High-Performance Computing Solutions

Headline: China’s Oceanlite Supercomputer Marries AI and Quantum Science—37 Million Cores Simulate Molecular Quantum Chemistry Introduction: In a milestone achievement, Chinese researchers have fused artificial intelligence with traditional supercomputing to simulate complex quantum chemistry at molecular scale—without using a quantum computer. Using the Oceanlite supercomputer powered by 37 million processing cores, the Sunway team has achieved a feat previously deemed impossible on classical machines. Key Insights: 1. Bridging AI and Quantum Physics Quantum chemistry models the probabilistic behavior of particles like electrons within molecules, governed by the wavefunction (Ψ). Such simulations are normally restricted to small molecules due to the exponential complexity of quantum states. To overcome this barrier, the Sunway team used neural-network quantum states (NNQS), allowing machine learning to approximate molecular wavefunctions with quantum-level accuracy. 2. Record-Breaking Simulation Researchers modeled a molecular system containing 120 spin orbitals—the largest AI-driven quantum chemistry simulation ever conducted on a classical supercomputer. The NNQS trained to predict electron energy distributions and refined itself iteratively until it mirrored true molecular quantum behavior. This approach demonstrates that deep learning frameworks can replicate quantum effects at unprecedented scale. 3. Oceanlite’s Engineering Triumph The experiment ran on the Sunway SW26010-Pro CPU, each chip featuring 384 cores optimized for high-performance computing (HPC). Engineers built a hierarchical communication model where management cores coordinated millions of lightweight compute processing elements (CPEs). Achieved 92% strong scaling and 98% weak scaling efficiency, indicating near-perfect hardware-software synchronization—an exceptional accomplishment in exascale computing. 4. Strategic and Scientific Impact Marks a leap forward for China’s AI and quantum research sectors, blending HPC power with neural architectures. The achievement positions China at the frontier of simulating quantum systems without quantum hardware. Why It Matters: This breakthrough redefines the boundary between classical and quantum computing, offering a path to simulate and design complex molecules—essential for materials science, drug discovery, and clean energy research—using today’s infrastructure. It also signals China’s deepening command of exascale computing and its integration with AI, setting a new global benchmark in scientific computing innovation. I share daily insights with 28,000+ followers and 10,000+ professional contacts across defense, tech, and policy. If this topic resonates, I invite you to connect and continue the conversation. Keith King https://lnkd.in/gHPvUttw

Better CFD Performance with Heterogeneous CPU-GPU Load Balancing 🚀The Load balancing using both CPUs and GPUs has improved the performance of a turbulent flow simulation by up to 87% compared to GPU-only execution. This was achieved by strategically distributing computationally intensive turbulent inlet regions to CPUs while assigning the less demanding bulk regions to GPUs. 🔬 The inhomogeneous spatial domain decomposition was optimized using a cutting-edge genetic algorithm tailored for cost-aware optimization. This method ensures that each simulation part is processed on the most suitable hardware, maximizing efficiency. 💻 The simulation ran on a single accelerated CPU-GPU node of the HoreKa supercomputer, utilizing OpenLB's support for MPI, OpenMP, AVX-512 vectorization, and CUDA. With 355 million lattice cells, the system achieved an impressive throughput of ~19.25 billion cell updates per second for the NSE-only case. 🔗 Learn More: OpenLB.net 🔗 Read the Preprint: https://lnkd.in/dsYVdbbZ 💳 Credits: openlb Simulation Setup: Fedor Bukreev Heterogeneous Load Balancing & Visualization: Adrian Kummerländer #HPC #CFD #OpenLB #LoadBalancing #CPU #GPU #Supercomputing #PerformanceOptimization #LatticeBoltzmann #Simulation #TechEngineering #HoreKa #HighPerformanceComputing

🔴 Researchers from TSMC present the blueprint for next-generation silicon photonics at the #ECTC. The paper "Heterogeneous Integration of a Compact Universal Photonic Engine for Silicon Photonics Applications in HPC" proves that establishing a standardized heterogeneous integration platform will define the next decade of #SiliconPhotonics and #HighPerformanceComputing. One of the most prominent challenges for the widespread adoption of silicon photonics technology is the availability of an integration platform that can simultaneously meet a wide range of power, performance, and cost criteria. While there is a vast diversity of proposed solutions in the industry, none has been considered a common standard. This research addresses this critical bottleneck by proposing a unified architecture. 1️⃣ Overcoming Integration Bottlenecks: #IntegrationPlatform & #SiPh The highly fragmented landscape of current silicon photonics solutions prevents scalable and cost-effective manufacturing. The industry desperately requires a common solution that can be universally applied to different advanced computing applications. 2️⃣ A Universal Photonic Engine: #HeterogeneousIntegration & #Packaging To solve this, the paper details the development of a compact universal photonic engine. By utilizing advanced heterogeneous integration techniques, this engine effectively combines essential optical and electrical components into a single, highly optimized package. 3️⃣ Scaling High Performance Computing: #HPC & #DataCenters This unified platform provides the critical hardware foundation needed for high-performance computing applications. It establishes a robust and scalable pathway to support the massive bandwidth requirements of modern data centers without compromising on power efficiency. 💡 My Take: As high-performance computing pushes the boundaries of traditional copper interconnects, the transition to optical data transmission is mandatory. However, the lack of a standardized, cost-effective packaging platform has severely delayed mass market adoption. By developing a compact universal photonic engine through heterogeneous integration, the industry finally has a scalable blueprint. This is not just about making transceivers smaller it is about establishing a foundational architecture that can seamlessly co-package optics with advanced compute dies, paving the way for the terabit era of AI and HPC. 👇 Link in the comments #AdvancedPackaging #HeterogeneousIntegration #AIHardware #OpticalInterconnects #DataCenter #3DIC #Optoelectronics #CoPackagedOptics Intel NVIDIA AMD Broadcom Marvell Technology Cisco ASE Group Amkor Technology, Inc. Applied Materials ASML Lam Research Lumentum Coherent Corp.

"Cold Compute" Breakthroughs: Why AI Infrastructure Just Got 30% Faster and 40% Cheaper The biggest hidden cost in the AI race isn't the code - it's the power bill and the "Wait Time" (Latency). We just saw two massive leaps in physics and telecom this month that fundamentally change the ROI of AI deployment: 1. The End of the Heat Tax: "Optical Magnets" ⚡ Researchers at the University of Basel and ETH Zurich just published a breakthrough in Nature demonstrating the ability to flip magnetic polarity using only laser pulses—no heat required. The Impact: This paves the way for "All-Optical" memory (MRAM). By replacing electricity with light at the chip level, we can eliminate the massive thermal cooling budgets that currently consume up to 40% of data center power. Big move. Source: https://lnkd.in/gbswjc9z 2. The "Speed of Light" Upgrade: Hollow-Core Fiber 🚀 At MWC Barcelona 2026, industry leaders (YOFC, Hengtong, FiberHome) unveiled the first commercial-scale Hollow-Core Fiber (HCF) solutions. Unlike traditional glass fiber that slows light down, HCF allows data to travel through an air-core at nearly the speed of light in a vacuum. The Impact: A 31% reduction in latency. In the world of high-performance computing, that is the difference between a "laggy" model and real-time intelligence. Second big move. Source: https://lnkd.in/gcXEdC4i Collaborative AI: 🤖🤝🤖 The future isn't one giant, isolated AI; it’s millions of specialized AI "agents" working together. Currently, these agents are too slow to "talk" to each other in real-time due to network friction. Required to swarm in the physical world. By combining Optical Switching (faster processing) with Hollow-Core Fiber (faster transmission), we are closer to a "Nervous System." This unlocks real time swarms and general agent to agent collaboration at scale. It’s not all GPU improvements… we’re moving to the fabric too… #AI #Infrastructure #Photonics #MWC2026 #ETHZurich #Innovation #DeepTech

Your HPC simulations probably run at <3% of peak performance. Here’s why, and what SC25 revealed 👇 1/ FLOPs don’t predict scientific performance The Top500 uses Linpack, a benchmark for dense linear algebra. But most scientific codes (MD, DFT, MLIPs, climate) are: sparse, communication-heavy, memory-bound, irregular. That’s why even exascale machines deliver 0.6%–3% of peak on real workloads. 2/ HPCG (high-performance conjugate gradient) is a more honest test for real simulation work. HPCG measures the building blocks of scientific computing: sparse matrix–vector multiply, multigrid V-cycles, communication collectives, irregular memory access. It reveals how well a machine handles real simulation patterns, not theoretical FLOPs. That’s why the HPCG Top10 looks nothing like the Top500. 3/ The actual bottleneck = data movement Jack Dongarra said it best: “Arithmetic is inexpensive and oversubscribed.” What slows your job down is: memory bandwidth, interconnect latency, node-to-node communication, data locality. Your simulation is movement-limited rather than compute-limited. 4/ HPC systems are now fully heterogeneous 2025 systems include: AMD MI300A NVIDIA Grace + GH200 Intel Max GPUs ARM A64FX cloud-native HPC nodes No two machines are built the same anymore. Your software and workflows must be ready to adapt. 5/ Precision is shifting 64-bit used to dominate simulation. But mixed-precision and adaptive-precision methods are becoming practical (thanks to AI + hardware changes). The future is right-precision computing instead of “max precision by default.” If you run scientific simulations, the key question isn’t FLOPs, but rather: “How fast can I move data, and how well does my algorithm tolerate irregularity?” This will shape the next decade of scientific computing. Have you ever profiled your simulation to understand where it’s actually limited (bandwidth? latency? compute?) What did you find? #HPC #Supercomputing #ScientificComputing #Top500 #SC25 #ComputationalScience #AIInfrastructure #MaterialsScience #Exascale

The Benchmark That Shocked HPC: How Standard Linux Just Outran Proprietary File Systems The IO500 benchmark has always revealed a hard truth about HPC performance: only proprietary, highly specialized file systems could reach the top. That belief has shaped infrastructure decisions across AI, research, cloud, and enterprise datacenters for years. Until now. In the newest episode of Data Unchained, I sat down with Jon Flynn to break down a result that is forcing the industry to rethink everything it thought it knew. Hammerspace and Samsung Electronics delivered a top tier 10 Node Production IO500 score using standard Linux, the upstream NFSv4.2 client, and enterprise NVMe SSDs. This is one of the clearest signs yet that HPC class performance no longer requires proprietary stacks or custom engineered clients. Jonathan walks through how upstream Linux kernel contributions, pNFS layout intelligence, metadata resilience, direct IO pathways, multi instance file distribution, and ZFS enhancements combined to unlock massive performance improvements. The team achieved more than double the prior bandwidth numbers and delivered a remarkable leap in IO Hard Read performance that would have been unthinkable with standard NFS only a few years ago. We also explore how this changes the competitive landscape for HPC and AI infrastructure. When standard Linux can rival or exceed the speed of long standing parallel file systems, the entire ecosystem shifts toward openness. This expands who can build high performance environments, lowers operational barriers, increases portability, and accelerates innovation across training pipelines, scientific workloads, and large scale compute environments. If you work in HPC, AI, storage engineering, kernel development, or large scale data architecture, this episode offers a clear view into the emerging future of performance at scale. Be sure to check out this episode of Data Unchained and more on all your favorite podcast platforms! YouTube - https://lnkd.in/g9jniVsT Apple Podcasts - https://apple.co/3yTKqxe Spotify - https://spoti.fi/3s9IVHs Amazon Music - https://amzn.to/3VAyIkZ #DataUnchained #Hammerspace #SamsungMemory #IO500 #Supercomputing25 #SC25 #LinuxKernel #NFSv42 #pNFS #ParallelFileSystems #HPC #AIInfrastructure #StoragePerformance #NVMe #GlobalFileSystem #PerformanceEngineering #AITraining #OpenStandards #HighPerformanceComputing #KernelInnovation #MLPerf #DataOrchestration #DataInfrastructure #Top500

Scale-up with Ethernet for AI and high-performance computing involves leveraging its robust ecosystem and making targeted enhancements. Features like UEC (Ultra Ethernet Consortium) innovations—such as Link-Level Retry (LLR), Credit-Based Flow Control (CBFC), optimized headers, and low-latency Forward Error Correction (FEC)—paired with low-latency Ethernet switches, effectively address memory semantics challenges, enabling efficient communication. Ethernet's broad adoption and compatibility ecosystem provide a cost-effective and scalable foundation. xPU vendors can flourish by adapting existing Ethernet fabrics, incorporating these advanced features, and capitalizing on Ethernet's momentum as the de facto standard for AI networking, ensuring scalability, flexibility, and widespread integration.

HYBRID MEMORY CUBE Hybrid Memory Cube (HMC) is a high-performance computer random-access memory (RAM) interface for through-silicon via (TSV)-based stacked DRAM memory. HMC competes with the incompatible rival interface High Bandwidth Memory (HBM). Hybrid Memory Cube (HMC) is a high-performance, low-power memory technology designed to address the growing demands of data-intensive applications. It represents a significant departure from traditional DRAM architectures, offering a more efficient and scalable solution. Key Features of HMC: 1. 3D Stacking: HMC stacks multiple DRAM dies vertically on a silicon interposer, allowing for higher density and reduced footprint. 2. Hybrid Architecture: It combines DRAM with a high-speed, low-latency interface, providing a balance between capacity and performance. 3. On-Die Logic: HMC incorporates logic circuitry on the interposer, enabling more complex memory operations and reducing the need for external controllers. 4. High Bandwidth: HMC offers significantly higher bandwidth compared to traditional DRAM, making it ideal for demanding workloads. Low Power Consumption: Its efficient architecture and power management features contribute to lower power consumption. Benefits of HMC: 1. Improved Performance: HMC delivers faster data transfer rates and lower latency, enhancing the overall performance of computing systems. 2. Increased Density: The 3D stacking architecture allows for higher memory capacities in a smaller physical space. 3. Reduced Power Consumption: HMC's energy-efficient design helps to lower operating costs and improve system reliability. 4. Scalability: HMC can be scaled to meet the growing demands of data-intensive applications. Applications of HMC: 1. High-Performance Computing (HPC): HMC is well-suited for scientific simulations, machine learning, and other HPC workloads that require massive amounts of data processing. 2. Data Centers: It can be used in data centers to improve the performance and energy efficiency of servers and storage systems. 3. Artificial Intelligence (AI): HMC's high bandwidth and low latency make it suitable for training and inference in AI applications. 4. 5G Networks: It can support the demanding requirements of 5G networks, including high data rates and low latency. Hybrid Memory Cube represents a promising technology that has the potential to revolutionize the memory landscape. Its unique architecture, high performance, and low power consumption make it an attractive option for a wide range of applications. As HMC continues to evolve, we can expect to see even greater benefits in terms of computing power and efficiency. Image Source - Cadence Blogs

I’ve listed below what i think are 5 of the best reasons why you should consider Modular or Prefab’s when deciding how to deploy either your Ai or HPC workloads. 1. Rapid Deployment and Scalability AI and HPC workloads often require rapid scaling as computational demands increase. Modular DC's can be deployed much quicker than traditional facilities, allowing businesses to quickly scale their compute power. These modular and pre-fabricated modules can be deployed in phases so you pay as you grow. 2. Energy Efficiency AI and HPC workloads are resource intensive, consuming large amounts of power. Modular DC's are designed with energy efficiency in mind, using the most modern technology available. This focus on efficiency not only reduces operating costs but also aligns with sustainability goals, as many organisations aim to reduce their carbon footprint while meeting demanding AI compute needs. Being agnostic in our approach with technology vendors helps us design a Pod to fit exactly with what the customer wants. 3. Cost Effective Deployment Traditional data centers require significant upfront capital investment and long construction periods. Modular data centers, by contrast, are more cost effective due to their pre engineered design, shorter construction times, and lower overhead costs. For AI and HPC deployments, this means faster ROI, as organisations can quickly get their compute infrastructure up and running without incurring the financial burdens of traditional builds. Typically we can build and deploy a Megawatt in around 20 weeks. 4. Optimised for High Density Computing AI and HPC applications require high density compute environments, and modular DC's are designed exactly for this. With fully customisable configurations, they can support the high power and cooling demands of GPU-heavy and CPU-dense setups typically required for AI model training / inference and HPC workloads. Modular designs also allow for targeted cooling such as in-row or rear door ensuring optimal performance for intensive compute tasks. 5. Flexibility Modular data centers offer a level of flexibility that traditional data centers simply can’t match. Whether your AI/HPC operations need to move closer to edge locations for reduced latency or expand across different geographies, these portable module Pods can be deployed almost anywhere. This flexibility allows businesses to quickly adapt to changing requirements and environments making them ideal for AI/HPC tasks where location and latency can be critical. As AI and HPC workloads continue to push the boundaries of traditional datacenter design , modular DC's offer a flexible, scalable, and cost effective solution. Their ability to quickly adapt to the high density and resource intensive needs of AI and HPC computing make them an intelligent choice for organisations looking to get ahead in the rapidly evolving HPC hosting and On Prem market. #Ai #HPC #modularconstruction #digitalinfrastructure #cloudservices

When distributed training jobs stall, most teams blame the model. In reality, the bottleneck is often the fabric. After working with large-scale GPU clusters, one lesson stands out: performance isn’t just about FLOPs - it’s about how data moves. This “ABC of GPU Fabrics” breaks down the core concepts that determine whether your cluster scales cleanly… or collapses under contention. From Accelerated Networking and InfiniBand to RDMA and NVLink, the fundamentals define how GPUs exchange gradients, synchronize kernels, and maintain throughput under load. But raw bandwidth isn’t enough. You have to understand: • Jitter and Latency Spikes - because tail latency stalls entire training steps. • Oversubscription and Queue Depth - where congestion quietly eats performance. • Packet Loss and Bandwidth Saturation - forcing retransmissions and slowing collectives. • Traffic Engineering and Virtual Lanes - preventing head-of-line blocking. • Cross-Rack Traffic and Workload Locality - minimizing expensive inter-rack communication. Fabric design choices like Clos topologies, Dragonfly networks, and Spine/Edge switching directly influence hop counts, predictability, and failure domains. Operational controls - Fabric Managers, Hotspot Detection, Utilization tracking, Yield Metrics - determine whether you’re running efficiently or simply burning GPU cycles. High-performance AI infrastructure isn’t accidental. It’s engineered - from topology to congestion control to placement strategy. If you’re building or scaling GPU clusters: Are you optimizing models… or optimizing the fabric that feeds them?