A Foray into Efficient Mapping of Algorithms to Hardware Platforms on Heterogeneous Systems

TL;DR

This paper compares the performance and efficiency of seven key algorithms across CPU, GPU, and FPGA architectures, revealing FPGA's potential as an alternative for certain computational tasks.

Contribution

It provides a comparative analysis of algorithm performance and power efficiency on heterogeneous hardware, highlighting FPGA's viability for specific applications.

Findings

FPGAs can outperform GPUs and CPUs in N-body simulations.

Performance per watt varies significantly across architectures.

Certain algorithms are better suited to specific hardware types.

Abstract

Heterogeneous computing can potentially offer significant performance and performance per watt improvements over homogeneous computing, but the question "what is the ideal mapping of algorithms to architectures?" remains an open one. In the past couple of years new types of computing devices such as FPGAs have come into general computing use. In this work we attempt to add to the body of scientific knowledge by comparing Kernel performance and performance per watt of seven key algorithms according to Berkley's dwarf taxonomy. We do so using the Rodinia benchmark suite on three different high-end hardware architecture representatives from the CPU, GPU and FPGA families. We find results that support some distinct mappings between the architecture and performance per watt. Perhaps the most interesting finding is that, for our specific hardware representatives, FPGAs should be considered as alternatives to GPUs and CPUs in several key algorithms: N-body simulations, dense linear algebra and structured grid.