Accelerating Data Processing Through Hardware

Accelerating Data Processing Through Hardware Information Guide

1. Introduction on Accelerating Data Processing Through Hardware
2. Main Features
3. History
4. Detailed Analysis
5. Conclusion

Introduction on Accelerating Data Processing Through Hardware

Over the past year, NVIDIA and IBM engineers have been working towards bringing GPU A Keynote by Philippe Cudre-Mauroux (University of Fribourg) This talk discusses optimizing workloads with heterogeneous ... DataPelago Nucleus is the core of DataPelago's universal Presented by Scott Shadley (Solidigm) Computational storage is a new paradigm in computing where The Systems Group at ETH Channel presents the Systems Group research General Info We want to meetup IRL, too! Checkout the website for information on social events this week. Check back weekly, as ...

Main Features

History

Hybrid CPU GPU Acceleration John Janakiraman, Datapelago

Spark vs. Accelerated Compute: A Balanced Look at Data Processing Strategies - Giri Tatavarty

Enabling hardware acceleration in OVS-DPDK using DPDK Framework - Sugesh Chandran

Hardware Accelerated Database Lectures #6 - Swarm64 (Karsten Rönner)

Protecting data at rest with Computational Storage

VLDB '20: Lowering the Latency of Data Processing Pipelines Through FPGA based Hardware Acceleration

Accelerating computation with FPGAs with a seismic data processing example

SIMD-Accelerated Data Processing

Accelerate Data Processing for Visualization by 20x with RAPIDS cuDF pandas Accelerator Mode

Detailed Analysis

Data is compiled from public records and verified media reports.

Last Updated: June 25, 2026

Conclusion

Disclaimer: Disclaimer: Details estimates are based on publicly available data, media reports, and financial analysis. Actual numbers may vary.