Burak Yenier’s Post

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.

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

As the demand for High-Performance Computing (HPC) and engineering simulations grows, the skills gap in this field becomes increasingly evident. SimOps offers a game-changing solution by simplifying complex processes, automating routine tasks, and making advanced simulation tools accessible to professionals at all levels. From aerospace to automotive to pharmaceuticals, industries are already benefiting from faster development cycles and streamlined operations thanks to SimOps. 📖 Learn more about how SimOps is shaping the future of HPC and engineering simulations. #SimOps #HPC #EngineeringSimulations #Automation #Innovation

Poll results are in! We solicited opinions regarding the reasons Verification takes so long. Results are in and the major takeaways are: 🗣 Design complexity is NOT slowing down DV 🗣 85 % of respondents identified verification techniques, methodologies and debug as the main problem 🗣 Simulation speeds are a major concern. 35 percent of respondents identified this as the primary issue 💡 How does VeriFire address these pain points ? Speed ⚡ With VeriFire, simulations run natively on AWS F1 FPGAs at speeds of up to 125Mhz. Communication with the host machine only occurs at the start and end of a test. All monitors and scoreboards run natively on the FPGA. Faster Debug 🐞 Our paradigm-shifting debug tools increase FPGA visibility and provide real time logs of a simulation similar to what engineers are used to in traditional simulation based verification environments Cloud based 🌩 No need to maintain your own compute farm. All you need is an AWS account and VeriFire and you are all set. Global teams are common in the semiconductor industry and the AWS cloud makes global co-operation seamless. Cost-efficient 💰 Verification and EDA tools burn 50 % of your R&D budget. VeriFire is a full-stack solution which includes compute, EDA tools and dedicated support. Our consolidated solution reduces cost by bundling all the key pieces of the Verification Puzzle. VeriFire is available now exclusively through the AWS partner network. #aws #verification #riscv #emulation #eda #semiconductor #vaas https://lnkd.in/dDEvGeAu

From aviation to automotive to architecture, computer simulation enables engineers across diverse sectors to predict the real-world behaviour of their virtual designs, circumventing the need for costly, time-consuming, physical trials. A well-supported high performance computing (HPC) system is a crucial enabler of current and future engineering simulations. Backed by over 20 years of experience, we are at the forefront of HPC, providing innovative processing and storage solutions to leverage big data for engineering workflows. Click the link below to learn how DUG HPC Cloud is delivering the power and support infrastructure needed to accelerate and scale engineering workflows. https://lnkd.in/daYbtDEs

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.

Chiplets Make Progress Using Interconnects As Glue.. Check out the article below. #chiplets #interconnects

Chiplets are part of the next big supercycle in silicon development, unleashing new innovation and solving existing challenges in the industry. Compared to traditional silicon designs, chiplets can: ➕ Scale performance and power-efficiency ➕ Lower costs and speed-time-to-market ➕ Integrate a variety of compute components for different AI workloads With 50+ partners, we're paving the way for the future of chiplets by creating the Chiplet System Architecture, providing greater differentiation and enabling effective and seamless software. For The Wall Street Journal, Kevork Kechichian discusses Arm's pivotal role in enabling ecosystem collaboration and the deployment of silicon solutions to deliver the computing power needed in the age of AI: https://lnkd.in/eb_UDSTA

The price discrepancy between input and output tokens in LLM models is intriguing. 🤔 Here's why this difference exists and matters: 💡 1. Computational Differences: - Input tokens are processed in a single, efficient forward pass through the model. - Output tokens require multiple forward passes, one for each generated token, increasing computational demands. 2. Memory Usage: - Generating output tokens is more memory-intensive, as the model's weights must be loaded for each forward pass. - Input tokens can be processed in batch mode, allowing for more efficient memory use. 3. GPU utilization: - Output token generation has lower GPU use due to minimal compute per forward pass compared to loading model weights. - Input token processing batches better, improving GPU use. 4. Pricing Strategy: - The higher price for output tokens reflects the additional resources required for generation. - It also incentivizes users to optimize their prompts and reduce unnecessary output. In conclusion, by understanding these pricing dynamics, organizations can make more informed decisions when integrating LLMs into their products or operations, effectively balancing cost considerations with performance requirements. 💡📊 Learned something? Follow me for more insights. #AIOps #CostManagement #LLM