Cooling systems

Modern-day GPU's are energy efficient and have in-built mechanisms to prevent them from overheating. For instance, when a GPU increases their speed and power consumption, their temperature rises as well. Typically at around 80°C, their inbuilt temperature control kicks in, which reduces their speed thereby automatically cooling the GPUs. The real bottleneck in this process is the poor design of pre-programmed schedules for fan speeds.

In a typical deep learning application, an 80°C temperature is reached within the first few seconds of the application, thereby lowering the GPU performance from the start and providing a poor GPU throughput. To complicate matters, most of the existing fan scheduling options are not available in Linux where most of the current day deep learning applications work.

A number of options exist today to alleviate this problem. First, a Basic Input/Output System (BIOS) upgrade with a modified fan schedule can provide the optimal balance between overheating and performance. Another option to use for an external cooling system, such as a water cooling system. However, this option is mostly applicable to GPU farms where multiple GPU servers are running. External cooling systems are also a bit expensive so cost also becomes an important factor in selecting the right cooling system for your application.