HPC and AI at TACC: Advancements, Collaboration, and Future Plans

💡 Dan Stanzione provides an update on the leadership facility at the TACC.

🌐 The relationship between AI and HPC is discussed, emphasizing their importance.

⚙️ Updates on the TACC's systems and experiments, including node sharing and disaggregation.

The demand for high-performance computing (HPC) is increasing rapidly, especially for AI and real-time data processing.

There is still a need for big tightly coupled machines in HPC, even though smaller machines and cloud computing are viable options.

Most of the computing time is dedicated to parallel jobs with a significant number of cores.

⭐ Real HPC usage is not just about ensembles and throughput, there is a demand for running big jobs.

💻 When considering what to put in high-performance computing systems, GPUs have a huge advantage in terms of flops per watt compared to CPUs.

🔋 The power per socket in HPC systems has increased, but the cost per socket has also increased, resulting in more efficient use of nodes.

💿 The rapid decrease in the cost of NVMe drives may make traditional disk storage less viable in the future.

🔑 The increasing use of AI is impacting the field of high-performance computing (HPC).

🚀 Memory bandwidth and GPU usage are important factors in HPC performance.

💡 The market and vendors' influence on HPC hardware choices is growing.

💡 The video discusses the importance of investing in AI to maintain global competitiveness and economic benefits.

💻 AI and HPC (High Performance Computing) are interconnected, and incorporating AI into HPC can revolutionize job roles and increase productivity.

🚀 Chat GPT, an AI-based tool, can generate code and provide support for programming tasks, improving program productivity and supporting HPC users.

👉 There is a discussion about canceling jobs in slurm and the investment of staff time in handling tickets.

🤖 The speaker highlights the milestone in artificial intelligence by showcasing the ability of computers to lie and be wrong like humans.

🔬 The impact of large language models on HPC groups is considered, with a focus on productivity and budget implications.

🧠 The possibility of a U-turn in AI around explainable AI and higher precision is discussed.

💾 The understanding of memory bandwidth and its impact on applications in different fields is explored.

📈 Many codes are not close to the ideal memory bandwidth bound, but there is Headway in terms of vectorization.

🔐 The speaker appreciates the discussion on utilization and uptime and shares comparable experiences.

📊 The importance of IO-related issues in running big jobs in HPC systems.

🔁 The strategy of launching and monitoring job runs multiple times to identify weak nodes and ensure reliability.

💾 The use of bgfs as a file system and its positive performance compared to luster.