Energy Measurement Laboratory for Heterogeneous Hardware Evaluation

Diaz, Leandro; Moreira, Rodrigo Martins; Favaro, Federico; Dufrechou, Ernesto; Oliver, Juan Pablo

doi:10.1109/urucon53396.2021.9647059

Cited by 1 publication

(1 citation statement)

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“…In particular, several recent works on FPGAs focus on the implementation of numerical linear algebra (NLA) kernels [4][5][6], which are a crucial component of a myriad of applications. However, most works focus on dense NLA [7][8][9], as well as other algorithms more suited for FPGAs such as image processing [10,11], machine learning [12], etc. Sparse linear algebra, on the other hand, poses several challenges due to its irregular access patterns and low computational intensity.…”

Section: Introductionmentioning

confidence: 99%

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

Favaro,

Dufrechou,

Oliver

et al. 2023

Micromachines

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The widespread adoption of massively parallel processors over the past decade has fundamentally transformed the landscape of high-performance computing hardware. This revolution has recently driven the advancement of FPGAs, which are emerging as an attractive alternative to power-hungry many-core devices in a world increasingly concerned with energy consumption. Consequently, numerous recent studies have focused on implementing efficient dense and sparse numerical linear algebra (NLA) kernels on FPGAs. To maximize the efficiency of these kernels, a key aspect is the exploration of analytical tools to comprehend the performance of the developments and guide the optimization process. In this regard, the roofline model (RLM) is a well-known graphical tool that facilitates the analysis of computational performance and identifies the primary bottlenecks of a specific software when executed on a particular hardware platform. Our previous efforts advanced in developing efficient implementations of the sparse matrix–vector multiplication (SpMV) for FPGAs, considering both speed and energy consumption. In this work, we propose an extension of the RLM that enables optimizing runtime and energy consumption for NLA kernels based on sparse blocked storage formats on FPGAs. To test the power of this tool, we use it to extend our previous SpMV kernels by leveraging a block-sparse storage format that enables more efficient data access.

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