Tomer Eitan’s Post

Speed Up HPC 🚀 & Cut Costs 💰 with Amazon S3 Mountpoint. The challenge of deploying high performance computing (HPC) infrastructure can be daunting.💪 Setting up complex networks and storage is time consuming and expensive! 💸 Amazon Web Services offers a clever new solution - S3 mountpoint. This allows you to directly access S3 buckets as a file system. 💡 No need to copy data to a dedicated file system! Just point your applications to S3 and go. 🏃♂️ Save up to 20% in costs by avoiding additional storage layers. 💵 Compatible with popular HPC tools like BeeGFS & Lustre for easy setup! ⚙️ Want to boost your HPC performance? 🤔 See how to get started with S3. Let's connect 👥 if you want to chat more about increasing productivity and reducing infrastructure costs for your HPC workloads! https://lnkd.in/eANaFmZD #aws #amazon #bigdata #dataanalytics #hpc #highperformancecomputing #amazons3 #mountpointforamazons3

Scaling Research Workloads. 🧪🔬 Managing HPC infrastructure for research can be complex. Choosing the right orchestration tools is key to maximizing productivity. 🤓 AWS outlines top considerations when evaluating options: ➕ Flexibility & scalability. ➕ Integration with storage. ➕ Workload support. ➕ Cost optimization.    For example, AWS ParallelCluster supports things like: ✅ Batch processing. ✅ Machine learning. ✅ Containers. ✅ Auto-scaling. See the article below👇for more details on matching tools to your computational research needs - AWS, third party or OSS. Let's chat if you want to architect the optimal solution for the hard problems you're solving! https://lnkd.in/e2CddGw4 #aws #amazon #bigdata #dataanalytics #research #hpc #highperformancecomputing 

If you are in the field of Molecular dynamics, you must read this post from Nathaniel Ng, JEN-CHANG “James” CHEN, and Shun Utsui called "Best practices for running molecular dynamics simulations on Amazon Web Services (AWS) Graviton3E" https://lnkd.in/eW67MaQm #moleculardynamics #HPC #AWSGraviton #Graviton3E

Are you running High-Performance Computing (HPC) workloads in the cloud and struggling with storage bottlenecks? Amazon FSx for Lustre is a fully managed, high-performance file system built for demanding workloads like scientific modeling, simulation, and data analytics. It delivers consistent throughput, low latency, and parallel access to large datasets, allowing you to scale your HPC workloads seamlessly in the cloud. What Is the Lustre File System? With a name coined from the combination of “Linux” and “cluster,” Lustre is a file system that is parallel and distributed. It’s most commonly used for cluster computing on a very large scale. How Amazon FSx for Lustre Works An Amazon FSx for Lustre file system has two main parts: a central file server and storage disks where data is stored. When clients want data, they talk to the file server. The server can speed things up by keeping commonly used data in a fast, in-memory cache. This means if the data is already in the cache or on a fast SSD, the server doesn't have to fetch it from the slower disk. This makes accessing data faster and increases the amount of data that can be transferred at once. Think of Amazon FSx for Lustre like a super-fast library system for your data. Instead of one librarian searching through bookshelves, many "librarians" (storage disks) hold your information. A central "head librarian" (file server) helps you find things quickly. It keeps often-used books (data) close by in a special, super-fast memory. So, when you need something, the head librarian checks there first. If it's there, you get it instantly, like finding a popular book right on the counter! This means less waiting (lower latency) and faster access (higher throughput) to your data. It's like having a whole team dedicated to getting you what you need, right away! Here are some real-world scenarios: 1. HPC Powerhouse 2. Big Data Analytics Turbocharger 3. Media & Entertainment Mastermind 4. AI & Machine Learning Accelerator #aws #awscommunity #awsdataengineer #awscommunitybuilders #awsusergroup Intuitive.Cloud Bhavesh Shah Bhuvaneswari Subramani Jared Cooper Mir Navazish Ali Albert Zhao Jason Dunn Shafraz Rahim 🌤 Farrah Campbell Ridhima Kapoor AWS User Group Hyderabad Faizal Khan

🚀 Boost Your HPC Workloads with #Amazon FSx for Lustre! 🚀 🔍 Are you looking for a high-performance, scalable file system to supercharge your compute-intensive workloads? Look no further! Amazon FSx for Lustre is here to optimize your HPC, machine learning, and big data applications. 💡 Why FSx for Lustre? ⚡ Speed: Delivering sub-millisecond latencies and hundreds of GB/s of throughput! 🧩 Scalability: Seamlessly scale storage and compute resources to meet your demands. 🔒 Security: Robust security features with data encryption at rest and in transit. 🛠️ Integration: Easily integrate with Amazon S3 for a unified data lake. 🌟 Use Cases: 🧬 Genomics Research: Accelerate your genome sequencing and analysis. 🎥 Media Rendering: Handle large-scale media rendering and VFX workloads. 📊 Financial Modeling: Perform complex simulations and analytics efficiently. 🧠 Machine Learning: Speed up model training and data processing. 📈 Get Started Today: Step 1: Choose your desired file system configuration. Step 2: Connect your compute resources. Step 3: Start processing data at lightning speed! 🔗 Learn More About Amazon FSx for Lustre: https://lnkd.in/gSF9C6X8 Empower your workloads with the unparalleled performance of Amazon FSx for Lustre! 💪 #razorops #kubernetes #cicd #pipeline #AWS #CloudComputing #HPC #BigData #MachineLearning #TechInnovation

Shaping the Future of #Pharma Workloads! SecureKloud's Scalable HPC Solution redefines clinical operations on AWS with elastic computing, scalability, and cost savings. Explore the transformative journey in our latest case study. Read More: https://lnkd.in/gZDMirpE #HPC #PharmaInnovation #PharmaTech #SecureKloud #BioPharma #AWS

Thanks Carlo Puliafito for sharing this and leading on this work. Our Special Issue on Serverless Computing for the FGCS journal -- wonderful experience working on this SI with my colleagues -- including Hao Wu (Beijing Normal University, Zhuhai (China)-- School of AI) and Luiz Fernando Bittencourt (Universidade Estadual de Campinas (Brazil)) -- great working with two Cardiff University / Prifysgol Caerdydd international partners. Along with significant interest in machine learning/AI on serverless-edge computing, there was also interest in cybersecurity, scheduling of functions & IoT resource integration, energy efficiency and applications (digital twins and healthcare). A summary at: https://lnkd.in/eizDUwst List of accepted papers: [1] Load balancing for heterogeneous serverless edge computing: A performance-driven and empirical approach [2] Intent-driven orchestration of serverless applications in the computing continuum [3] QoS-aware offloading policies for serverless functions in the Cloud-to-Edge continuum [4] SD-SRF: An intelligent service deployment scheme for serverless-operated cloud-edge computing in 6G networks [5] Security computing resource allocation based on deep reinforcement learning in serverless multi-cloud edge computing [6] Deep reinforcement learning-based scheduling for optimizing system load and response time in edge and fog computing environments [7] Rescheduling serverless workloads across the cloud-to-edge continuum [8] Fluidity: Providing flexible deployment and adaptation policy experimentation for serverless and distributed applications spanning cloud–edge–mobile environments [9] QoS-aware edge AI placement and scheduling with multiple implementations in FaaS-based edge computing [10] StructMesh: A storage framework for serverless computing continuum [11] The globus compute dataset: An open function-as-a-service dataset from the edge to the cloud [12] Evaluating NiFi and MQTT based serverless data pipelines in fog computing environment [13] FaaS for IoT: Evolving serverless towards deviceless in I/O clouds [14] Expanding the cloud-to-edge continuum to the IoT in serverless federated learning [15] EneA-FL: Energy-aware orchestration for serverless federated learning [16] Exploiting microservices and serverless for Digital Twins in the cloud-to-edge continuum [17] Towards providing a priority-based vital sign offloading in healthcare with serverless computing and a fog-cloud architecture Michela Taufer

Announcing AWS Parallel Computing Service to run HPC workloads at virtually any scale With AWS Parallel Computing Service, run HPC workloads at virtually any scale effortlessly; leverage Slurm and managed clusters to accelerate simulations. Read mode on following blog post!

Announcing AWS Parallel Computing Service to run HPC workloads at virtually any scale With AWS Parallel Computing Service, run HPC workloads at virtually any scale effortlessly; leverage Slurm and managed clusters to accelerate simulations. Read mode on following blog post!