The Case for Polymorphic Registers in Dataflow Computing

Catalin Bogdan Ciobanu, Georgi Gaydadjiev, Christian Pilato, Donatella Sciuto

Research output: Contribution to journalArticleScientificpeer-review

2 Citations (Scopus)
105 Downloads (Pure)

Abstract

Heterogeneous systems are becoming increasingly popular, delivering high performance through hardware specialization. However, sequential data accesses may have a negative impact on performance. Data parallel solutions such as Polymorphic Register Files (PRFs) can potentially accelerate applications by facilitating high-speed, parallel access to performance-critical data. This article shows how PRFs can be integrated into dataflow computational platforms. Our semi-automatic, compiler-based methodology generates customized PRFs and modifies the computational kernels to efficiently exploit them. We use a separable 2D convolution case study to evaluate the impact of memory latency and bandwidth on performance compared to a state-of-the-art NVIDIA Tesla C2050 GPU. We improve the throughput up to 56.17X and show that the PRF-augmented system outperforms the GPU for 9×9 or larger mask sizes, even in bandwidth-constrained systems.
Original languageEnglish
Pages (from-to)1185-1219
Number of pages35
JournalInternational Journal of Parallel Programming
Volume46
Issue number6
DOIs
Publication statusPublished - Dec 2018

Keywords

  • Dataflow computing
  • Parallel memory accesses
  • Polymorphic register file
  • Bandwidth
  • Vector lanes
  • Convolution
  • High performance computing
  • High-level synthesis

Fingerprint

Dive into the research topics of 'The Case for Polymorphic Registers in Dataflow Computing'. Together they form a unique fingerprint.

Cite this