R-GPU
| Autor: | Henk Corporaal, Gert-Jan van den Braak |
|---|---|
| Přispěvatelé: | Electronic Systems |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
010302 applied physics
Design Computer science Pipeline (computing) Performance GPGPU Graphics processing unit 02 engineering and technology Parallel computing Energy consumption 01 natural sciences Graphics pipeline 020202 computer hardware & architecture Hardware and Architecture Reconfigurable architecture 0103 physical sciences 0202 electrical engineering electronic engineering information engineering SDG 7 - Affordable and Clean Energy General-purpose computing on graphics processing units Performance improvement Massively parallel SDG 7 – Betaalbare en schone energie Software Information Systems Efficient energy use |
| Zdroj: | ACM Transactions on Architecture and Code Optimization, 13(1):12. Association for Computing Machinery, Inc |
| ISSN: | 1544-3566 |
| Popis: | Over the last decade, Graphics Processing Unit (GPU) architectures have evolved from a fixed-function graphics pipeline to a programmable, energy-efficient compute accelerator for massively parallel applications. The compute power arises from the GPU’s Single Instruction/Multiple Threads architecture: concurrently running many threads and executing them as Single Instruction/Multiple Data--style vectors. However, compute power is still lost due to cycles spent on data movement and control instructions instead of data computations. Even more cycles are lost on pipeline stalls resulting from long latency (memory) operations. To improve not only performance but also energy efficiency, we introduce R-GPU: a reconfigurable GPU architecture with communicating cores. R-GPU is an addition to a GPU, which can still be used as such, but also has the ability to reorganize the cores of a GPU in a reconfigurable network. In R-GPU data movement and control is implicit in the configuration of the network. Each core executes a fixed instruction, reducing instruction decode count and increasing energy efficiency. On a number of benchmarks we show an average performance improvement of 2.1 × over the same GPU without modifications. We further make a conservative power estimation of R-GPU which shows that power consumption can be reduced by 6%, leading to an energy consumption reduction of 55%, while area only increases by a mere 4%. |
| Databáze: | OpenAIRE |
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