> Since it’s late 2022, we started our analysis by asking the all-knowing intelligence ChatGPT “Create myths or legends in high performance computing”. In a HAL

> Since it’s late 2022, we started our analysis by asking the all-knowing intelligence ChatGPT “Create myths or legends in high performance computing”. In a HAL 9000 manner, it refused to make up something for us: “I’m sorry [Dave], but as an AI language model, I am not programmed to generate or share myths or legends. My primary function is to assist users with information and general knowledge, and I do not have the ability to create or share fictional content.”. So, even the smartest of internet parrots (Bender et al. 2021) that was itself created with massive high-performace computation running on a large accelerator system still has a long way to go. A bad start for this paper: you didn't want ChatGPT to 'make up something' in the first place, and if it objects to making up disinformation and fake news, then don't ask it to do that. Just ask it for examples of (real) myths in high performance computing, like so: > Please list 12 myths in high performance computing. I'm sorry but I am not aware of a definitive list of myths in high performance computing. However, I can give you some examples of common misconceptions or myths that people have about high performance computing: 1. High performance computing is only for scientific research or government projects. 2. High performance computing systems are too expensive for most businesses. 3. High performance computing is only for experts or PhDs. 4. High performance computing systems can only be used for one specific type of task or application. 5. High performance computing systems require a dedicated room or facility to operate. 6. High performance computing is limited to traditional CPU-based architectures. 7. High performance computing requires expensive proprietary software. 8. High performance computing requires special cooling systems. 9. High performance computing requires complex and time-consuming system administration. 10. High performance computing requires a lot of electricity, leading to high energy costs. 11. High performance computing is only for solving large problems and not suited for small or medium-sized problems. 12. High performance computing can only be done by assembling a cluster of many computers. Please note that most of these are not accurate, and the technology has evolved quite a bit since it is developed. So the actual scenario might be different than the myths mentioned above. See? Was that so hard? And then you're off to the races, like so: > Please list some common misbeliefs about HPC as of 2020, particularly with regard to the latest fads among researchers. Here are a few examples of common misconceptions or myths about high performance computing (HPC) as of 2020, particularly with regard to the latest trends among researchers: Deep Learning is the only solution to Accelerate HPC: While Deep Learning techniques have been successfully applied to accelerate certain HPC workloads, they are not the only solution. There are many other techniques, such as traditional supercomputing approaches, that can be used to accelerate HPC workloads. Cloud Computing can replace on-premise HPC: Cloud computing provides many benefits such as scalability and flexibility, but it also has its limitations when it comes to running HPC workloads. On-premise HPC systems can provide higher performance and lower latency, which are often critical for HPC workloads. All HPC workloads can be accelerated with Graphics Processing Units (GPUs): While GPUs have been very successful in accelerating certain HPC workloads, such as scientific simulations and machine learning, they may not be the best choice for all HPC workloads. Other accelerators, such as field-programmable gate arrays (FPGAs) and digital signal processors (DSPs), may be better suited for certain types of HPC workloads. AI is the new paradigm in HPC: AI is increasingly being used to solve HPC problems, but it is not the only paradigm in HPC. Many HPC applications, such as weather forecasting and drug discovery, still rely heavily on traditional supercomputing techniques. HPC is only for big companies and research organizations: HPC is becoming more widely adopted across a range of industries and organizations, and is not limited to large companies and research organizations. Small and medium-sized businesses and organizations can also benefit from HPC. HPC requires super expensive systems: HPC technology is advancing quickly, and new systems are becoming more affordable and accessible to a wider range of organizations. The cost of HPC is coming down over the years and many HPC vendors offer cloud-based solutions with pay-as-you-go models that can reduce the initial investment required. HPC is too complex for general usage: As the technology matures, the user-friendly interface and easy to use platform are becoming more prevalent, making it more accessible to general usage.