Towards the Inclusion of FPGAs on Commodity Heterogeneous Systems

Guzmán, Maria Angélica Dávila; Tejero, Rubén Gran; Gaudó, María Villarroya; Gracia, Darío Suárez

doi:10.1109/hpcs.2018.00092

Cited by 2 publications

(1 citation statement)

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“…Similar efforts have been performed to integrate a variety of devices to compute a single data-parallel kernel, but the optimizations are focused at the device level by using more complex and problem-specific architectures [8], [2].…”

Section: Related Workmentioning

confidence: 99%

Towards Co-execution on Commodity Heterogeneous Systems: Optimizations for Time-Constrained Scenarios

Nozal,

Bosque,

Beivide

2020

Preprint

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Heterogeneous systems are present from powerful supercomputers, to mobile devices, including desktop computers, thanks to their excellent performance and energy consumption. The ubiquity of these architectures in both desktop systems and mediumsized service servers allow enough variability to exploit a wide range of problems, such as multimedia workloads, video encoding, image filtering and inference in machine learning. Due to the heterogeneity, some efforts have been done to reduce the programming effort and preserve performance portability, but these systems include a set of challenges. The context in which applications offload the workload along with the management overheads introduced when doing co-execution, penalize the performance gains under time-constrained scenarios. Therefore, this paper proposes optimizations for the EngineCL runtime to reduce the penalization when co-executing in commodity systems, as well as algorithmic improvements when load balancing. An exhaustive experimental evaluation is performed, showing optimization improvements of 7.5% and 17.4% for binary and ROI-based offloading modes, respectively. Thanks to all the optimizations, the new load balancing algorithm is always the most efficient scheduling configuration, achieving an average efficiency of 0.84 under a pessimistic scenario.

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Section: Related Workmentioning

confidence: 99%