Optimizing the Performance of the Sparse Matrix-Vector Multiplication Kernel in FPGA Guided by the Roofline Model.

Area of Science:

• High-Performance Computing
• Computer Architecture
• Numerical Analysis

Background:

• Field-Programmable Gate Arrays (FPGAs) are increasingly used in high-performance computing as energy-efficient alternatives to many-core processors.
• Efficient implementation of numerical linear algebra (NLA) kernels on FPGAs is crucial for performance and energy consumption.
• Analytical tools are needed to understand and optimize NLA kernel performance on FPGAs.

Purpose of the Study:

• To propose an extended Roofline Model (RLM) for optimizing NLA kernels on FPGAs.
• To enhance runtime and energy efficiency of NLA kernels using sparse blocked storage formats.
• To guide the development of efficient FPGA implementations for computational tasks.

Main Methods:

• Extension of the Roofline Model (RLM) to incorporate sparse blocked storage formats.
• Implementation of optimized sparse matrix-vector multiplication (SpMV) kernels on FPGAs.
• Leveraging block-sparse storage formats for improved data access and computational efficiency.

Main Results:

• The extended RLM provides a framework for analyzing and optimizing NLA kernels on FPGAs.
• The proposed approach demonstrates improvements in runtime and energy consumption for SpMV kernels.
• Block-sparse storage formats facilitate more efficient data access, leading to better performance.

Conclusions:

• The extended RLM is a valuable tool for optimizing NLA kernels on FPGAs, balancing speed and energy efficiency.
• FPGA implementations of NLA kernels can achieve significant performance gains through optimized storage formats and analytical modeling.
• This work contributes to the advancement of energy-efficient high-performance computing on FPGAs.