Exploring the GPU Architecture

A Graphics Processor Unit (GPU) is mostly known for the hardware device used when running applications that weigh heavy on graphics, i.e. 3D modeling software or VDI infrastructures. In the consumer market, a GPU is mostly used to accelerate gaming graphics. Today, GPGPU’s (General Purpose GPU) are the choice of hardware to accelerate computational workloads in modern High Performance Computing (HPC) landscapes.

HPC in itself is the platform serving workloads like Machine Learning (ML), Deep Learning (DL), and Artificial Intelligence (AI). Using a GPGPU is not only about ML computations that require image recognition anymore. Calculations on tabular data is also a common exercise in i.e. healthcare, insurance and financial industry verticals. But why do we need a GPU for these types of all these workloads? This blogpost will go into the GPU architecture and why they are a good fit for HPC workloads running on vSphere ESXi.

Latency vs Throughput


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