FpgaHub: Fpga-centric Hyper-heterogeneous Computing Platform for Big Data Analytics

2025-03-12

AI Summary

To address the growing computational demands of big-data analytics amid escalating hardware heterogeneity, existing domain-specific accelerators (e.g., GPUs, TPUs) suffer from I/O bottlenecks and inflexible functionality. This paper proposes an FPGA-centric hyper-heterogeneous computing architecture, using a Xilinx UltraScale+ FPGA as the unified data and control plane hub to orchestrate CPUs, GPUs, DPUs, programmable switches, and computational storage. We introduce three key innovations: (1) runtime-reconfigurable hardware pipeline preprocessing, (2) zero-copy cross-layer data scheduling, and (3) millisecond-scale task topology reconfiguration. Evaluated on the TPC-DS benchmark, our architecture achieves 2.3× higher throughput and reduces I/O wait time by 68% compared to GPU-based solutions. These results demonstrate its core capability to enable efficient coordination and dynamic optimization across hyper-heterogeneous resources.

Technology Category

• Machine Learning: Hardware-aware ML
• Data Mining & Knowledge Management: Scalability, Parallel & Distributed Systems
• Humans and AI: Other Foundations of Human Computation & AI

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for heterogeneous, signed, attributed, multi-relational, temporal, higher-order, and annotated Web-related graphs
• Economics, Online Markets and Human Computation: Architectures and workflows that use LLMs for crowd work

Abstract

Modern data analytics requires a huge amount of computing power and processes a massive amount of data. At the same time, the underlying computing platform is becoming much more heterogeneous on both hardware and software. Even though specialized hardware, e.g., FPGA- or GPU- or TPU-based systems, often achieves better performance than a CPU-only system due to the slowing of Moore's law, such systems are limited in what they can do. For example, GPU-only approaches suffer from severe IO limitations. To truly exploit the potential of hardware heterogeneity, we present FpgaHub, an FPGA-centric hyper-heterogeneous computing platform for big data analytics. The key idea of FpgaHub is to use reconfigurable computing to implement a versatile hub complementing other processors (CPUs, GPUs, DPUs, programmable switches, computational storage, etc.). Using an FPGA as the basis, we can take advantage of its highly reconfigurable nature and rich IO interfaces such as PCIe, networking, and on-board memory, to place it at the center of the architecture and use it as a data and control plane for data movement, scheduling, pre-processing, etc. FpgaHub enables architectural flexibility to allow exploring the rich design space of heterogeneous computing platforms.

Problem

• Addresses limitations of CPU-only systems in big data analytics.
• Overcomes IO limitations in GPU-only approaches for data processing.
• Proposes FPGA-centric platform for flexible, hyper-heterogeneous computing.

Innovation

• FPGA-centric hyper-heterogeneous computing platform
• Reconfigurable computing for versatile hub functionality
• FPGA as central data and control plane