Simr (formerly UberCloud)’s Post

We’re excited to share that our article on SimOps has been published on HPCwire! In this article, we explore how SimOps is addressing the growing skills gap in HPC and engineering simulations by simplifying complex processes and automating routine tasks. This allows professionals at all levels to manage simulations more effectively and contribute to innovation across industries. A big thank you to HPCwire for providing a platform to highlight the impact of SimOps on the future of engineering simulations. Check out the full article on HPCwire. #SimOps #HPC #EngineeringSimulations #SkillsGap #Automation

🚀 The Power of Multi-Core Processing in Embedded Systems In today's embedded systems, multi-core processing is revolutionizing the way we handle complex tasks. By utilizing multiple processor cores, we can execute tasks concurrently, leading to better performance and efficiency. 🌟 Benefits of Multi-Core Processing: Parallelism (⚙️): Multiple cores work simultaneously on different tasks, boosting system performance, particularly in real-time and computationally intense applications. Task Distribution (📊): The system's software distributes tasks dynamically or statically across cores, optimizing resource usage and balancing the load. Communication & Synchronization (🔗): Cores communicate and synchronize using shared memory or message passing to ensure smooth coordination and data sharing. Power Efficiency (🔋): By distributing workloads across cores, multi-core systems often consume less power. Selectively powering down cores or adjusting frequencies helps achieve even greater power efficiency. Scalability (📈): Need more performance? Multi-core systems scale seamlessly by adding additional cores to meet varying application demands. 🔑 Why it Matters: Multi-core architectures are the backbone of high-performance embedded systems, from real-time control to advanced computing applications. Though challenging, they offer tremendous potential for innovation. #EmbeddedSystems #MultiCoreProcessing #RealTimeSystems #ParallelComputing #SoftwareDevelopment #TechInnovation

The CMSIS Solution is made even more powerful when Layers are integrated. Learn how using a layer can help adapt your application amongst different targets. #embeded #microcontroller #ISSDK NXP Semiconductors

I've had the privilege of meeting some of the smartest people managing engineering computing environments. Their expertise is undeniable, but their job titles? All over the place. HPC Manager/Director, CAE Specialist, Simulation IT Engineer, Engineering Cloud Manager! And the most interesting part? I hardly ever meet anyone who planned for this career. They started as researchers, engineers, or developers—just trying to run simulations for product development. Then they hit roadblocks. Compute bottlenecks. Software frustrations. Access issues. Instead of accepting the pain, they solved it. In the process, they fell in love with this world and made it their career. SimOps is for them. It’s a way to take what they’ve learned and build structured career paths for the next generation. To set best practices. To create learning opportunities. To ensure that the people coming next don’t have to stumble into this field by accident—they can choose it. read more: https://www.simops.com/ #Engineering #HPC #Simulation #CloudComputing #CAE #SimOps #CareerGrowth

Learn how software layers simplify #embedded software development for #Cortex #microcontroller. 👉 Join "The CMSIS Solution" Webinar on 3. Dec: https://lnkd.in/gysJER96 Software Layers collect source files and software components along with configuration. This new feature of the #CMSIS-Toolbox gives your projects a better structure and simplifies: ✅ Development flows with evaluation boards and production hardware. ✅ Evaluation of middleware and hardware modules across different microcontroller boards. ✅ Code reuse across projects, i.e. board support for test-case deployment. ✅ Test-driven software development on simulation model and hardware. During this webinar Matthias Hertel explains the overall concept and usage. Hans Schneebauer demos layer features of the new #VSCode CMSIS Solution Extension. It is the 5. session of a webinar series. Watch previous session where NXP Semiconductors, Embedded Wizard, and Embedded Systems Academy explained how #CMSIS simplifies development. Coming up are sessions with Memfault and AI/ML development where we again use layers to connect hardware sensors. #AI #ML #CI #DevOps

Thanks, Burak, for uncovering this HPC Operations skills gap. There is barely a specific education/training opportunity for the area between engineering simulations and the operation of the underlying HPC infrastructure. And yes, #SimOps aims at filling this gap.

Don’t fall for those claims about DeepSeek creating a model for just $5.6M. That number only covers the final training run. It's like saying you built a house for the cost of putting on the roof. Let me break down what's actually involved: First, the research costs. Before you can even think about training your final model, you need: - Architectural research and development - Ablation experiments on different approaches - Testing various algorithms and data strategies - Building and testing your infrastructure Then there's the infrastructure itself. DeepSeek's setup requires: - A cluster of 2,048 H800 GPUs - Additional GPUs for model inference and serving - Specialized load balancing systems - Custom memory compression implementations And we haven't even touched the human side: - Engineering talent for GPU optimization - Specialists in custom programming - Infrastructure engineers This is why the "$5.6M vs billions" narrative is so misleading. The real cost of AI development isn't just about the final training run - it's about the entire ecosystem needed to get there.

The Scalability in EMT Simulation - 2 Scalability in EMT (Electromagnetic Transient) simulation refers to a simulation tool's ability to efficiently handle increasing system complexity and size—whether that's larger power systems, more detailed models—without sacrificing performance or accuracy. Here are several critical aspects of scalability in EMT simulations: 1. Computational Scalability - Parallel Processing: Modern EMT simulations leverage parallel computing (multi-core processors, GPUs) to handle large-scale models. This reduces computation time by distributing the workload efficiently. - Algorithm Efficiency: The solvers and algorithms must scale with system size. Some solvers struggle as systems grow, so choosing optimized numerical solvers is key to maintaining performance. 2. Data Scalability - Memory Management: As systems expand, so does the memory needed to store data like waveforms and parameters. Scalable simulations use efficient memory management to avoid overloading resources. - I/O Performance: Large-scale EMT simulations involve heavy data input/output operations. Efficient file handling ensures smoother performance, especially for high-frequency outputs. 3. Model Scalability - System Size: A scalable simulation platform can manage wide-area EMT simulation without significant performance hits. - Model Detail: EMT tools need to handle complex, detailed models—like intricate protection systems or advanced converter models—while maintaining computational efficiency. 4.Time Step Scalability -Time Step Scalability: Running the model at a larger time step while maintaining acceptable accuracy can improve scalability. This approach reduces computation by minimizing the number of steps required while ensuring the results remain reliable. 5. Communication Scalability - Interfacing with Other Systems: EMT simulations often need to interface with other EMT model. A scalable solution must handle more cross-domain communication seamlessly and efficiently. As power grids grow more complex with renewable integration, the scalability of EMT simulation tools becomes even more critical. At Quark Power Inc., we’re committed to pushing the limits of scalability with our Quark Power Transient Simulator (QPTS), providing faster, more detailed simulations for large-scale power systems. What challenges are you seeing with scalability in your EMT simulations? #EMT #Scalability #PowerSystems #Simulation #RenewableEnergy

For businesses and tech enthusiasts, the concept of pipelining isn't just a technical marvel—it’s a reminder of how efficiency and precision drive success. In computer architecture, efficiency isn't just about faster hardware—it's about smarter processes. That's where pipelining comes in, transforming instruction execution into an assembly-line operation, boosting CPU throughput without increasing its clock speed. Imagine baking a multi-layered cake: while one layer bakes, you start preparing the next. Similarly, pipelining overlaps stages like fetching, decoding, and executing instructions. The result? Improved performance, shorter wait times, and a streamlined process. But like any innovation, pipelining isn’t without its challenges: 🛑 Resource Conflicts – Multiple instructions vying for the same hardware. 🔗 Data Hazards – Dependencies that create bottlenecks. 🔀 Control Hazards – Branch predictions that disrupt flow. Thankfully, solutions like stalling, forwarding, and innovative architectures like superscalar and VLIW are helping tackle these issues head-on. Whether in CPUs or day-to-day operations, overlapping tasks (smartly) is the key to scaling impact. What’s your take on optimizing processes for better performance? Let’s discuss in the comments! 👇 #Miva #ComputerArchitecture #SoftwareEngineering Image by AI: DALL·E 3

Firmly entrenched in a technological revolution, quantum computing stands at the forefront of this transformative era. For those immersed in digitized product management, understanding how this cutting-edge technology influences data security and management is essential for staying ahead in today’s dynamic landscape. #QuantumComputing #technology #digitizedproductmanagement https://lnkd.in/gE2ecknc