HotTiles: Accelerating SpMM with Heterogeneous Accelerator Architectures

Gerasimos Gerogiannis, Sriram Aananthakrishnan, Josep Torrellas, Ibrahim Hur

Research output: Chapter in Book/Report/Conference proceedingConference contribution


Sparse Matrix Dense Matrix Multiplication (SpMM) is an important kernel with application across a wide range of domains, including machine learning and linear algebra solvers. In many sparse matrices, the pattern of nonzeros is nonuniform: nonzeros form dense and sparse regions, rather than being uniformly distributed across the whole matrix. We refer to this property as Intra-Matrix Heterogeneity (IMH). Currently, SpMM accelerator designs do not leverage this heterogeneity. They employ the same processing elements (PEs) for all the regions of a sparse matrix, resulting in suboptimal acceleration. To address this limitation, we utilize heterogeneous SpMM accelerator architectures, which include different types of PEs to exploit IMH. We develop an analytical modeling framework to predict the performance of different types of accelerator PEs taking into account IMH. Furthermore, we present a heuristic for partitioning sparse matrices among heterogeneous PEs. We call our matrix modeling and partitioning method HotTiles. To evaluate HotTiles, we simulate three different heterogeneous architectures. Each one consists of two types of workers (i.e., PEs): one suited for compute-bound denser regions (Hot Worker) and one for memory-bound sparser regions (Cold Worker). Our results show that exploiting IMH with HotTiles is very effective. Depending on the architecture, heterogeneous execution with HotTiles outperforms homogeneous execution using only hot or only cold workers by 9.2-16.8x and 1.4-3.7x, respectively. In addition, HotTiles outperforms the best worker type used on a per-matrix basis by 1.3-2.5 x. Finally, HotTiles outperforms an IMH-unaware heterogeneous execution strategy by 1.4-2.2x.

Original languageEnglish (US)
Title of host publicationProceedings - 2024 IEEE International Symposium on High-Performance Computer Architecture, HPCA 2024
PublisherIEEE Computer Society
Number of pages17
ISBN (Electronic)9798350393132
StatePublished - 2024
Event30th IEEE International Symposium on High-Performance Computer Architecture, HPCA 2024 - Edinburgh, United Kingdom
Duration: Mar 2 2024Mar 6 2024

Publication series

NameProceedings - International Symposium on High-Performance Computer Architecture
ISSN (Print)1530-0897


Conference30th IEEE International Symposium on High-Performance Computer Architecture, HPCA 2024
Country/TerritoryUnited Kingdom


  • hardware accelerators
  • heterogeneous computing
  • sparse computations
  • SpMM

ASJC Scopus subject areas

  • Hardware and Architecture


Dive into the research topics of 'HotTiles: Accelerating SpMM with Heterogeneous Accelerator Architectures'. Together they form a unique fingerprint.

Cite this