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.

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

Liquid cooling is rapidly gaining traction as the preferred thermal management solution for high-performance computing systems. Unlike traditional air cooling, which relies on heatsinks and fans to dissipate heat, liquid cooling uses thermally conductive fluids to transfer heat away from components like CPUs and GPUs. This method offers superior thermal efficiency and significantly quieter operation, making it ideal for overclocked processors and high-power graphics cards. Custom liquid cooling setups also bring a visually striking aesthetic, with RGB lighting and transparent tubing enhancing their appeal. However, adopting liquid cooling comes with certain complexities. It is generally more expensive due to the cost of pumps, radiators, and specialized coolant. Installation requires precision, as improper assembly can lead to leaks that might damage hardware. Maintenance, including fluid replacement and cleaning, also demands more effort compared to air-cooled systems. Interestingly, liquid cooling is transcending personal computers and entering the realm of cloud computing and large-scale data centers. Industry giants such as Microsoft, Google, and OVHcloud have implemented liquid cooling technologies to address the intense heat generated by workloads like artificial intelligence (AI), machine learning, and other high-performance applications. These systems are not only improving thermal efficiency but also reducing energy consumption, aligning with global sustainability goals. Immersion cooling, a subset of liquid cooling where components are submerged in dielectric fluids, is further pushing the boundaries of energy-efficient computing. The future of liquid cooling looks promising. As technological advancements make these systems more affordable, user-friendly, and reliable, we could see them becoming standard in gaming PCs, enterprise servers, and hyperscale data centers. By reducing dependency on traditional cooling methods, liquid cooling has the potential to redefine the thermal management landscape across industries while contributing to a more sustainable technological future. Video credit and rights are reserved for the respective owner(s). (DM for credit or removal) #technology

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.

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

I'm old enough to remember when even the suggestion that demanding HPC workloads could run in the cloud was met with incredulity. Fortunately, that time is long past and the #AWS cloud now provides Amazon EC2 instances with lots of memory, memory bandwidth, compute power, and network bandwidth, along with file systems and other storage that are a great fit for those HPC workloads. For example, the new Hpc8a instances, powered by 5th generation AMD EPYC processors, push the frontier even farther forward, with substantial improvements in all of the important dimensions (up to +40% performance, +42% memory bandwidth, and up to +25% better price performance). With cluster-scale networking (300 Gbps Elastic Fabric Adapter) , the instances are a great fit for tightly coupled CFD, engineering simulation, and climate modeling workloads, and will empower researchers and engineers to create the next wave of large-scale simulations and scientific discoveries. To learn more read Channy Yun (윤석찬) 's new blog post at https://lnkd.in/gXgv2Pua .