High Performance Computing (HPC) comes into play when individual machines fall short of solving a large problem. You can play games on a laptop, attend webinars from your mobile, and process spreadsheets on your desktops. But it is practically impossible to run a responsive gaming server on your laptop which is used by thousands of gamers worldwide, or to run the WhatsApp service with your mobile, or to host Microsoft or Google services on your desktop.
As users, you do not have to worry about these backend tasks. But if you are on the service-provider’s side, bandwidth, throughput, latency, SLA, and performance become the key terms that guide your day-to-day job. If you run a company with thousands of customers, it is likely that you need to work with “high performance”.
HPC is tightly linked with parallel computing. A typical computer program runs sequentially -- that is, it performs one task at a time. In contrast, in parallel computing, a program often performs multiple tasks at the same time, resulting in higher throughput (number of tasks completed in unit time). What is the scale of parallelism we are talking about here? A typical server may have a few tens of cores. Thus, this server can potentially execute a few tens of instructions simultaneously. If your server has an accelerator (graphics processing unit or GPU), the accelerator typically has thousands of cores.
Can I offer Google-like services if I have such a server with the associated accelerator? No. You need thousands of such servers to be able to address the scale. In HPC, at least a few hundreds of such servers work together to solve a given problem. If they have to work together, they need to share data. This is done by a fast interconnect between these servers -- whose top speed can be 100 times that of your best download speed on your desktop.
This story is from the October 2021 edition of PCQuest.
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This story is from the October 2021 edition of PCQuest.
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