Graphical Processing Unit (GPU) was originally invented for the purpose of gaming and other graphical intensive environments. GPUs perform vector calculations efficiently and support a high degree of parallelism. Given that, GPUs are used today to support workloads such as genomic sequencing in life-science, fraud detection, and backtesting in financial services and AI/ML workloads such as self-driving cars.
Nvidia, Intel, and AMD are some of the common GPU vendors.GPUs differ in the amount and type of cores.
How to accelerate GPU performance?
GPUs require high volumes of data in high-performance fashion. To make sure storage is not the bottleneck, engineers traditionally have placed the data on the GPUs servers local storage,
which is expensive.
Next-generation solutions such as WekaFS can saturate GPUs over a shared file system
offering the performance of local MVNEs over networked lower-cost storage.