GPU-based Data Analytics - Performance Analysis

This rise of hardware accelerators to meet the demand of AI workloads has also led to a variety of novel methods to leverage GPUs for traditional data analytics workloads. A key concern for any data-intensive system using GPUs is the efficiency of moving the data to the accelerator. In this project, we will investigate the performance of GPU databases and how they are impacted by the different kinds of data movement (CPU memory to GPU memory, SSD to GPU, etc.).

The first step would be to understand the landscape of available GPU database systems and the different trade-offs they offer. Then, we will pick one system (or more depending on the project size) for performance analysis.

If you are interested in data management systems, GPUs, storage devices, benchmarking, and performance analysis, this project would be a great fit for you. This project would be suitable as a standalone project, a research project with an MSc thesis followup, or BSc thesis. We can adjust the scope of the project depending on the project and group size.

Related work / links:

[1] Sirius

[2] Rethinking Analytical Processing in the GPU Era

[3] Designing GPU-Accelerated Query Engines with NVIDIA GQE

[4] Path to GPU-Initiated I/O for Data-Intensive Systems