leading tech company used Temporal to automate GPU workflows, boost scalability, and streamline resource management. ## What Data Science Is and How It Supports Personalized Experiences DevFeed: [What Data Science Is and How It Supports Personalized Experiences]() Original publisher: [Read original article]() Author: Target Brands, Inc Published: 2016-03-01T06:00:00Z Content type: opinion Language: en Sources: [Target]() Topics: [Data Science](), [Machine learning](), [Algorithms, Complexity](), [High-Performance Computing]() Tags: [algorithms](), [data](), [data-science](), [data-science-and-engineering](), [engineering](), [high-performance-computing](), [machine-learning](), [science]() ### AI overview The article explains data science through everyday decision-making, describing how experience and instruction help people build models that guide judgment and anticipate outcomes. It connects these ideas to statistical models, machine learning algorithms, and Target's goal of creating personalized guest experiences. ### Source excerpt On my first encounter with it, around early 2010's, I was mystified. It sounded like witchcraft and I imagined the practitioners to be a coven of witches and wizards, all holding Ph.D.s in the dark art of "Data Science" and being respectfully addressed as "Data Scientists". It was believed they would magically transform haystacks into gold and then ask for your first-born in return as a reward for their service (a la Rumpelstiltskin) There is no denying the fact that the title "Data Scientist" is the most coveted one these days and has a nice ring to it. It's also true that data science has traditionally been a monopoly of mathematicians and statisticians. Obviously, developing statistical models and machine learning algorithms requires years of training and practice to specialize. In my opinion it is more of an art form driven by science and can easily be mistaken for magic. It's common knowledge that the more experienced in life we get, the easier it is for us to make up our mind. For instance, "What diner to pick for a boy's-night-out?", "When to stay off highways to avoid being stuck in a traffic-jam?", "When to buy a house? When NOT to buy?", are all such decions we make everyday. This ability comes as result of years of learning from implicit experience (a.k.a unsupervised learning) and explicit instructions from parents, teachers, friends, family and media (a.k.a supervised learning.) Our brain builds models of the world, of the situations we have been in, of banal and extraordinary, of nice and not-so-nice, of appropriate and inappropriate etc. These models facilitate judgment, govern behavior and enable anticipation of likely outcomes. That's basically data science. The recent progress in large scale and high performance computing has opened doors for such complex calculations to be performed on-demand and much more efficiently than was possible before. Hence, the buzz! At Target we operate in a guest-centric universe. We don't treat our guests as a statist