The Case for Polymorphic Registers in Dataflow Computing

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

*Bijbehorende auteur voor dit werk

Onderzoeksoutput: ArticleAcademicpeer review

2 Citaten (Scopus)
111 Downloads (Pure)


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 C2050GPU. We improve the throughput upto 56.17X and show that the PRF-augmented system outperforms the GPU for for 9 x 9 or larger mask sizes, even in bandwidth-constrained systems.

Originele taal-2English
Pagina's (van-tot)1185-1219
Aantal pagina's35
TijdschriftInternational journal of parallel programming
Nummer van het tijdschrift6
StatusPublished - dec.-2018
Extern gepubliceerdJa

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